As the hospitalist’s role in medicine grows, the transition of care from inpatient to primary care providers (PCPs, including primary care physicians, nurse practitioners, or physician assistants), becomes increasingly important. Inadequate communication at this transition is associated with preventable adverse events leading to rehospitalization, disability, and death.^1-3 While professional societies recommend PCPs be notified at every care transition, the specific timing and modality of this communication is not well defined.⁴

Providing PCPs access to the inpatient electronic health record (EHR) may reduce the need for active communication. However, a recent survey of PCPs in the general internal medicine division of an academic hospital found a strong preference for additional communication with inpatient providers, despite a shared EHR.⁵

We examined communication preferences of general internal medicine PCPs at a different academic institution and extended our study to include community-based PCPs who were both affiliated and unaffiliated with the institution.

METHODS

Between October 2015 and June 2016, we surveyed PCPs from 3 practice groups with institutional affiliation or proximity to The Johns Hopkins Hospital: all general internal medicine faculty with outpatient practices (“academic,” 2 practice sites, n = 35), all community-based PCPs affiliated with the health system (“community,” 36 practice sites, n = 220), and all PCPs from an unaffiliated managed care organization (“unaffiliated,” 5 practice sites ranging from 0.3 to 4 miles from The Johns Hopkins Hospital, n = 29).

All groups have work-sponsored e-mail services. At the time of the survey, both the academic and community groups used an EHR that allowed access to inpatient laboratory and radiology data and discharge summaries. The unaffiliated group used paper health records. The hospital faxes discharge summaries to all PCPs who are identified by patients.

The investigators and representatives from each practice group collaborated to develop 15 questions with mutually exclusive answers to evaluate PCP experiences with and preferences for communication with inpatient teams. The survey was constructed and administered through Qualtrics’ online platform (Qualtrics, Provo, UT) and distributed via e-mail. The study was reviewed and acknowledged by the Johns Hopkins institutional review board as quality improvement activity.

The survey contained branching logic. Only respondents who indicated preference for communication received questions regarding preferred mode of communication. We used the preferred mode of communication for initial contact from the inpatient team in our analysis. χ² and Fischer’s exact tests were performed with JMP 12 software (SAS Institute Inc, Cary, NC).

RESULTS

Fourteen (40%) academic, 43 (14%) community, and 16 (55%) unaffiliated PCPs completed the survey, for 73 total responses from 284 surveys distributed (26%).

Among the 73 responding PCPs, 31 (42%) reported receiving notification of admission during “every” or “almost every” hospitalization, with no significant variation across practice groups (P = 0.5).

Across all groups, 64 PCPs (88%) preferred communication at 1 or more points during hospitalizations (panel A of Figure). “Both upon admission and prior to discharge” was selected most frequently, and there were no differences between practice groups (P = 0.2).

DISCUSSION

Our findings add to previous evidence of low rates of communication between inpatient providers and PCPs⁶ and a preference from PCPs for communication during hospitalizations despite shared EHRs.⁵ We extend previous work by demonstrating that PCP preferences for mode of communication vary by practice setting. Our findings lead us to hypothesize that identifying and incorporating PCP preferences may improve communication, though at the potential expense of standardization and efficiency.

There may be several reasons for the differing communication preferences observed. Most academic PCPs are located near or have admitting privileges to the hospital and are not in clinic full time. Their preference for the telephone may thus result from interpersonal relationships born from proximity and greater availability for telephone calls, or reduced fluency with the EHR compared to full-time community clinicians.

The unaffiliated group’s preference for fax may reflect a desire for communication that integrates easily with paper charts and is least disruptive to workflow, or concerns about health information confidentiality in e-mails.

Our study’s generalizability is limited by a low response rate, though it is comparable to prior studies.⁷ The unaffiliated group was accessed by convenience (acquaintance with the medical director); however, we note it had the highest response rate (55%).

In summary, we found low rates of communication between inpatient providers and PCPs, despite a strong preference from most PCPs for such communication during hospitalizations. PCPs’ preferred mode of communication differed based on practice setting. Addressing PCP communication preferences may be important to future care transition interventions.

Disclosure

The authors report no conflicts of interest.

As the hospitalist’s role in medicine grows, the transition of care from inpatient to primary care providers (PCPs, including primary care physicians, nurse practitioners, or physician assistants), becomes increasingly important. Inadequate communication at this transition is associated with preventable adverse events leading to rehospitalization, disability, and death.^1-3 While professional societies recommend PCPs be notified at every care transition, the specific timing and modality of this communication is not well defined.⁴

Providing PCPs access to the inpatient electronic health record (EHR) may reduce the need for active communication. However, a recent survey of PCPs in the general internal medicine division of an academic hospital found a strong preference for additional communication with inpatient providers, despite a shared EHR.⁵

We examined communication preferences of general internal medicine PCPs at a different academic institution and extended our study to include community-based PCPs who were both affiliated and unaffiliated with the institution.

METHODS

Between October 2015 and June 2016, we surveyed PCPs from 3 practice groups with institutional affiliation or proximity to The Johns Hopkins Hospital: all general internal medicine faculty with outpatient practices (“academic,” 2 practice sites, n = 35), all community-based PCPs affiliated with the health system (“community,” 36 practice sites, n = 220), and all PCPs from an unaffiliated managed care organization (“unaffiliated,” 5 practice sites ranging from 0.3 to 4 miles from The Johns Hopkins Hospital, n = 29).

All groups have work-sponsored e-mail services. At the time of the survey, both the academic and community groups used an EHR that allowed access to inpatient laboratory and radiology data and discharge summaries. The unaffiliated group used paper health records. The hospital faxes discharge summaries to all PCPs who are identified by patients.

The investigators and representatives from each practice group collaborated to develop 15 questions with mutually exclusive answers to evaluate PCP experiences with and preferences for communication with inpatient teams. The survey was constructed and administered through Qualtrics’ online platform (Qualtrics, Provo, UT) and distributed via e-mail. The study was reviewed and acknowledged by the Johns Hopkins institutional review board as quality improvement activity.

The survey contained branching logic. Only respondents who indicated preference for communication received questions regarding preferred mode of communication. We used the preferred mode of communication for initial contact from the inpatient team in our analysis. χ² and Fischer’s exact tests were performed with JMP 12 software (SAS Institute Inc, Cary, NC).

RESULTS

Fourteen (40%) academic, 43 (14%) community, and 16 (55%) unaffiliated PCPs completed the survey, for 73 total responses from 284 surveys distributed (26%).

Among the 73 responding PCPs, 31 (42%) reported receiving notification of admission during “every” or “almost every” hospitalization, with no significant variation across practice groups (P = 0.5).

Across all groups, 64 PCPs (88%) preferred communication at 1 or more points during hospitalizations (panel A of Figure). “Both upon admission and prior to discharge” was selected most frequently, and there were no differences between practice groups (P = 0.2).

DISCUSSION

Our findings add to previous evidence of low rates of communication between inpatient providers and PCPs⁶ and a preference from PCPs for communication during hospitalizations despite shared EHRs.⁵ We extend previous work by demonstrating that PCP preferences for mode of communication vary by practice setting. Our findings lead us to hypothesize that identifying and incorporating PCP preferences may improve communication, though at the potential expense of standardization and efficiency.

There may be several reasons for the differing communication preferences observed. Most academic PCPs are located near or have admitting privileges to the hospital and are not in clinic full time. Their preference for the telephone may thus result from interpersonal relationships born from proximity and greater availability for telephone calls, or reduced fluency with the EHR compared to full-time community clinicians.

The unaffiliated group’s preference for fax may reflect a desire for communication that integrates easily with paper charts and is least disruptive to workflow, or concerns about health information confidentiality in e-mails.

Our study’s generalizability is limited by a low response rate, though it is comparable to prior studies.⁷ The unaffiliated group was accessed by convenience (acquaintance with the medical director); however, we note it had the highest response rate (55%).

In summary, we found low rates of communication between inpatient providers and PCPs, despite a strong preference from most PCPs for such communication during hospitalizations. PCPs’ preferred mode of communication differed based on practice setting. Addressing PCP communication preferences may be important to future care transition interventions.

Disclosure

The authors report no conflicts of interest.

As the hospitalist’s role in medicine grows, the transition of care from inpatient to primary care providers (PCPs, including primary care physicians, nurse practitioners, or physician assistants), becomes increasingly important. Inadequate communication at this transition is associated with preventable adverse events leading to rehospitalization, disability, and death.^1-3 While professional societies recommend PCPs be notified at every care transition, the specific timing and modality of this communication is not well defined.⁴

Providing PCPs access to the inpatient electronic health record (EHR) may reduce the need for active communication. However, a recent survey of PCPs in the general internal medicine division of an academic hospital found a strong preference for additional communication with inpatient providers, despite a shared EHR.⁵

We examined communication preferences of general internal medicine PCPs at a different academic institution and extended our study to include community-based PCPs who were both affiliated and unaffiliated with the institution.

METHODS

Between October 2015 and June 2016, we surveyed PCPs from 3 practice groups with institutional affiliation or proximity to The Johns Hopkins Hospital: all general internal medicine faculty with outpatient practices (“academic,” 2 practice sites, n = 35), all community-based PCPs affiliated with the health system (“community,” 36 practice sites, n = 220), and all PCPs from an unaffiliated managed care organization (“unaffiliated,” 5 practice sites ranging from 0.3 to 4 miles from The Johns Hopkins Hospital, n = 29).

All groups have work-sponsored e-mail services. At the time of the survey, both the academic and community groups used an EHR that allowed access to inpatient laboratory and radiology data and discharge summaries. The unaffiliated group used paper health records. The hospital faxes discharge summaries to all PCPs who are identified by patients.

The investigators and representatives from each practice group collaborated to develop 15 questions with mutually exclusive answers to evaluate PCP experiences with and preferences for communication with inpatient teams. The survey was constructed and administered through Qualtrics’ online platform (Qualtrics, Provo, UT) and distributed via e-mail. The study was reviewed and acknowledged by the Johns Hopkins institutional review board as quality improvement activity.

The survey contained branching logic. Only respondents who indicated preference for communication received questions regarding preferred mode of communication. We used the preferred mode of communication for initial contact from the inpatient team in our analysis. χ² and Fischer’s exact tests were performed with JMP 12 software (SAS Institute Inc, Cary, NC).

RESULTS

Fourteen (40%) academic, 43 (14%) community, and 16 (55%) unaffiliated PCPs completed the survey, for 73 total responses from 284 surveys distributed (26%).

Among the 73 responding PCPs, 31 (42%) reported receiving notification of admission during “every” or “almost every” hospitalization, with no significant variation across practice groups (P = 0.5).

Across all groups, 64 PCPs (88%) preferred communication at 1 or more points during hospitalizations (panel A of Figure). “Both upon admission and prior to discharge” was selected most frequently, and there were no differences between practice groups (P = 0.2).

DISCUSSION

Our findings add to previous evidence of low rates of communication between inpatient providers and PCPs⁶ and a preference from PCPs for communication during hospitalizations despite shared EHRs.⁵ We extend previous work by demonstrating that PCP preferences for mode of communication vary by practice setting. Our findings lead us to hypothesize that identifying and incorporating PCP preferences may improve communication, though at the potential expense of standardization and efficiency.

There may be several reasons for the differing communication preferences observed. Most academic PCPs are located near or have admitting privileges to the hospital and are not in clinic full time. Their preference for the telephone may thus result from interpersonal relationships born from proximity and greater availability for telephone calls, or reduced fluency with the EHR compared to full-time community clinicians.

The unaffiliated group’s preference for fax may reflect a desire for communication that integrates easily with paper charts and is least disruptive to workflow, or concerns about health information confidentiality in e-mails.

Our study’s generalizability is limited by a low response rate, though it is comparable to prior studies.⁷ The unaffiliated group was accessed by convenience (acquaintance with the medical director); however, we note it had the highest response rate (55%).

In summary, we found low rates of communication between inpatient providers and PCPs, despite a strong preference from most PCPs for such communication during hospitalizations. PCPs’ preferred mode of communication differed based on practice setting. Addressing PCP communication preferences may be important to future care transition interventions.

Disclosure

The authors report no conflicts of interest.

The 12-lead electrocardiogram (ECG) remains one of the most widely used and readily available diagnostic tests in modern medicine.¹ Reflecting the electrical behavior of the heart, this point-of-care diagnostic test is used in almost every area of medicine for diagnosis, prognostication, and selection of appropriate treatment. The ECG is sometimes the only and most efficient way of detecting life-threatening conditions, thus allowing a timely delivery of emergency care.² However, the practical power of the 12-lead ECG relies on the ability of the clinician to interpret this test correctly.

For decades, ECG interpretation has been a core component of undergraduate and postgraduate medical training.^3-5 Unfortunately, numerous studies have demonstrated alarming rates of inaccuracy and variability in interpreting ECGs among trainees at all levels of education.^4,6,7 Senior medical students have been repeatedly shown to miss 26% to 62% of acute myocardial infarctions (MI).^6,8-10 Another recent study involving internal medicine residents demonstrated that only half of the straightforward common ECGs were interpreted correctly, while 26% of trainees missed an acute MI and 56% missed ventricular tachycardia (VT).¹¹ Even cardiology subspecialty fellows demonstrated poor performance, missing up to 26% of ST-elevation MIs on ECGs that had multiple findings.¹² Inaccurate interpretations of ECGs can lead to inappropriate management decisions, adverse patient outcomes, unnecessary additional testing, and even preventable deaths.^4,13-15

Several guidelines have emphasized the importance of teaching trainees 12-lead ECG interpretation and have recognized the value of assessments in ensuring that learners acquire the necessary competencies.^16-19 Similarly, there have been many calls for more rigorous and structured curricula for ECG interpretation throughout undergraduate and postgraduate medical education.^11,16 However, we still lack a thoughtful guideline outlining the specific competencies that medical trainees should attain. This includes medical students, nurses working in hospital and in out-of-hospital settings, and residents of different specialties, including emergency medicine, cardiology, and electrophysiology (EP) fellows.

Setting goals and objectives for target learners is recognized to be the initial step and a core prerequisite for effective curriculum development.²⁰ In this publication, we summarize the objectives from previously published trainee assessments and propose reasonably attainable ECG interpretation competencies for both graduating medical students and residents at the end of their postgraduate training. This document is being endorsed by researchers and educators of 2 international societies dedicated to the study of electrical heart diseases: the International Society of Electrocardiology (ISE) and the International Society of Holter and Noninvasive Electrocardiology (ISHNE).

Current Competencies in Literature

We performed a systematic search to identify ECG competencies that are currently mentioned in the literature. Information was retrieved from MEDLINE (1946-2016) and EMBASE (1947-2016) by using the following MeSH terms: electrocardiogram, electrocardiography, electrocardiogram interpretation, electrocardiogram competency, medical school, medical student, undergraduate medicine, undergraduate medical education, residency education, internship, and residency. Our search was limited to English-language articles that studied physician trainees. The references of the full-length articles were examined for additional citations. The search revealed a total of 65 publications involving medical students and 120 publications involving residents. Abstracts of publications were then assessed for relevance, and the methods of the remaining articles were scrutinized for references to specific ECG interpretation objectives. This strategy narrowed the search to 9 and 14 articles involving medical students and residents, respectively. Studies were not graded for quality because the purpose of the search was to identify the specific ECG competencies that authors expected trainees to obtain. Almost all the articles proposed teaching tools and specific objectives that were defined by the investigators arbitrarily and assessed the trainee’s ability to interpret ECGs (summarized in supplementary Table).

Defining ECG Interpretation Competencies

The initial draft of proposed ECG interpretation competencies was developed at Queen’s University in Ontario, Canada. A list of ECG patterns and diagnoses previously mentioned in literature was used as a starting point. From there, each item was refined and organized into 4 main categories (see Figures 1 and 2).

Class A “Common electrocardiographic emergencies” represent patterns that are frequently seen in hospitals, in which accurate interpretation of the ECG within minutes is essential for delivering care that is potentially lifesaving to the patient (eg, ST-elevation MI).

The final distribution of ECG patterns is illustrated in Figure 2. (Figure 3 defines the learning objectives for each ECG pattern defined in Figure 2.) Here, we provide a rationale for

Class A: Common Electrocardiographic Emergencies

This group contains ECG findings that require recognition within minutes to deliver potentially lifesaving care. For this reason, undergraduate medical education programs should prioritize mastering class A conditions to minimize the risk of misdiagnosis and late recognition.

Class A patterns include ST elevation MI (STEMI) and localization of territory to ensure ST-segment elevations are seen in contiguous leads.^29,30 Students should learn the criteria for STEMI as per the “Universal Definition of Myocardial Infarction” and be aware of early signs of STEMI that may be seen prior to ST-segment changes, such as hyper-acute T-waves (increased amplitude and symmetrical).³⁰

Asystole, wide complex tachycardias, and ventricular fibrillation (VF) are all crucial ECG patterns that must be identified to deliver advanced cardiac life support (ACLS) care as per the 2010 AHA Guidelines for cardiopulmonary resuscitation and emergency cardio care.³¹ Of note, students should understand the differential diagnosis of wide complex tachycardias and should be able to suspect VF in clinically appropriate scenarios. We included the category “unstable/symptomatic supraventricular tachycardia” to represent rapid rhythms that are supraventricular in origin, which either produce symptoms or cause impairment of vital organ function.³¹ In emergency situations, it may not be crucial to correctly identify the specific supraventricular rhythm to deliver ACLS care; hence, the specific supraventricular tachycardia diagnoses were included in Class B.

Finally, we believe that medical students should be able to recognize long QT, hypo/hyperkalemia, and distinguish types of atrioventricular (AV) block. Distinguishing types of AV block is important because both third degree AV block and second degree AV block Mobitz II can be life threatening and require further investigation or emergency treatment in an inpatient setting.³² Prompt recognition of long QT is crucial because it can be associated with ventricular tachyarrhythmias. This includes a polymorphic pattern characterized by the twisting of QRS peaks around the baseline (torsades des pointes), which can eventually lead to VF.

Class B: Common Nonemergency Patterns

Class B patterns represent common findings that are seen on a daily basis that may impact patient care in a clinically appropriate context. Diagnoses in this section were divided into “tachycardia syndromes,” “bradycardia syndromes,” “conduction abnormalities,” “ischemia,” and “other.”

Undergraduate trainees should become proficient in identifying the cause of bradycardia and distinguishing types of AV blocks. Similarly, they should also have an approach to differentiate tachycardia syndromes.^33,34 These skills are required to correctly manage patients in both inpatient and outpatient settings. They should be taught in undergraduate programs and reinforced in postgraduate training.

Common findings, such as bundle branch blocks, left anterior fascicular block, premature ventricular/atrial complexes, electronic pacemakers, and left ventricular hypertrophy, are essential to the daily interpretation of ECGs. Junior learners should be proficient in recognizing these patterns. Findings consistent with pericarditis are not uncommon and can be very helpful to guide the clinician to the diagnosis. Notable exceptions from the medical student competency list include detection of lead misplacement, common artifacts, nonspecific intraventricular conduction delay, interatrial block, and benign early repolarization. These findings require a deeper understanding of electrocardiography and would be more appropriate for senior learners.

Class C: Uncommon Electrocardiographic Emergencies

Class C findings represent uncommon conditions that, if recognized, can prevent serious adverse patient outcomes. These include preexcitation, STEMI with preexisting left bundle branch block sinus pauses, Brugada pattern, hypothermia, effects of toxic drugs, ventricular aneurysm, and right ventricular hypertrophy. The recognition of these patterns is crucial to avoid severe adverse patient outcomes, and independent practicing physicians should be aware of these findings. However, given that a high proportion of senior medical students miss common electrocardiographic emergencies, undergraduate medical education programs should instead focus resources on ensuring medical students are proficient in identifying class A and class B conditions.^6,8-10 Postgraduate programs should ensure that postgraduate trainees can identify these potentially life-threatening conditions (see section “How to Teach Electrocardiology”).

Class D: Uncommon and Nonemergency Patterns

Class D findings represent less common findings that are not seen every day and do not require urgent medical attention. These include right atrial abnormality, left posterior fascicular block, low atrial rhythms, and electrolyte abnormalities that exclude potassium. Notably, electrolyte abnormalities are important to identify; however, typically, treatment is guided by the lab results.³⁵ Overall, postgraduate trainees should certainly be aware of these findings, but medical student training should instead focus on learning the framework and correctly identifying class A and class B ECG patterns.

Teaching ECG Interpretation Strategies

No clear teaching approaches to ECG interpretation have been described in the literature, and no recommendations on knowledge translation have been formally explored. A possible educational approach to the teaching of electrocardiology could involve several methods for helping students with ECG interpretation:³⁶

1. Pattern recognition: The ECG, at its most immediate level, is a graphic image, and recognition of images is essentially recognition of patterns. These patterns can only be learned through repeated visualization of examples with a written or verbal explanation. Repeated visualization over time will help avoid “erosion” of knowledge. Examples of learning tools include periodic in-person ECG rounds, well-illustrated books or atlases, and online tools with good quality ECGs and explanations. These learning opportunities are strongly reinforced by collecting cases from the clinical encounters of the trainee that illustrate the aforementioned patterns. Some of these patterns can be found in guidelines, such as the one published by the AHA and ACC.²⁹

2. Application of published criteria: Guidelines, review papers, and books offer diagnostic criteria for many entities, such as chamber enlargement, bundle branch blocks, and abnormal Q waves. Learning these criteria and applying them to the analysis of ECGs is a commonly used learning strategy.

3. Inductive-deductive reasoning: This strategy requires a deeper understanding of the pathophysiology behind ECG patterns. It requires ECGs to be interpreted in a certain clinical context, and the goal of ECG interpretation is to answer a clinical question that is used to guide patient care. This strategy typically employs the use of algorithms to lead the interpreter to the correct diagnosis, and mastery of this skill grows from ongoing clinical experience. Examples of the “inductive-deductive reasoning” are localizing an accessory AV pathway, the differential diagnosis of narrow or wide complex tachycardias, and identifying the site of coronary artery occlusion in a patient with a STEMI.

4. Ladder diagrams: Ladder diagrams have been used for over 100 years to graphically illustrate the mechanism of arrhythmias. They can be incredibly useful to help learners visualize impulse conduction in reentry mechanisms as well as other abnormal rhythms. However, there are some rhythms that are difficult to illustrate on ladder diagrams.³⁷

5. Peer and near-peer teaching: Peer teaching occurs when learners prepare and deliver teaching material to learners of a similar training level. The expectation to deliver a teaching session encourages students to learn and organize information in thoughtful ways. It builds strong teamwork skills and has been shown to positively affect all involved learners.^38-40

Each ECG interpretation strategy has its advantages, and we recommend that students be exposed to all available approaches if teaching resources are available.

Teaching Delivery Format

Each of the above teaching strategies can be delivered to students in various ways. The following teaching formats have been previously documented in the literature:

1. Classroom-based teaching: This is a traditional learning format that takes place in a large- or small-group classroom. Typically, these sessions are led by a single instructor, and they are focused on the direct sharing of information and group discussion.⁴¹

2. Electronic practice tools: Numerous electronic tools have been developed with the purpose of providing deliberate practice to master ECG interpretation. Some of these tools employ active learner engagement, while others provide a bank of ECGs for self-directed passive learning.^42-46

3. Video lectures: Short video lectures have been created to facilitate self-directed lecture based learning. These lectures are hosted on a variety of web-based platforms, including YouTube and Vimeo.⁴⁷

4. Traditional and electronic books: Numerous traditional textbooks have been published on ECG interpretation and are designed to facilitate independent learning. Some textbooks directly deliver teaching material, while others contain sets of ECGs to allow for repetitive practice. More recently, iBooks incorporating self-assessment tools have been used to assist ECG teaching.³⁴ The advantage of these tools is that they can also be used to supplement in-person classes.

5. Games: A unique ECG interpretation learning strategy consists of using puzzles and games to learn ECGs. This is meant to improve student engagement and interest in learning ECG interpretation.⁴⁸

Given that there is currently a lack of evidence-based data to support 1 instructional format over another, we do not favor any particular one. This decision should be left to instructors and individual learners based on their preference and available resources. Further studies would be helpful to determine the effectiveness of various methods in teaching ECG interpretation and to identify any additional specific factors that facilitate learning.

Evaluation Strategies

1. Longitudinal ongoing feedback: This form of feedback universally takes place in all training programs and focuses on direct observation and point-of-care feedback by a senior healthcare professional during clinical practice. Typically, the feedback is informal and is centered around specific case presentations.

2. Formative testing: This assessment strategy is aimed at monitoring the learning of trainees and providing them with appropriate feedback. Tutors and teachers can use this data to individualize instruction and fill any training gaps that individuals and the class may have. Students themselves can use this information to encourage additional study to ensure they acquire required skills. Examples of formative testing are low-stakes in-training exams and asking audience questions during a workshop or lecture.⁴⁹

3. Summative testing: Summative assessments are created to measure the level of proficiency developed by a learner and compare it against some standard or benchmark. This form of assessment establishes the extent to which educational objectives have been met. The most common example is an end-of-term examination.

Online ECG examination has been successfully used to provide methods of testing. They are easy to distribute, highly convenient for learners, and allow the display of high-quality graphics. They can also be graded electronically, thereby minimizing the resources required to administer and grade exams.^36,50

We recommend using a combination of assessment formats to ensure the optimal evaluation of learner skill and to focus learning on areas of weakness. Summative assessments are highly valuable to ensure learners acquired the necessary ECG interpretation competencies. Remediation strategies should be available to provide additional practice to learners who do not meet competencies expected at their level of training.

The Need for ECG Interpretation Competencies and Milestones

Since the introduction of ECG in the late 1800s, there continues to be a significant variation in ECG interpretation skills among trainees and medical professionals.^4,6-12 Concerns continue to exist about the rate of missed diagnoses involving critical ECGs, leading to inappropriate patient management decisions. Despite the obvious need, teaching ECG interpretation is given little emphasis in medical education, and the curriculum remains quite disorganized. In this position paper, we call for a more structured ECG interpretation curriculum in medical education and hope to assist this process by assigning ECG patterns to 2 milestones in training: graduating medical students and first year postgraduate medical residents.

Defining competencies would help medical education programs to focus resources on teaching clinically important conditions for the appropriate level of training. We divide ECG findings into 4 categories (classes A to D), and we place emphasis on learning electrocardiographic emergencies early in training and spending less time on ECG findings that are unlikely to change patient management.

The goal is to ensure 100% recognition of class A (electrocardiographic emergencies) by the end of medical school. To ensure each medical education program fulfils this goal, a structured curriculum including a summative assessment is required.

Methods of Teaching

Various instructional mediums have been successfully implemented to teach ECG interpretation competencies, including lectures, puzzles, web-based programs, iBooks, and YouTube.^{34-41-44,47,48.51-53} A survey of clerkship directors in internal medicine revealed that 75% of clerkship programs teach ECG interpretation in a classroom lecture-based setting, 44% use teaching rounds, and only 17% utilize online/web-based instruction.³ Canadian family medicine programs have a relatively equal distribution between classroom-based, computer-based, and bedside teaching.⁵

In comparing the efficacy of instructional styles, several small comparative studies favor an electronic teaching format because of the enhanced learner interaction and visual learning, but there does not appear to be a consistently proven large advantage of 1 teaching format over another.^43,48,51,54 The overall theme emerging from this literature is the importance of repetition and active engagement in ECG interpretation, which appear to be more important than 1 particular strategy.²² Computer-based training appears to deliver these 2 qualities, unlike the traditional lecture-style passive learning model. The concept of repetition and engagement is also well supported in medical education literature outside ECG interpretation.^55,56

Given these data, we recommend that each medical education program select teaching methods based on their available resources, as long as adequate teaching time is allotted to ensure that trainees acquire the competencies defined in this publication.

Assessment Methods

It appears that the larger factor in determining ECG interpretation performance is not the learning format, but the form of assessment. Two studies have demonstrated that summative assessment substantially improves ECG interpretation performance when compared with formative assessment; in fact, this effect was so large that it overshadowed any small difference in teaching formats.^57,58 This concept aligns with medical education literature, which acknowledges that assessment drives learning by raising the stakes, thereby boosting student effort and encouraging learning to an effect much larger than can be generated by any particular learning style.^57,59 Nevertheless, well-designed formative assessment can focus students on effective learning by identifying gaps and important information.⁶⁰ Only 33% of Canadian family medicine residency programs and 71% of American clerkship programs have formal assessment of ECG interpretation skills.^3,5 There is no doubt that assessment, both formative and summative, should be implemented in all undergraduate and postgraduate medical training programs. Online assessment methods have the advantage of delivering high-quality images and a variety of question formats; hence, their use should be encouraged.^36,50,61-63

Teaching Personnel and Timing of Training

Who should teach ECG interpretation and when should this teaching take place? ECG interpretation in training programs is typically taught by attending physicians in each respective field. However, given that there is a large ECG interpretation error rate by noncardiologist physicians, we advise that ECG training content be created with input from own-specialty attending physicians and cardiologists.⁴ This teaching should take place early in medical school at the time medical students learn pathophysiology of the heart and should continue throughout training. Longitudinal training is preferred to block-based training because of improved resident satisfaction, but medical education literature did not reveal a difference in student performance with either strategy.^64-66

Despite its immense clinical value, there continues to be a lack of a comprehensive ECG interpretation curriculum in medical education programs. The goal of this position paper is to encourage the development of organized curricula in undergraduate and postgraduate medical education programs, and to ensure the acquisition of level-appropriate ECG interpretation skills while maintaining patient safety. We assist this process by grouping ECG findings into 4 classes (A to D) based on the frequency of encounter and emergent nature and by assigning them to each level of training. Methods of teaching ECG interpretation are less important and can be selected based on the available resources of each education program and student preference; however, online learning is encouraged. We also recommend that summative trainee evaluation methods be implemented in all programs to ensure that appropriate competencies are acquired and to further encourage self-directed learning. Resources should be allocated to ensure that every trainee is reaching their training milestones and should ensure that no electrocardiographic emergency (class A condition) is ever missed by a trainee. We hope that these guidelines will inform medical education systems and help prevent adverse patient outcomes caused by the misinterpretation of this valuable clinical diagnostic tool.

Disclosure

On behalf of all authors, the corresponding author states that there is no conflict of interest. This manuscript did not utilize any sources of funding.

The 12-lead electrocardiogram (ECG) remains one of the most widely used and readily available diagnostic tests in modern medicine.¹ Reflecting the electrical behavior of the heart, this point-of-care diagnostic test is used in almost every area of medicine for diagnosis, prognostication, and selection of appropriate treatment. The ECG is sometimes the only and most efficient way of detecting life-threatening conditions, thus allowing a timely delivery of emergency care.² However, the practical power of the 12-lead ECG relies on the ability of the clinician to interpret this test correctly.

For decades, ECG interpretation has been a core component of undergraduate and postgraduate medical training.^3-5 Unfortunately, numerous studies have demonstrated alarming rates of inaccuracy and variability in interpreting ECGs among trainees at all levels of education.^4,6,7 Senior medical students have been repeatedly shown to miss 26% to 62% of acute myocardial infarctions (MI).^6,8-10 Another recent study involving internal medicine residents demonstrated that only half of the straightforward common ECGs were interpreted correctly, while 26% of trainees missed an acute MI and 56% missed ventricular tachycardia (VT).¹¹ Even cardiology subspecialty fellows demonstrated poor performance, missing up to 26% of ST-elevation MIs on ECGs that had multiple findings.¹² Inaccurate interpretations of ECGs can lead to inappropriate management decisions, adverse patient outcomes, unnecessary additional testing, and even preventable deaths.^4,13-15

Several guidelines have emphasized the importance of teaching trainees 12-lead ECG interpretation and have recognized the value of assessments in ensuring that learners acquire the necessary competencies.^16-19 Similarly, there have been many calls for more rigorous and structured curricula for ECG interpretation throughout undergraduate and postgraduate medical education.^11,16 However, we still lack a thoughtful guideline outlining the specific competencies that medical trainees should attain. This includes medical students, nurses working in hospital and in out-of-hospital settings, and residents of different specialties, including emergency medicine, cardiology, and electrophysiology (EP) fellows.

Setting goals and objectives for target learners is recognized to be the initial step and a core prerequisite for effective curriculum development.²⁰ In this publication, we summarize the objectives from previously published trainee assessments and propose reasonably attainable ECG interpretation competencies for both graduating medical students and residents at the end of their postgraduate training. This document is being endorsed by researchers and educators of 2 international societies dedicated to the study of electrical heart diseases: the International Society of Electrocardiology (ISE) and the International Society of Holter and Noninvasive Electrocardiology (ISHNE).

Current Competencies in Literature

We performed a systematic search to identify ECG competencies that are currently mentioned in the literature. Information was retrieved from MEDLINE (1946-2016) and EMBASE (1947-2016) by using the following MeSH terms: electrocardiogram, electrocardiography, electrocardiogram interpretation, electrocardiogram competency, medical school, medical student, undergraduate medicine, undergraduate medical education, residency education, internship, and residency. Our search was limited to English-language articles that studied physician trainees. The references of the full-length articles were examined for additional citations. The search revealed a total of 65 publications involving medical students and 120 publications involving residents. Abstracts of publications were then assessed for relevance, and the methods of the remaining articles were scrutinized for references to specific ECG interpretation objectives. This strategy narrowed the search to 9 and 14 articles involving medical students and residents, respectively. Studies were not graded for quality because the purpose of the search was to identify the specific ECG competencies that authors expected trainees to obtain. Almost all the articles proposed teaching tools and specific objectives that were defined by the investigators arbitrarily and assessed the trainee’s ability to interpret ECGs (summarized in supplementary Table).

Defining ECG Interpretation Competencies

The initial draft of proposed ECG interpretation competencies was developed at Queen’s University in Ontario, Canada. A list of ECG patterns and diagnoses previously mentioned in literature was used as a starting point. From there, each item was refined and organized into 4 main categories (see Figures 1 and 2).

Class A “Common electrocardiographic emergencies” represent patterns that are frequently seen in hospitals, in which accurate interpretation of the ECG within minutes is essential for delivering care that is potentially lifesaving to the patient (eg, ST-elevation MI).

The final distribution of ECG patterns is illustrated in Figure 2. (Figure 3 defines the learning objectives for each ECG pattern defined in Figure 2.) Here, we provide a rationale for

Class A: Common Electrocardiographic Emergencies

This group contains ECG findings that require recognition within minutes to deliver potentially lifesaving care. For this reason, undergraduate medical education programs should prioritize mastering class A conditions to minimize the risk of misdiagnosis and late recognition.

Class A patterns include ST elevation MI (STEMI) and localization of territory to ensure ST-segment elevations are seen in contiguous leads.^29,30 Students should learn the criteria for STEMI as per the “Universal Definition of Myocardial Infarction” and be aware of early signs of STEMI that may be seen prior to ST-segment changes, such as hyper-acute T-waves (increased amplitude and symmetrical).³⁰

Asystole, wide complex tachycardias, and ventricular fibrillation (VF) are all crucial ECG patterns that must be identified to deliver advanced cardiac life support (ACLS) care as per the 2010 AHA Guidelines for cardiopulmonary resuscitation and emergency cardio care.³¹ Of note, students should understand the differential diagnosis of wide complex tachycardias and should be able to suspect VF in clinically appropriate scenarios. We included the category “unstable/symptomatic supraventricular tachycardia” to represent rapid rhythms that are supraventricular in origin, which either produce symptoms or cause impairment of vital organ function.³¹ In emergency situations, it may not be crucial to correctly identify the specific supraventricular rhythm to deliver ACLS care; hence, the specific supraventricular tachycardia diagnoses were included in Class B.

Finally, we believe that medical students should be able to recognize long QT, hypo/hyperkalemia, and distinguish types of atrioventricular (AV) block. Distinguishing types of AV block is important because both third degree AV block and second degree AV block Mobitz II can be life threatening and require further investigation or emergency treatment in an inpatient setting.³² Prompt recognition of long QT is crucial because it can be associated with ventricular tachyarrhythmias. This includes a polymorphic pattern characterized by the twisting of QRS peaks around the baseline (torsades des pointes), which can eventually lead to VF.

Class B: Common Nonemergency Patterns

Class B patterns represent common findings that are seen on a daily basis that may impact patient care in a clinically appropriate context. Diagnoses in this section were divided into “tachycardia syndromes,” “bradycardia syndromes,” “conduction abnormalities,” “ischemia,” and “other.”

Undergraduate trainees should become proficient in identifying the cause of bradycardia and distinguishing types of AV blocks. Similarly, they should also have an approach to differentiate tachycardia syndromes.^33,34 These skills are required to correctly manage patients in both inpatient and outpatient settings. They should be taught in undergraduate programs and reinforced in postgraduate training.

Common findings, such as bundle branch blocks, left anterior fascicular block, premature ventricular/atrial complexes, electronic pacemakers, and left ventricular hypertrophy, are essential to the daily interpretation of ECGs. Junior learners should be proficient in recognizing these patterns. Findings consistent with pericarditis are not uncommon and can be very helpful to guide the clinician to the diagnosis. Notable exceptions from the medical student competency list include detection of lead misplacement, common artifacts, nonspecific intraventricular conduction delay, interatrial block, and benign early repolarization. These findings require a deeper understanding of electrocardiography and would be more appropriate for senior learners.

Class C: Uncommon Electrocardiographic Emergencies

Class C findings represent uncommon conditions that, if recognized, can prevent serious adverse patient outcomes. These include preexcitation, STEMI with preexisting left bundle branch block sinus pauses, Brugada pattern, hypothermia, effects of toxic drugs, ventricular aneurysm, and right ventricular hypertrophy. The recognition of these patterns is crucial to avoid severe adverse patient outcomes, and independent practicing physicians should be aware of these findings. However, given that a high proportion of senior medical students miss common electrocardiographic emergencies, undergraduate medical education programs should instead focus resources on ensuring medical students are proficient in identifying class A and class B conditions.^6,8-10 Postgraduate programs should ensure that postgraduate trainees can identify these potentially life-threatening conditions (see section “How to Teach Electrocardiology”).

Class D: Uncommon and Nonemergency Patterns

Class D findings represent less common findings that are not seen every day and do not require urgent medical attention. These include right atrial abnormality, left posterior fascicular block, low atrial rhythms, and electrolyte abnormalities that exclude potassium. Notably, electrolyte abnormalities are important to identify; however, typically, treatment is guided by the lab results.³⁵ Overall, postgraduate trainees should certainly be aware of these findings, but medical student training should instead focus on learning the framework and correctly identifying class A and class B ECG patterns.

Teaching ECG Interpretation Strategies

No clear teaching approaches to ECG interpretation have been described in the literature, and no recommendations on knowledge translation have been formally explored. A possible educational approach to the teaching of electrocardiology could involve several methods for helping students with ECG interpretation:³⁶

1. Pattern recognition: The ECG, at its most immediate level, is a graphic image, and recognition of images is essentially recognition of patterns. These patterns can only be learned through repeated visualization of examples with a written or verbal explanation. Repeated visualization over time will help avoid “erosion” of knowledge. Examples of learning tools include periodic in-person ECG rounds, well-illustrated books or atlases, and online tools with good quality ECGs and explanations. These learning opportunities are strongly reinforced by collecting cases from the clinical encounters of the trainee that illustrate the aforementioned patterns. Some of these patterns can be found in guidelines, such as the one published by the AHA and ACC.²⁹

2. Application of published criteria: Guidelines, review papers, and books offer diagnostic criteria for many entities, such as chamber enlargement, bundle branch blocks, and abnormal Q waves. Learning these criteria and applying them to the analysis of ECGs is a commonly used learning strategy.

3. Inductive-deductive reasoning: This strategy requires a deeper understanding of the pathophysiology behind ECG patterns. It requires ECGs to be interpreted in a certain clinical context, and the goal of ECG interpretation is to answer a clinical question that is used to guide patient care. This strategy typically employs the use of algorithms to lead the interpreter to the correct diagnosis, and mastery of this skill grows from ongoing clinical experience. Examples of the “inductive-deductive reasoning” are localizing an accessory AV pathway, the differential diagnosis of narrow or wide complex tachycardias, and identifying the site of coronary artery occlusion in a patient with a STEMI.

4. Ladder diagrams: Ladder diagrams have been used for over 100 years to graphically illustrate the mechanism of arrhythmias. They can be incredibly useful to help learners visualize impulse conduction in reentry mechanisms as well as other abnormal rhythms. However, there are some rhythms that are difficult to illustrate on ladder diagrams.³⁷

5. Peer and near-peer teaching: Peer teaching occurs when learners prepare and deliver teaching material to learners of a similar training level. The expectation to deliver a teaching session encourages students to learn and organize information in thoughtful ways. It builds strong teamwork skills and has been shown to positively affect all involved learners.^38-40

Each ECG interpretation strategy has its advantages, and we recommend that students be exposed to all available approaches if teaching resources are available.

Teaching Delivery Format

Each of the above teaching strategies can be delivered to students in various ways. The following teaching formats have been previously documented in the literature:

1. Classroom-based teaching: This is a traditional learning format that takes place in a large- or small-group classroom. Typically, these sessions are led by a single instructor, and they are focused on the direct sharing of information and group discussion.⁴¹

2. Electronic practice tools: Numerous electronic tools have been developed with the purpose of providing deliberate practice to master ECG interpretation. Some of these tools employ active learner engagement, while others provide a bank of ECGs for self-directed passive learning.^42-46

3. Video lectures: Short video lectures have been created to facilitate self-directed lecture based learning. These lectures are hosted on a variety of web-based platforms, including YouTube and Vimeo.⁴⁷

4. Traditional and electronic books: Numerous traditional textbooks have been published on ECG interpretation and are designed to facilitate independent learning. Some textbooks directly deliver teaching material, while others contain sets of ECGs to allow for repetitive practice. More recently, iBooks incorporating self-assessment tools have been used to assist ECG teaching.³⁴ The advantage of these tools is that they can also be used to supplement in-person classes.

5. Games: A unique ECG interpretation learning strategy consists of using puzzles and games to learn ECGs. This is meant to improve student engagement and interest in learning ECG interpretation.⁴⁸

Given that there is currently a lack of evidence-based data to support 1 instructional format over another, we do not favor any particular one. This decision should be left to instructors and individual learners based on their preference and available resources. Further studies would be helpful to determine the effectiveness of various methods in teaching ECG interpretation and to identify any additional specific factors that facilitate learning.

Evaluation Strategies

1. Longitudinal ongoing feedback: This form of feedback universally takes place in all training programs and focuses on direct observation and point-of-care feedback by a senior healthcare professional during clinical practice. Typically, the feedback is informal and is centered around specific case presentations.

2. Formative testing: This assessment strategy is aimed at monitoring the learning of trainees and providing them with appropriate feedback. Tutors and teachers can use this data to individualize instruction and fill any training gaps that individuals and the class may have. Students themselves can use this information to encourage additional study to ensure they acquire required skills. Examples of formative testing are low-stakes in-training exams and asking audience questions during a workshop or lecture.⁴⁹

3. Summative testing: Summative assessments are created to measure the level of proficiency developed by a learner and compare it against some standard or benchmark. This form of assessment establishes the extent to which educational objectives have been met. The most common example is an end-of-term examination.

Online ECG examination has been successfully used to provide methods of testing. They are easy to distribute, highly convenient for learners, and allow the display of high-quality graphics. They can also be graded electronically, thereby minimizing the resources required to administer and grade exams.^36,50

We recommend using a combination of assessment formats to ensure the optimal evaluation of learner skill and to focus learning on areas of weakness. Summative assessments are highly valuable to ensure learners acquired the necessary ECG interpretation competencies. Remediation strategies should be available to provide additional practice to learners who do not meet competencies expected at their level of training.

The Need for ECG Interpretation Competencies and Milestones

Since the introduction of ECG in the late 1800s, there continues to be a significant variation in ECG interpretation skills among trainees and medical professionals.^4,6-12 Concerns continue to exist about the rate of missed diagnoses involving critical ECGs, leading to inappropriate patient management decisions. Despite the obvious need, teaching ECG interpretation is given little emphasis in medical education, and the curriculum remains quite disorganized. In this position paper, we call for a more structured ECG interpretation curriculum in medical education and hope to assist this process by assigning ECG patterns to 2 milestones in training: graduating medical students and first year postgraduate medical residents.

Defining competencies would help medical education programs to focus resources on teaching clinically important conditions for the appropriate level of training. We divide ECG findings into 4 categories (classes A to D), and we place emphasis on learning electrocardiographic emergencies early in training and spending less time on ECG findings that are unlikely to change patient management.

The goal is to ensure 100% recognition of class A (electrocardiographic emergencies) by the end of medical school. To ensure each medical education program fulfils this goal, a structured curriculum including a summative assessment is required.

Methods of Teaching

Various instructional mediums have been successfully implemented to teach ECG interpretation competencies, including lectures, puzzles, web-based programs, iBooks, and YouTube.^{34-41-44,47,48.51-53} A survey of clerkship directors in internal medicine revealed that 75% of clerkship programs teach ECG interpretation in a classroom lecture-based setting, 44% use teaching rounds, and only 17% utilize online/web-based instruction.³ Canadian family medicine programs have a relatively equal distribution between classroom-based, computer-based, and bedside teaching.⁵

In comparing the efficacy of instructional styles, several small comparative studies favor an electronic teaching format because of the enhanced learner interaction and visual learning, but there does not appear to be a consistently proven large advantage of 1 teaching format over another.^43,48,51,54 The overall theme emerging from this literature is the importance of repetition and active engagement in ECG interpretation, which appear to be more important than 1 particular strategy.²² Computer-based training appears to deliver these 2 qualities, unlike the traditional lecture-style passive learning model. The concept of repetition and engagement is also well supported in medical education literature outside ECG interpretation.^55,56

Given these data, we recommend that each medical education program select teaching methods based on their available resources, as long as adequate teaching time is allotted to ensure that trainees acquire the competencies defined in this publication.

Assessment Methods

It appears that the larger factor in determining ECG interpretation performance is not the learning format, but the form of assessment. Two studies have demonstrated that summative assessment substantially improves ECG interpretation performance when compared with formative assessment; in fact, this effect was so large that it overshadowed any small difference in teaching formats.^57,58 This concept aligns with medical education literature, which acknowledges that assessment drives learning by raising the stakes, thereby boosting student effort and encouraging learning to an effect much larger than can be generated by any particular learning style.^57,59 Nevertheless, well-designed formative assessment can focus students on effective learning by identifying gaps and important information.⁶⁰ Only 33% of Canadian family medicine residency programs and 71% of American clerkship programs have formal assessment of ECG interpretation skills.^3,5 There is no doubt that assessment, both formative and summative, should be implemented in all undergraduate and postgraduate medical training programs. Online assessment methods have the advantage of delivering high-quality images and a variety of question formats; hence, their use should be encouraged.^36,50,61-63

Teaching Personnel and Timing of Training

Who should teach ECG interpretation and when should this teaching take place? ECG interpretation in training programs is typically taught by attending physicians in each respective field. However, given that there is a large ECG interpretation error rate by noncardiologist physicians, we advise that ECG training content be created with input from own-specialty attending physicians and cardiologists.⁴ This teaching should take place early in medical school at the time medical students learn pathophysiology of the heart and should continue throughout training. Longitudinal training is preferred to block-based training because of improved resident satisfaction, but medical education literature did not reveal a difference in student performance with either strategy.^64-66

Despite its immense clinical value, there continues to be a lack of a comprehensive ECG interpretation curriculum in medical education programs. The goal of this position paper is to encourage the development of organized curricula in undergraduate and postgraduate medical education programs, and to ensure the acquisition of level-appropriate ECG interpretation skills while maintaining patient safety. We assist this process by grouping ECG findings into 4 classes (A to D) based on the frequency of encounter and emergent nature and by assigning them to each level of training. Methods of teaching ECG interpretation are less important and can be selected based on the available resources of each education program and student preference; however, online learning is encouraged. We also recommend that summative trainee evaluation methods be implemented in all programs to ensure that appropriate competencies are acquired and to further encourage self-directed learning. Resources should be allocated to ensure that every trainee is reaching their training milestones and should ensure that no electrocardiographic emergency (class A condition) is ever missed by a trainee. We hope that these guidelines will inform medical education systems and help prevent adverse patient outcomes caused by the misinterpretation of this valuable clinical diagnostic tool.

Disclosure

On behalf of all authors, the corresponding author states that there is no conflict of interest. This manuscript did not utilize any sources of funding.

The 12-lead electrocardiogram (ECG) remains one of the most widely used and readily available diagnostic tests in modern medicine.¹ Reflecting the electrical behavior of the heart, this point-of-care diagnostic test is used in almost every area of medicine for diagnosis, prognostication, and selection of appropriate treatment. The ECG is sometimes the only and most efficient way of detecting life-threatening conditions, thus allowing a timely delivery of emergency care.² However, the practical power of the 12-lead ECG relies on the ability of the clinician to interpret this test correctly.

For decades, ECG interpretation has been a core component of undergraduate and postgraduate medical training.^3-5 Unfortunately, numerous studies have demonstrated alarming rates of inaccuracy and variability in interpreting ECGs among trainees at all levels of education.^4,6,7 Senior medical students have been repeatedly shown to miss 26% to 62% of acute myocardial infarctions (MI).^6,8-10 Another recent study involving internal medicine residents demonstrated that only half of the straightforward common ECGs were interpreted correctly, while 26% of trainees missed an acute MI and 56% missed ventricular tachycardia (VT).¹¹ Even cardiology subspecialty fellows demonstrated poor performance, missing up to 26% of ST-elevation MIs on ECGs that had multiple findings.¹² Inaccurate interpretations of ECGs can lead to inappropriate management decisions, adverse patient outcomes, unnecessary additional testing, and even preventable deaths.^4,13-15

Several guidelines have emphasized the importance of teaching trainees 12-lead ECG interpretation and have recognized the value of assessments in ensuring that learners acquire the necessary competencies.^16-19 Similarly, there have been many calls for more rigorous and structured curricula for ECG interpretation throughout undergraduate and postgraduate medical education.^11,16 However, we still lack a thoughtful guideline outlining the specific competencies that medical trainees should attain. This includes medical students, nurses working in hospital and in out-of-hospital settings, and residents of different specialties, including emergency medicine, cardiology, and electrophysiology (EP) fellows.

Setting goals and objectives for target learners is recognized to be the initial step and a core prerequisite for effective curriculum development.²⁰ In this publication, we summarize the objectives from previously published trainee assessments and propose reasonably attainable ECG interpretation competencies for both graduating medical students and residents at the end of their postgraduate training. This document is being endorsed by researchers and educators of 2 international societies dedicated to the study of electrical heart diseases: the International Society of Electrocardiology (ISE) and the International Society of Holter and Noninvasive Electrocardiology (ISHNE).

Current Competencies in Literature

We performed a systematic search to identify ECG competencies that are currently mentioned in the literature. Information was retrieved from MEDLINE (1946-2016) and EMBASE (1947-2016) by using the following MeSH terms: electrocardiogram, electrocardiography, electrocardiogram interpretation, electrocardiogram competency, medical school, medical student, undergraduate medicine, undergraduate medical education, residency education, internship, and residency. Our search was limited to English-language articles that studied physician trainees. The references of the full-length articles were examined for additional citations. The search revealed a total of 65 publications involving medical students and 120 publications involving residents. Abstracts of publications were then assessed for relevance, and the methods of the remaining articles were scrutinized for references to specific ECG interpretation objectives. This strategy narrowed the search to 9 and 14 articles involving medical students and residents, respectively. Studies were not graded for quality because the purpose of the search was to identify the specific ECG competencies that authors expected trainees to obtain. Almost all the articles proposed teaching tools and specific objectives that were defined by the investigators arbitrarily and assessed the trainee’s ability to interpret ECGs (summarized in supplementary Table).

Defining ECG Interpretation Competencies

The initial draft of proposed ECG interpretation competencies was developed at Queen’s University in Ontario, Canada. A list of ECG patterns and diagnoses previously mentioned in literature was used as a starting point. From there, each item was refined and organized into 4 main categories (see Figures 1 and 2).

Class A “Common electrocardiographic emergencies” represent patterns that are frequently seen in hospitals, in which accurate interpretation of the ECG within minutes is essential for delivering care that is potentially lifesaving to the patient (eg, ST-elevation MI).

The final distribution of ECG patterns is illustrated in Figure 2. (Figure 3 defines the learning objectives for each ECG pattern defined in Figure 2.) Here, we provide a rationale for

Class A: Common Electrocardiographic Emergencies

This group contains ECG findings that require recognition within minutes to deliver potentially lifesaving care. For this reason, undergraduate medical education programs should prioritize mastering class A conditions to minimize the risk of misdiagnosis and late recognition.

Class A patterns include ST elevation MI (STEMI) and localization of territory to ensure ST-segment elevations are seen in contiguous leads.^29,30 Students should learn the criteria for STEMI as per the “Universal Definition of Myocardial Infarction” and be aware of early signs of STEMI that may be seen prior to ST-segment changes, such as hyper-acute T-waves (increased amplitude and symmetrical).³⁰

Asystole, wide complex tachycardias, and ventricular fibrillation (VF) are all crucial ECG patterns that must be identified to deliver advanced cardiac life support (ACLS) care as per the 2010 AHA Guidelines for cardiopulmonary resuscitation and emergency cardio care.³¹ Of note, students should understand the differential diagnosis of wide complex tachycardias and should be able to suspect VF in clinically appropriate scenarios. We included the category “unstable/symptomatic supraventricular tachycardia” to represent rapid rhythms that are supraventricular in origin, which either produce symptoms or cause impairment of vital organ function.³¹ In emergency situations, it may not be crucial to correctly identify the specific supraventricular rhythm to deliver ACLS care; hence, the specific supraventricular tachycardia diagnoses were included in Class B.

Finally, we believe that medical students should be able to recognize long QT, hypo/hyperkalemia, and distinguish types of atrioventricular (AV) block. Distinguishing types of AV block is important because both third degree AV block and second degree AV block Mobitz II can be life threatening and require further investigation or emergency treatment in an inpatient setting.³² Prompt recognition of long QT is crucial because it can be associated with ventricular tachyarrhythmias. This includes a polymorphic pattern characterized by the twisting of QRS peaks around the baseline (torsades des pointes), which can eventually lead to VF.

Class B: Common Nonemergency Patterns

Class B patterns represent common findings that are seen on a daily basis that may impact patient care in a clinically appropriate context. Diagnoses in this section were divided into “tachycardia syndromes,” “bradycardia syndromes,” “conduction abnormalities,” “ischemia,” and “other.”

Undergraduate trainees should become proficient in identifying the cause of bradycardia and distinguishing types of AV blocks. Similarly, they should also have an approach to differentiate tachycardia syndromes.^33,34 These skills are required to correctly manage patients in both inpatient and outpatient settings. They should be taught in undergraduate programs and reinforced in postgraduate training.

Common findings, such as bundle branch blocks, left anterior fascicular block, premature ventricular/atrial complexes, electronic pacemakers, and left ventricular hypertrophy, are essential to the daily interpretation of ECGs. Junior learners should be proficient in recognizing these patterns. Findings consistent with pericarditis are not uncommon and can be very helpful to guide the clinician to the diagnosis. Notable exceptions from the medical student competency list include detection of lead misplacement, common artifacts, nonspecific intraventricular conduction delay, interatrial block, and benign early repolarization. These findings require a deeper understanding of electrocardiography and would be more appropriate for senior learners.

Class C: Uncommon Electrocardiographic Emergencies

Class C findings represent uncommon conditions that, if recognized, can prevent serious adverse patient outcomes. These include preexcitation, STEMI with preexisting left bundle branch block sinus pauses, Brugada pattern, hypothermia, effects of toxic drugs, ventricular aneurysm, and right ventricular hypertrophy. The recognition of these patterns is crucial to avoid severe adverse patient outcomes, and independent practicing physicians should be aware of these findings. However, given that a high proportion of senior medical students miss common electrocardiographic emergencies, undergraduate medical education programs should instead focus resources on ensuring medical students are proficient in identifying class A and class B conditions.^6,8-10 Postgraduate programs should ensure that postgraduate trainees can identify these potentially life-threatening conditions (see section “How to Teach Electrocardiology”).

Class D: Uncommon and Nonemergency Patterns

Class D findings represent less common findings that are not seen every day and do not require urgent medical attention. These include right atrial abnormality, left posterior fascicular block, low atrial rhythms, and electrolyte abnormalities that exclude potassium. Notably, electrolyte abnormalities are important to identify; however, typically, treatment is guided by the lab results.³⁵ Overall, postgraduate trainees should certainly be aware of these findings, but medical student training should instead focus on learning the framework and correctly identifying class A and class B ECG patterns.

Teaching ECG Interpretation Strategies

No clear teaching approaches to ECG interpretation have been described in the literature, and no recommendations on knowledge translation have been formally explored. A possible educational approach to the teaching of electrocardiology could involve several methods for helping students with ECG interpretation:³⁶

1. Pattern recognition: The ECG, at its most immediate level, is a graphic image, and recognition of images is essentially recognition of patterns. These patterns can only be learned through repeated visualization of examples with a written or verbal explanation. Repeated visualization over time will help avoid “erosion” of knowledge. Examples of learning tools include periodic in-person ECG rounds, well-illustrated books or atlases, and online tools with good quality ECGs and explanations. These learning opportunities are strongly reinforced by collecting cases from the clinical encounters of the trainee that illustrate the aforementioned patterns. Some of these patterns can be found in guidelines, such as the one published by the AHA and ACC.²⁹

2. Application of published criteria: Guidelines, review papers, and books offer diagnostic criteria for many entities, such as chamber enlargement, bundle branch blocks, and abnormal Q waves. Learning these criteria and applying them to the analysis of ECGs is a commonly used learning strategy.

3. Inductive-deductive reasoning: This strategy requires a deeper understanding of the pathophysiology behind ECG patterns. It requires ECGs to be interpreted in a certain clinical context, and the goal of ECG interpretation is to answer a clinical question that is used to guide patient care. This strategy typically employs the use of algorithms to lead the interpreter to the correct diagnosis, and mastery of this skill grows from ongoing clinical experience. Examples of the “inductive-deductive reasoning” are localizing an accessory AV pathway, the differential diagnosis of narrow or wide complex tachycardias, and identifying the site of coronary artery occlusion in a patient with a STEMI.

4. Ladder diagrams: Ladder diagrams have been used for over 100 years to graphically illustrate the mechanism of arrhythmias. They can be incredibly useful to help learners visualize impulse conduction in reentry mechanisms as well as other abnormal rhythms. However, there are some rhythms that are difficult to illustrate on ladder diagrams.³⁷

5. Peer and near-peer teaching: Peer teaching occurs when learners prepare and deliver teaching material to learners of a similar training level. The expectation to deliver a teaching session encourages students to learn and organize information in thoughtful ways. It builds strong teamwork skills and has been shown to positively affect all involved learners.^38-40

Each ECG interpretation strategy has its advantages, and we recommend that students be exposed to all available approaches if teaching resources are available.

Teaching Delivery Format

Each of the above teaching strategies can be delivered to students in various ways. The following teaching formats have been previously documented in the literature:

1. Classroom-based teaching: This is a traditional learning format that takes place in a large- or small-group classroom. Typically, these sessions are led by a single instructor, and they are focused on the direct sharing of information and group discussion.⁴¹

2. Electronic practice tools: Numerous electronic tools have been developed with the purpose of providing deliberate practice to master ECG interpretation. Some of these tools employ active learner engagement, while others provide a bank of ECGs for self-directed passive learning.^42-46

3. Video lectures: Short video lectures have been created to facilitate self-directed lecture based learning. These lectures are hosted on a variety of web-based platforms, including YouTube and Vimeo.⁴⁷

4. Traditional and electronic books: Numerous traditional textbooks have been published on ECG interpretation and are designed to facilitate independent learning. Some textbooks directly deliver teaching material, while others contain sets of ECGs to allow for repetitive practice. More recently, iBooks incorporating self-assessment tools have been used to assist ECG teaching.³⁴ The advantage of these tools is that they can also be used to supplement in-person classes.

5. Games: A unique ECG interpretation learning strategy consists of using puzzles and games to learn ECGs. This is meant to improve student engagement and interest in learning ECG interpretation.⁴⁸

Given that there is currently a lack of evidence-based data to support 1 instructional format over another, we do not favor any particular one. This decision should be left to instructors and individual learners based on their preference and available resources. Further studies would be helpful to determine the effectiveness of various methods in teaching ECG interpretation and to identify any additional specific factors that facilitate learning.

Evaluation Strategies

1. Longitudinal ongoing feedback: This form of feedback universally takes place in all training programs and focuses on direct observation and point-of-care feedback by a senior healthcare professional during clinical practice. Typically, the feedback is informal and is centered around specific case presentations.

2. Formative testing: This assessment strategy is aimed at monitoring the learning of trainees and providing them with appropriate feedback. Tutors and teachers can use this data to individualize instruction and fill any training gaps that individuals and the class may have. Students themselves can use this information to encourage additional study to ensure they acquire required skills. Examples of formative testing are low-stakes in-training exams and asking audience questions during a workshop or lecture.⁴⁹

3. Summative testing: Summative assessments are created to measure the level of proficiency developed by a learner and compare it against some standard or benchmark. This form of assessment establishes the extent to which educational objectives have been met. The most common example is an end-of-term examination.

Online ECG examination has been successfully used to provide methods of testing. They are easy to distribute, highly convenient for learners, and allow the display of high-quality graphics. They can also be graded electronically, thereby minimizing the resources required to administer and grade exams.^36,50

We recommend using a combination of assessment formats to ensure the optimal evaluation of learner skill and to focus learning on areas of weakness. Summative assessments are highly valuable to ensure learners acquired the necessary ECG interpretation competencies. Remediation strategies should be available to provide additional practice to learners who do not meet competencies expected at their level of training.

The Need for ECG Interpretation Competencies and Milestones

Since the introduction of ECG in the late 1800s, there continues to be a significant variation in ECG interpretation skills among trainees and medical professionals.^4,6-12 Concerns continue to exist about the rate of missed diagnoses involving critical ECGs, leading to inappropriate patient management decisions. Despite the obvious need, teaching ECG interpretation is given little emphasis in medical education, and the curriculum remains quite disorganized. In this position paper, we call for a more structured ECG interpretation curriculum in medical education and hope to assist this process by assigning ECG patterns to 2 milestones in training: graduating medical students and first year postgraduate medical residents.

Defining competencies would help medical education programs to focus resources on teaching clinically important conditions for the appropriate level of training. We divide ECG findings into 4 categories (classes A to D), and we place emphasis on learning electrocardiographic emergencies early in training and spending less time on ECG findings that are unlikely to change patient management.

The goal is to ensure 100% recognition of class A (electrocardiographic emergencies) by the end of medical school. To ensure each medical education program fulfils this goal, a structured curriculum including a summative assessment is required.

Methods of Teaching

Various instructional mediums have been successfully implemented to teach ECG interpretation competencies, including lectures, puzzles, web-based programs, iBooks, and YouTube.^{34-41-44,47,48.51-53} A survey of clerkship directors in internal medicine revealed that 75% of clerkship programs teach ECG interpretation in a classroom lecture-based setting, 44% use teaching rounds, and only 17% utilize online/web-based instruction.³ Canadian family medicine programs have a relatively equal distribution between classroom-based, computer-based, and bedside teaching.⁵

In comparing the efficacy of instructional styles, several small comparative studies favor an electronic teaching format because of the enhanced learner interaction and visual learning, but there does not appear to be a consistently proven large advantage of 1 teaching format over another.^43,48,51,54 The overall theme emerging from this literature is the importance of repetition and active engagement in ECG interpretation, which appear to be more important than 1 particular strategy.²² Computer-based training appears to deliver these 2 qualities, unlike the traditional lecture-style passive learning model. The concept of repetition and engagement is also well supported in medical education literature outside ECG interpretation.^55,56

Given these data, we recommend that each medical education program select teaching methods based on their available resources, as long as adequate teaching time is allotted to ensure that trainees acquire the competencies defined in this publication.

Assessment Methods

It appears that the larger factor in determining ECG interpretation performance is not the learning format, but the form of assessment. Two studies have demonstrated that summative assessment substantially improves ECG interpretation performance when compared with formative assessment; in fact, this effect was so large that it overshadowed any small difference in teaching formats.^57,58 This concept aligns with medical education literature, which acknowledges that assessment drives learning by raising the stakes, thereby boosting student effort and encouraging learning to an effect much larger than can be generated by any particular learning style.^57,59 Nevertheless, well-designed formative assessment can focus students on effective learning by identifying gaps and important information.⁶⁰ Only 33% of Canadian family medicine residency programs and 71% of American clerkship programs have formal assessment of ECG interpretation skills.^3,5 There is no doubt that assessment, both formative and summative, should be implemented in all undergraduate and postgraduate medical training programs. Online assessment methods have the advantage of delivering high-quality images and a variety of question formats; hence, their use should be encouraged.^36,50,61-63

Teaching Personnel and Timing of Training

Who should teach ECG interpretation and when should this teaching take place? ECG interpretation in training programs is typically taught by attending physicians in each respective field. However, given that there is a large ECG interpretation error rate by noncardiologist physicians, we advise that ECG training content be created with input from own-specialty attending physicians and cardiologists.⁴ This teaching should take place early in medical school at the time medical students learn pathophysiology of the heart and should continue throughout training. Longitudinal training is preferred to block-based training because of improved resident satisfaction, but medical education literature did not reveal a difference in student performance with either strategy.^64-66

Despite its immense clinical value, there continues to be a lack of a comprehensive ECG interpretation curriculum in medical education programs. The goal of this position paper is to encourage the development of organized curricula in undergraduate and postgraduate medical education programs, and to ensure the acquisition of level-appropriate ECG interpretation skills while maintaining patient safety. We assist this process by grouping ECG findings into 4 classes (A to D) based on the frequency of encounter and emergent nature and by assigning them to each level of training. Methods of teaching ECG interpretation are less important and can be selected based on the available resources of each education program and student preference; however, online learning is encouraged. We also recommend that summative trainee evaluation methods be implemented in all programs to ensure that appropriate competencies are acquired and to further encourage self-directed learning. Resources should be allocated to ensure that every trainee is reaching their training milestones and should ensure that no electrocardiographic emergency (class A condition) is ever missed by a trainee. We hope that these guidelines will inform medical education systems and help prevent adverse patient outcomes caused by the misinterpretation of this valuable clinical diagnostic tool.

Disclosure

On behalf of all authors, the corresponding author states that there is no conflict of interest. This manuscript did not utilize any sources of funding.

Slightly more than 41% of health care personnel who had the flu during the 2014-2015 influenza season went to work while they were ill, according to an annual survey.

Physicians, however, were well above this average, with 63% reporting they had worked with an influenza-like illness (ILI); they were not quite as far above average as pharmacists, though, who had a 67% rate of “presenteeism” – the highest among all of the health care occupations included in the survey, said Sophia Chiu, MD, MPH, of the Centers for Disease Control and Prevention’s National Institute for Occupational Safety and Health, and her associates.

[email protected]

Slightly more than 41% of health care personnel who had the flu during the 2014-2015 influenza season went to work while they were ill, according to an annual survey.

Physicians, however, were well above this average, with 63% reporting they had worked with an influenza-like illness (ILI); they were not quite as far above average as pharmacists, though, who had a 67% rate of “presenteeism” – the highest among all of the health care occupations included in the survey, said Sophia Chiu, MD, MPH, of the Centers for Disease Control and Prevention’s National Institute for Occupational Safety and Health, and her associates.

[email protected]

Slightly more than 41% of health care personnel who had the flu during the 2014-2015 influenza season went to work while they were ill, according to an annual survey.

Physicians, however, were well above this average, with 63% reporting they had worked with an influenza-like illness (ILI); they were not quite as far above average as pharmacists, though, who had a 67% rate of “presenteeism” – the highest among all of the health care occupations included in the survey, said Sophia Chiu, MD, MPH, of the Centers for Disease Control and Prevention’s National Institute for Occupational Safety and Health, and her associates.

[email protected]

WASHINGTON – The Medicare Payment Advisory Commission continues to mull the specifics of its proposed recommendation to scrap the Quality Payment Program’s MIPS component.

The basics of the MIPS (Merit-based Incentive Payment System) replacement have not changed. The proposal calls for creation of a voluntary value program (VVP) that would withhold a percentage – currently 2% – of Medicare payments for physicians who are not part of an advanced alternative payment model (APM) under the Quality Payment Program of the Medicare Access and CHIP Reauthorization Act of 2015.

There would be two ways to recapture the withheld pay. The first would be to join an APM. The second would be to participate in a VVP by entering a voluntary reporting group. Under the proposal, VVPs would be at least 10 providers who would report together on population-based measures, patient experience, and cost measures, according to staff presentations given Nov. 2 at a meeting of the Medicare Payment Advisory Commission (MedPAC).

Proposed measures would be patient oriented, would encourage coordination across all providers, would promote positive change in the delivery system, and would be less burdensome to providers. Measures would be more in line with those employed by APMs – important because the overall goal would be encouraging participation in an APM rather than permanently lingering in the VVP.

To that end, MedPAC staff member Kate Bloniarz noted during the presentation of the VVP proposal that the total payments in the program “should be capped to be less attractive than joining an [advanced] APM. This comes from a general sense among commissioners that clinicians should not be able to receive large bonuses” for remaining in Medicare fee-for-service.

MedPAC staff recommended that the Centers for Medicare & Medicaid Services offer a fallback group that would provide an option to providers that would otherwise not have access to other groups to join.

Commission member Kathy Buto, former vice president of global health policy at Johnson & Johnson, suggested withholding be increased to perhaps 3%, with providers able to recoup 2% in the VVP and 3% in an APM, to further incentivize APM participation.

Staff noted that certain quality measures and process measures would be lost if MIPS were to go away, but they could be accounted for in other channels, such as through electronic health records and registries.

Most commissioners expressed support for both the repeal of MIPS and the conceptual framework for the new VVP, although many sought more details, particularly in the handling of specialists.

“I don’t know that we want to try to make the VVP do too much, especially when you get into the specialties that are very, very episodic,” said commission member Brian DeBusk, PhD, CEO of DeRoyal Industries. “The classic example would be a joint replacement. … I hope to see some specialist APMs developed in parallel and I think that is going to take some pressure off to try to make the VVP be all things to all people.”

Commission member Pat Wang, CEO of Healthfirst, offered a possible solution.

“I would suggest that we try to think about doing that in the context of something that is a little bit, perhaps, not full bore APM, but a VVP for specialists with their own metrics that are not big, gigantic readmissions,” she said. “Those are very broad population health metrics [that may not work for specialists].”

But at least two commission members voiced their dissent to the proposal as presented, with one going so far as to saying that MIPS should not be repealed.

David Nerenz, PhD, of the Henry Ford Health System, said that he had “very serious concerns about the VVP part of this proposal [and] they are such that if it comes to us as a recommendation in more or less its current form I will not support it.”

He called it “pretty significant social engineering in the structure of medical practice and I think we are doing it in the absence of what to me would be compelling evidence that this large group structure we are talking about is good.

“I also don’t see any evidence that beneficiaries find value in the set of measures we are talking about,” Dr. Nerenz added. He is on the record as supporting the repeal of MIPS.

Commission member Alice Coombs, MD, of Weymouth, Mass., was the lone voice speaking in support of MIPS: “I think MIPS has a lot of problems … but there are some things that are coming out of MIPS that are actually good.”

She called the VVP proposal “inadequate” and took issue with the measures. As a practicing physician, she said that she favors more population health measures that will affect patient outcomes.

MedPAC staff expect to have a draft recommendation prepared for discussion at the December meeting, with a final vote on what will be presented to Congress coming as soon as January.

[email protected]

WASHINGTON – The Medicare Payment Advisory Commission continues to mull the specifics of its proposed recommendation to scrap the Quality Payment Program’s MIPS component.

The basics of the MIPS (Merit-based Incentive Payment System) replacement have not changed. The proposal calls for creation of a voluntary value program (VVP) that would withhold a percentage – currently 2% – of Medicare payments for physicians who are not part of an advanced alternative payment model (APM) under the Quality Payment Program of the Medicare Access and CHIP Reauthorization Act of 2015.

There would be two ways to recapture the withheld pay. The first would be to join an APM. The second would be to participate in a VVP by entering a voluntary reporting group. Under the proposal, VVPs would be at least 10 providers who would report together on population-based measures, patient experience, and cost measures, according to staff presentations given Nov. 2 at a meeting of the Medicare Payment Advisory Commission (MedPAC).

Proposed measures would be patient oriented, would encourage coordination across all providers, would promote positive change in the delivery system, and would be less burdensome to providers. Measures would be more in line with those employed by APMs – important because the overall goal would be encouraging participation in an APM rather than permanently lingering in the VVP.

To that end, MedPAC staff member Kate Bloniarz noted during the presentation of the VVP proposal that the total payments in the program “should be capped to be less attractive than joining an [advanced] APM. This comes from a general sense among commissioners that clinicians should not be able to receive large bonuses” for remaining in Medicare fee-for-service.

MedPAC staff recommended that the Centers for Medicare & Medicaid Services offer a fallback group that would provide an option to providers that would otherwise not have access to other groups to join.

Commission member Kathy Buto, former vice president of global health policy at Johnson & Johnson, suggested withholding be increased to perhaps 3%, with providers able to recoup 2% in the VVP and 3% in an APM, to further incentivize APM participation.

Staff noted that certain quality measures and process measures would be lost if MIPS were to go away, but they could be accounted for in other channels, such as through electronic health records and registries.

Most commissioners expressed support for both the repeal of MIPS and the conceptual framework for the new VVP, although many sought more details, particularly in the handling of specialists.

“I don’t know that we want to try to make the VVP do too much, especially when you get into the specialties that are very, very episodic,” said commission member Brian DeBusk, PhD, CEO of DeRoyal Industries. “The classic example would be a joint replacement. … I hope to see some specialist APMs developed in parallel and I think that is going to take some pressure off to try to make the VVP be all things to all people.”

Commission member Pat Wang, CEO of Healthfirst, offered a possible solution.

“I would suggest that we try to think about doing that in the context of something that is a little bit, perhaps, not full bore APM, but a VVP for specialists with their own metrics that are not big, gigantic readmissions,” she said. “Those are very broad population health metrics [that may not work for specialists].”

But at least two commission members voiced their dissent to the proposal as presented, with one going so far as to saying that MIPS should not be repealed.

David Nerenz, PhD, of the Henry Ford Health System, said that he had “very serious concerns about the VVP part of this proposal [and] they are such that if it comes to us as a recommendation in more or less its current form I will not support it.”

He called it “pretty significant social engineering in the structure of medical practice and I think we are doing it in the absence of what to me would be compelling evidence that this large group structure we are talking about is good.

“I also don’t see any evidence that beneficiaries find value in the set of measures we are talking about,” Dr. Nerenz added. He is on the record as supporting the repeal of MIPS.

Commission member Alice Coombs, MD, of Weymouth, Mass., was the lone voice speaking in support of MIPS: “I think MIPS has a lot of problems … but there are some things that are coming out of MIPS that are actually good.”

She called the VVP proposal “inadequate” and took issue with the measures. As a practicing physician, she said that she favors more population health measures that will affect patient outcomes.

MedPAC staff expect to have a draft recommendation prepared for discussion at the December meeting, with a final vote on what will be presented to Congress coming as soon as January.

[email protected]

WASHINGTON – The Medicare Payment Advisory Commission continues to mull the specifics of its proposed recommendation to scrap the Quality Payment Program’s MIPS component.

The basics of the MIPS (Merit-based Incentive Payment System) replacement have not changed. The proposal calls for creation of a voluntary value program (VVP) that would withhold a percentage – currently 2% – of Medicare payments for physicians who are not part of an advanced alternative payment model (APM) under the Quality Payment Program of the Medicare Access and CHIP Reauthorization Act of 2015.

There would be two ways to recapture the withheld pay. The first would be to join an APM. The second would be to participate in a VVP by entering a voluntary reporting group. Under the proposal, VVPs would be at least 10 providers who would report together on population-based measures, patient experience, and cost measures, according to staff presentations given Nov. 2 at a meeting of the Medicare Payment Advisory Commission (MedPAC).

Proposed measures would be patient oriented, would encourage coordination across all providers, would promote positive change in the delivery system, and would be less burdensome to providers. Measures would be more in line with those employed by APMs – important because the overall goal would be encouraging participation in an APM rather than permanently lingering in the VVP.

To that end, MedPAC staff member Kate Bloniarz noted during the presentation of the VVP proposal that the total payments in the program “should be capped to be less attractive than joining an [advanced] APM. This comes from a general sense among commissioners that clinicians should not be able to receive large bonuses” for remaining in Medicare fee-for-service.

MedPAC staff recommended that the Centers for Medicare & Medicaid Services offer a fallback group that would provide an option to providers that would otherwise not have access to other groups to join.

Commission member Kathy Buto, former vice president of global health policy at Johnson & Johnson, suggested withholding be increased to perhaps 3%, with providers able to recoup 2% in the VVP and 3% in an APM, to further incentivize APM participation.

Staff noted that certain quality measures and process measures would be lost if MIPS were to go away, but they could be accounted for in other channels, such as through electronic health records and registries.

Most commissioners expressed support for both the repeal of MIPS and the conceptual framework for the new VVP, although many sought more details, particularly in the handling of specialists.

“I don’t know that we want to try to make the VVP do too much, especially when you get into the specialties that are very, very episodic,” said commission member Brian DeBusk, PhD, CEO of DeRoyal Industries. “The classic example would be a joint replacement. … I hope to see some specialist APMs developed in parallel and I think that is going to take some pressure off to try to make the VVP be all things to all people.”

Commission member Pat Wang, CEO of Healthfirst, offered a possible solution.

“I would suggest that we try to think about doing that in the context of something that is a little bit, perhaps, not full bore APM, but a VVP for specialists with their own metrics that are not big, gigantic readmissions,” she said. “Those are very broad population health metrics [that may not work for specialists].”

But at least two commission members voiced their dissent to the proposal as presented, with one going so far as to saying that MIPS should not be repealed.

David Nerenz, PhD, of the Henry Ford Health System, said that he had “very serious concerns about the VVP part of this proposal [and] they are such that if it comes to us as a recommendation in more or less its current form I will not support it.”

He called it “pretty significant social engineering in the structure of medical practice and I think we are doing it in the absence of what to me would be compelling evidence that this large group structure we are talking about is good.

“I also don’t see any evidence that beneficiaries find value in the set of measures we are talking about,” Dr. Nerenz added. He is on the record as supporting the repeal of MIPS.

Commission member Alice Coombs, MD, of Weymouth, Mass., was the lone voice speaking in support of MIPS: “I think MIPS has a lot of problems … but there are some things that are coming out of MIPS that are actually good.”

She called the VVP proposal “inadequate” and took issue with the measures. As a practicing physician, she said that she favors more population health measures that will affect patient outcomes.

MedPAC staff expect to have a draft recommendation prepared for discussion at the December meeting, with a final vote on what will be presented to Congress coming as soon as January.

[email protected]

The Food and Drug Administration has approved frontline alectinib for the treatment of anaplastic lymphoma kinase (ALK)–positive metastatic non–small cell lung cancer (NSCLC) that has been detected by an FDA-approved test.

The drug previously received accelerated approval for treatment of patients with ALK-positive metastatic NSCLC whose disease progressed while receiving crizotinib or who were intolerant of that treatment.

Wikimedia Commons/FitzColinGerald/Creative Commons License

[email protected]

The Food and Drug Administration has approved frontline alectinib for the treatment of anaplastic lymphoma kinase (ALK)–positive metastatic non–small cell lung cancer (NSCLC) that has been detected by an FDA-approved test.

The drug previously received accelerated approval for treatment of patients with ALK-positive metastatic NSCLC whose disease progressed while receiving crizotinib or who were intolerant of that treatment.

Wikimedia Commons/FitzColinGerald/Creative Commons License

[email protected]

The Food and Drug Administration has approved frontline alectinib for the treatment of anaplastic lymphoma kinase (ALK)–positive metastatic non–small cell lung cancer (NSCLC) that has been detected by an FDA-approved test.

The drug previously received accelerated approval for treatment of patients with ALK-positive metastatic NSCLC whose disease progressed while receiving crizotinib or who were intolerant of that treatment.

Wikimedia Commons/FitzColinGerald/Creative Commons License

[email protected]

SCOTTSDALE, ARIZ. – Patients with hyperparathyroidism and single-gland disease can be considered cured if their intraoperative parathyroid hormone (PTH) level drops by 50% or more, or to normal or near-normal levels (15-65 pg/mL), and don’t require immediate follow-up for lab work, according to a retrospective review of patients who underwent parathyroidectomy at Mayo Clinic, Rochester, Minn.

These findings applied only to patients in the study whose preoperative sestamibi scans were concordant with intraoperative findings. Single-gland disease represented about 85% of cases, Melanie L. Lyden, MD, reported at the annual meeting of the Western Surgical Association.

SCOTTSDALE, ARIZ. – Patients with hyperparathyroidism and single-gland disease can be considered cured if their intraoperative parathyroid hormone (PTH) level drops by 50% or more, or to normal or near-normal levels (15-65 pg/mL), and don’t require immediate follow-up for lab work, according to a retrospective review of patients who underwent parathyroidectomy at Mayo Clinic, Rochester, Minn.

These findings applied only to patients in the study whose preoperative sestamibi scans were concordant with intraoperative findings. Single-gland disease represented about 85% of cases, Melanie L. Lyden, MD, reported at the annual meeting of the Western Surgical Association.

SCOTTSDALE, ARIZ. – Patients with hyperparathyroidism and single-gland disease can be considered cured if their intraoperative parathyroid hormone (PTH) level drops by 50% or more, or to normal or near-normal levels (15-65 pg/mL), and don’t require immediate follow-up for lab work, according to a retrospective review of patients who underwent parathyroidectomy at Mayo Clinic, Rochester, Minn.

These findings applied only to patients in the study whose preoperative sestamibi scans were concordant with intraoperative findings. Single-gland disease represented about 85% of cases, Melanie L. Lyden, MD, reported at the annual meeting of the Western Surgical Association.

Take-Home Points

• Shared decision-making tools, such as predictive models, can help empower the patient to make decisions for or against surgery equipped with more information about the expected outcome.
• There is a role for preoperative collection of PROMs in the clinical decision-making process.
• Mental health state, as reported by the VR-12 MCS, is a significant predictor of postoperative pain and function as reported by the VAS pain and ASES function scores.
• A significant portion of the predictive ability of this model comes from the fact that at 1-year postoperatively, patients receiving a rTSA will on average have a 3.8 point lower on ASES function score than those receiving a TSA (P < .001, ω²=.083).
• Future studies to discern the role of different modalities to improve a patient’s emotional health preoperatively will be beneficial as the healthcare industry trends toward value based medicine collecting PROMs as part of reimbursement schemes.

Over the past few decades, decisions regarding patients’ care have gradually transitioned from a paternalistic model to a more cooperative exchange between patient and physician. Shared decision-making provides patients a measure of autonomy in making choices for their health and their future. Patient participation may mitigate uncertainty and discomfort during selection of a course of treatment, which may lead to increased satisfaction levels after surgery.¹ Moreover, shared decision-making may help patients better manage postoperative expectations through evidenced-based discussions of preoperative health levels and their corresponding outcomes. Patient-reported outcome measures (PROMs) use clinically sensitive and specific metrics to evaluate a patient’s self-reported pain, functional ability, and mental state.² These metrics are useful in setting patient expectations for potential outcomes of treatment options. Use of evidence-based clinical decision-making tools, such as PROM-based predictive models, can facilitate a collaborative decision-making environment for patient and physician. Given the present cost-containment era, and the need for preoperative metrics that can assist in predictive analysis of postoperative improvement, models are clearly valuable.

In attempts to help patients set well-informed and reasonable expectations, physicians have turned to PROMs to facilitate preoperative evidence-based discussions. Although PROMs have been in use for almost 30 years, only recently have they been used to create tools that can aid quantitatively in the surgical decision-making process.^2-6 Combining physical examination findings, imaging studies, comorbidities, and quantitative tools, such as this model, may enhance patients’ understanding of their preoperative condition and expected prognosis and thereby guide their surgical decisions.

We conducted a study to determine whether certain preoperative PROMs can predict 1-year postoperative visual analog scale (VAS) pain scores and American Shoulder and Elbow Surgeons (ASES) Function scores in total shoulder arthroplasty (TSA) and reverse TSA (rTSA). We hypothesized that preoperative mental health status as captured by Veterans RAND 12-Item Health Survey (VR-12) mental health component summary (MCS) score and preoperative VAS pain score would predict both VAS pain score and ASES Function score 1 year after surgery. Specifically, we hypothesized that a higher preoperative VR-12 MCS score would predict less pain and better function 1 year after surgery and that a higher preoperative VAS pain score would predict more pain and worse function 1 year after surgery.

Methods

This study was approved by the Institutional Review Board of Partners Healthcare. The study used the Surgical Outcome System (Arthrex), a comprehensive prospective database that stores preoperative and 1-year postoperative patient demographics and TSA-PROM data. Surveys are emailed to all enrolled patients before surgery and 1 year after surgery. As indicated by the Institutional Review Boards of all participating institutions, patients in the Surgical Outcome System have to sign a consent form to permit use of their responses in research.

The database includes patient data from 42 orthopedic surgeons across the United States. All primary TSAs and primary rTSAs in the database were included in this study, regardless of arthroplasty indication. Revisions were excluded. Also excluded were cases in which the 1-year postoperative questionnaire was not completed. Of the 1681 patients eligible for 1-year follow-up, 1225 (73%) completed the 1-year postoperative questionnaire. PROMs used in the study were VAS pain score, ASES Function score, VR-12 MCS score, and Single Assessment Numerical Evaluation (SANE). Unfortunately, not all surgeons use every measure in the 1-year postoperative questionnaire set. Thus, in our complete models, total number of observations was 1004 for modeling 1-year postoperative VAS pain scores and 986 for modeling 1-year postoperative ASES Function scores. 

Metrics

On VAS, pain is rated from 0 (no pain) to 10 (pain as bad as it can be). Tashjian and colleagues⁷ estimated that the minimal clinically important difference (MCID) for postoperative VAS pain scores was 1.4 in a cohort of 326 patients who had TSA, rTSA, or shoulder hemiarthroplasty. ASES Function score is scaled from 0 to 30, with 30 representing best function.⁸ Wong and colleagues⁹ identified an MCID of 6.5 for ASES Function scores in a cohort of 107 patients who had TSA or rTSA. SANE ratings range from 0% to 100%, with 100% indicating the patient’s shoulder was totally “normal.”10 VR-12 MCS scores appear on a logarithmic scale, with higher numbers representing better mental health. The population mean estimate for VR-12 MCS scores is 50.1 (SD, 11.49; overall possible range, –2.47 to 76.1).11 Our patient population’s scores ranged from 12.5 to 73.8.

Statistical Analysis

Simple bivariate and multivariate linear regressions were performed to evaluate the predictive value of each of the outlined PROMs. Our complete model controls for patient sex, age, and type of arthroplasty. Categorical variables were dummy-coded. Both 1-year postoperative VAS pain score and 1-year postoperative ASES Function score were investigated as dependent variables. Regression coefficients and P and ω² values are reported. Omega square represents how much of the variance in an outcome variable a model explains, like R², and ω² values can also be calculated for individual factors to see how much variance a given factor accounts for. For a simple relative risk calculation, we divided our cohort into 3 equal-sized groups based on preoperative VR-12 MCS scores and compared the risk that patients with scores in the top third (better mental health) would end up below certain ASES Total scores with the risk of patients with scores in the bottom third (worse mental health). All statistical analyses were performed with Stata (StataCorp).

Results

Table 1 lists summary statistics for the population used in these models.

Predicting VAS Pain Score (Table 2)

Preoperative VAS pain score and VR-12 MCS score both predicted 1-year postoperative VAS pain score (P < .001). Preoperative ASES Function score did not predict pain 1 year after surgery. By contrast, higher preoperative VAS pain scores were associated with higher VAS pain scores 1 year after surgery. Higher preoperative VR-12 MCS scores were significantly associated with lower VAS pain scores 1 year after surgery, indicating that better preoperative mental health is significantly associated with better self-reported outcomes in terms of pain 1 year after surgery. These associations remained statistically significant when controlling for age at time of surgery, sex, and type of arthroplasty.

Preoperative VR-12 MCS score was more predictive of 1-year postoperative VAS pain score than preoperative VAS pain score was. In other words, preoperative VR-12 MCS score accounted for more variability in outcome for 1-year postoperative VAS pain score (2.4%; ω² = .023) than preoperative VAS pain score did (1.6%; ω² = .015). 

Predicting ASES Function Score (Table 3)

By contrast, preoperative VAS pain score did not predict 1-year postoperative ASES Function score. Preoperative ASES Function and VR-12 MCS scores both predicted 1-year postoperative ASES Function score (P < .001). Higher preoperative ASES Function scores were associated with higher 1-year postoperative ASES Function scores. In other words, reporting better shoulder function before surgery was associated with reporting better shoulder function after surgery.

An example gives a sense of the effect size associated with the coefficient for preoperative ASES Function score. Our model predicts that, compared with a patient who reports 5 points lower on preoperative ASES Function (which ranges from 0-30), a patient who reports 5 points higher will report on average about 1 point higher on 1-year postoperative ASES Function. As in the model for postoperative pain, these associations with preoperative function and mental health scores held when controlling for age, sex, and type of arthroplasty. 

As in the postoperative pain model, preoperative VR-12 MCS score was more predictive of 1-year postoperative ASES Function score than preoperative ASES Function score was. Preoperative VR-12 MCS score accounted for more of the variation that our model predicts (ω² = .029) than preoperative ASES Function score did (ω² = .020). We compared the risk that patients with high preoperative VR-12 MCS scores (top third of cohort) would end up with ASES Total scores below 70, below 80, or below 90 with the risk of patients with low preoperative VR-12 MCS scores (bottom third). Results appear in Table 4.

A significant part of the predictive ability of our model for postoperative ASES Function scores stems from the fact that a patient who undergoes rTSA (vs TSA) is predicted to have an ASES Function score 3.8 points lower 1 year after surgery (P < .001, ω² = .083). With type of arthroplasty controlled for, female sex is associated with an ASES Function score 1.6 points lower 1 year after surgery (P < .001, ω² = .016).

Preoperative SANE score did not predict 1-year postoperative VAS pain score or ASES Function score. In addition, when our complete model was run with 1-year postoperative SANE score as the dependent variable, preoperative SANE score did not predict 1-year postoperative SANE score. Our data provide no supporting evidence for the use of SANE scores for predictive modeling for shoulder arthroplasty.

Discussion

We prospectively gathered data to determine which factors would predict patient subjective outcomes of primary TSA and primary rTSA. We hypothesized that preoperative VR-12 MCS scores and preoperative VAS pain scores would predict postoperative pain and function as measured with those PROMs. Second, we hypothesized that better preoperative mental health (as measured with VR-12 MCS scores) would predict lower postoperative pain (VAS pain scores) and better postoperative function (ASES Function scores). Third, we hypothesized that higher preoperative pain (VAS pain scores) would correlate with higher postoperative pain (VAS pain scores) and worse postoperative function (ASES Function scores).

Our main goal is to provide patients and surgeons with a predictive model that generates insights into what patients can expect after surgery. This model is not intended to be a screening tool for operative indications, but a clinical tool for helping set expectations.

Our results showed that patients with more pain before surgery were more likely to have more pain 1 year after surgery—confirming the hypothesized relationship between pain before and after surgery. Contrary to the hypothesis, however, degree of pain before surgery was not associated with function 1 year after surgery. Our mental health hypothesis was confirmed: Patients with better preoperative mental health scores had on average less pain and better function 1 year after surgery. Not surprisingly, our model demonstrated that patients with better self-reported function before surgery had better self-reported function after surgery. Patient-reported function before surgery did not significantly affect how much pain the patient had 1 year after surgery. Although we did not hypothesize about the role of function in predicting 1-year outcomes, function is a significant factor to be considered when setting patient expectations regarding shoulder arthroplasty outcomes (Table 5).

Although the effect sizes of each analyzed factor are small, together our models for 1-year postoperative pain and function provide significant insight into patients’ likely outcomes 1 year after TSA and rTSA.

Many studies have found that mental health correlated with pain and function during recovery from orthopedic trauma.^12-18 For example, Wylie and colleagues¹⁹ found that preoperative mental health, as measured with the 36-Item Short Form Health Survey (SF-36) MCS score, predicted patient-reported pain and function in the setting of rotator cuff injury, regardless of treatment type (operative, nonoperative). Others have found that mental health may play a role in how patients report their pain and function on various PROMs.^20,21 Modalities for improving patients’ emotional health baseline may even become a preoperative requirement as the healthcare industry moves toward value-based medicine and collection of patient-related outcomes as part of reimbursement schemes. 

By contrast, some studies have found that preoperative mental health did not predict postoperative outcomes. For example, Kennedy and colleagues²² found that preoperative mental health (as measured with SF-36 MCS scores) did not predict functional outcome in patients with ankle arthritis treated with ankle arthroplasty or arthrodesis. Likewise, Styron and colleagues²³ found no correlation between preoperative mental health (SF-12 MCS scores) and postoperative mental health and function in TSA. Their findings contradict those of the present study and many other studies.^12-18 The contradiction in findings demonstrates the need for well-designed, sufficiently powered studies of the link between preoperative mental health and postoperative outcome. Our study, with its large sample and heterogeneous population, is a start.

Two other groups (Simmen and colleagues,¹⁸ Matsen and colleagues²⁴) have attempted to develop a model predicting outcomes of shoulder arthroplasty. Simmen and colleagues¹⁸ estimated the probability of “treatment success” 1 year after TSA. Their model had 4 factors predictive of patient outcomes. Previous shoulder surgery and age over 75 years were significantly associated with lower probability of success, whereas higher preoperative SF-36 MCS scores and higher preoperative DASH (Disabilities of the Arm, Shoulder, and Hand) Function scores were associated with higher probability of success. The authors deemed TSA successful if the patient achieved a Constant score of ≥80 out of 100. Their model predicts probability of TSA “success,” whereas our models predict particular outcome scores. Both their results and ours support the hypothesis that preoperative mental health and function scores can predict how well a patient fares after surgery. Simmen and colleagues18 based their model on a cohort of only 140 patients and reported a 33.6% success rate (47/140 surgeries).

Matsen and colleagues²⁴ used a 1-practice cohort of 337 patients who underwent different types of arthroplasties, including TSA, rTSA, hemiarthroplasty, and ream-and-run arthroplasty. Although their focus was not preoperative PROMs predicting postoperative PROMs, they used the Simple Shoulder Test (SST) baseline score as a predictive variable. They found that 6 baseline characteristics—American Society of Anesthesiologists class I, shoulder problem unrelated to work, no prior shoulder surgery, glenoid type other than A1, humeral head not superiorly displaced on anteroposterior radiograph, and lower baseline SST score—were statistically associated with better outcomes, and they developed a model driven by these characteristics. They urged other investigators to perform the same kind of analysis with larger patient populations from multiple practices. One of the strengths of our study is its large patient population. We collected data on 1004 patients for modeling 1-year postoperative VAS pain scores and 986 patients for modeling 1-year postoperative ASES Function scores.

Our study had several limitations. First, its data came from a 42-surgeon database, and there may be variations in how these surgeons enroll patients in the registry. If some surgeons did not enroll all their surgical patients, our sample could have been subject to selection bias. Second, in developing our model, we used only patient characteristics that were available in the database. On the other hand, the heterogeneity of the surgeon sample lended external validity to the model. A third limitation was that the model always predicts better pain and function outcomes after TSA than after rTSA. In other words, it does not consider whether TSA is appropriate for a particular patient. Instead, it predicts 1-year shoulder arthroplasty outcomes. 

Our goal here is not to provide outcomes information or a surgical screening tool, but to report on our use of a simple data-driven tool for setting expectations. When appropriate data become available, tools like this should be expanded to predict longer-term shoulder arthroplasty outcomes. We need more studies that combine preoperative PROMs, more baseline clinical and patient characteristics (following the Matsen and colleagues²⁴ model), and large sample sizes.

Conclusion

The educational models presented here can help patients and surgeons learn what to expect after surgery. These models reveal the value in collecting preoperative subjective PROMs and show how a quantitative tool can help facilitate shared decision-making and set patient expectations. Separately, the effect size of each factor is small, but together a patient’s preoperative VAS pain score, ASES Function score, VR-12 MCS score, age, sex, and type of arthroplasty can provide information predictive of the patient’s self-reported pain and function 1 year after surgery.

Take-Home Points

• Shared decision-making tools, such as predictive models, can help empower the patient to make decisions for or against surgery equipped with more information about the expected outcome.
• There is a role for preoperative collection of PROMs in the clinical decision-making process.
• Mental health state, as reported by the VR-12 MCS, is a significant predictor of postoperative pain and function as reported by the VAS pain and ASES function scores.
• A significant portion of the predictive ability of this model comes from the fact that at 1-year postoperatively, patients receiving a rTSA will on average have a 3.8 point lower on ASES function score than those receiving a TSA (P < .001, ω²=.083).
• Future studies to discern the role of different modalities to improve a patient’s emotional health preoperatively will be beneficial as the healthcare industry trends toward value based medicine collecting PROMs as part of reimbursement schemes.

Over the past few decades, decisions regarding patients’ care have gradually transitioned from a paternalistic model to a more cooperative exchange between patient and physician. Shared decision-making provides patients a measure of autonomy in making choices for their health and their future. Patient participation may mitigate uncertainty and discomfort during selection of a course of treatment, which may lead to increased satisfaction levels after surgery.¹ Moreover, shared decision-making may help patients better manage postoperative expectations through evidenced-based discussions of preoperative health levels and their corresponding outcomes. Patient-reported outcome measures (PROMs) use clinically sensitive and specific metrics to evaluate a patient’s self-reported pain, functional ability, and mental state.² These metrics are useful in setting patient expectations for potential outcomes of treatment options. Use of evidence-based clinical decision-making tools, such as PROM-based predictive models, can facilitate a collaborative decision-making environment for patient and physician. Given the present cost-containment era, and the need for preoperative metrics that can assist in predictive analysis of postoperative improvement, models are clearly valuable.

In attempts to help patients set well-informed and reasonable expectations, physicians have turned to PROMs to facilitate preoperative evidence-based discussions. Although PROMs have been in use for almost 30 years, only recently have they been used to create tools that can aid quantitatively in the surgical decision-making process.^2-6 Combining physical examination findings, imaging studies, comorbidities, and quantitative tools, such as this model, may enhance patients’ understanding of their preoperative condition and expected prognosis and thereby guide their surgical decisions.

We conducted a study to determine whether certain preoperative PROMs can predict 1-year postoperative visual analog scale (VAS) pain scores and American Shoulder and Elbow Surgeons (ASES) Function scores in total shoulder arthroplasty (TSA) and reverse TSA (rTSA). We hypothesized that preoperative mental health status as captured by Veterans RAND 12-Item Health Survey (VR-12) mental health component summary (MCS) score and preoperative VAS pain score would predict both VAS pain score and ASES Function score 1 year after surgery. Specifically, we hypothesized that a higher preoperative VR-12 MCS score would predict less pain and better function 1 year after surgery and that a higher preoperative VAS pain score would predict more pain and worse function 1 year after surgery.

Methods

This study was approved by the Institutional Review Board of Partners Healthcare. The study used the Surgical Outcome System (Arthrex), a comprehensive prospective database that stores preoperative and 1-year postoperative patient demographics and TSA-PROM data. Surveys are emailed to all enrolled patients before surgery and 1 year after surgery. As indicated by the Institutional Review Boards of all participating institutions, patients in the Surgical Outcome System have to sign a consent form to permit use of their responses in research.

The database includes patient data from 42 orthopedic surgeons across the United States. All primary TSAs and primary rTSAs in the database were included in this study, regardless of arthroplasty indication. Revisions were excluded. Also excluded were cases in which the 1-year postoperative questionnaire was not completed. Of the 1681 patients eligible for 1-year follow-up, 1225 (73%) completed the 1-year postoperative questionnaire. PROMs used in the study were VAS pain score, ASES Function score, VR-12 MCS score, and Single Assessment Numerical Evaluation (SANE). Unfortunately, not all surgeons use every measure in the 1-year postoperative questionnaire set. Thus, in our complete models, total number of observations was 1004 for modeling 1-year postoperative VAS pain scores and 986 for modeling 1-year postoperative ASES Function scores. 

Metrics

On VAS, pain is rated from 0 (no pain) to 10 (pain as bad as it can be). Tashjian and colleagues⁷ estimated that the minimal clinically important difference (MCID) for postoperative VAS pain scores was 1.4 in a cohort of 326 patients who had TSA, rTSA, or shoulder hemiarthroplasty. ASES Function score is scaled from 0 to 30, with 30 representing best function.⁸ Wong and colleagues⁹ identified an MCID of 6.5 for ASES Function scores in a cohort of 107 patients who had TSA or rTSA. SANE ratings range from 0% to 100%, with 100% indicating the patient’s shoulder was totally “normal.”10 VR-12 MCS scores appear on a logarithmic scale, with higher numbers representing better mental health. The population mean estimate for VR-12 MCS scores is 50.1 (SD, 11.49; overall possible range, –2.47 to 76.1).11 Our patient population’s scores ranged from 12.5 to 73.8.

Statistical Analysis

Simple bivariate and multivariate linear regressions were performed to evaluate the predictive value of each of the outlined PROMs. Our complete model controls for patient sex, age, and type of arthroplasty. Categorical variables were dummy-coded. Both 1-year postoperative VAS pain score and 1-year postoperative ASES Function score were investigated as dependent variables. Regression coefficients and P and ω² values are reported. Omega square represents how much of the variance in an outcome variable a model explains, like R², and ω² values can also be calculated for individual factors to see how much variance a given factor accounts for. For a simple relative risk calculation, we divided our cohort into 3 equal-sized groups based on preoperative VR-12 MCS scores and compared the risk that patients with scores in the top third (better mental health) would end up below certain ASES Total scores with the risk of patients with scores in the bottom third (worse mental health). All statistical analyses were performed with Stata (StataCorp).

Results

Table 1 lists summary statistics for the population used in these models.

Predicting VAS Pain Score (Table 2)

Preoperative VAS pain score and VR-12 MCS score both predicted 1-year postoperative VAS pain score (P < .001). Preoperative ASES Function score did not predict pain 1 year after surgery. By contrast, higher preoperative VAS pain scores were associated with higher VAS pain scores 1 year after surgery. Higher preoperative VR-12 MCS scores were significantly associated with lower VAS pain scores 1 year after surgery, indicating that better preoperative mental health is significantly associated with better self-reported outcomes in terms of pain 1 year after surgery. These associations remained statistically significant when controlling for age at time of surgery, sex, and type of arthroplasty.

Preoperative VR-12 MCS score was more predictive of 1-year postoperative VAS pain score than preoperative VAS pain score was. In other words, preoperative VR-12 MCS score accounted for more variability in outcome for 1-year postoperative VAS pain score (2.4%; ω² = .023) than preoperative VAS pain score did (1.6%; ω² = .015). 

Predicting ASES Function Score (Table 3)

By contrast, preoperative VAS pain score did not predict 1-year postoperative ASES Function score. Preoperative ASES Function and VR-12 MCS scores both predicted 1-year postoperative ASES Function score (P < .001). Higher preoperative ASES Function scores were associated with higher 1-year postoperative ASES Function scores. In other words, reporting better shoulder function before surgery was associated with reporting better shoulder function after surgery.

An example gives a sense of the effect size associated with the coefficient for preoperative ASES Function score. Our model predicts that, compared with a patient who reports 5 points lower on preoperative ASES Function (which ranges from 0-30), a patient who reports 5 points higher will report on average about 1 point higher on 1-year postoperative ASES Function. As in the model for postoperative pain, these associations with preoperative function and mental health scores held when controlling for age, sex, and type of arthroplasty. 

As in the postoperative pain model, preoperative VR-12 MCS score was more predictive of 1-year postoperative ASES Function score than preoperative ASES Function score was. Preoperative VR-12 MCS score accounted for more of the variation that our model predicts (ω² = .029) than preoperative ASES Function score did (ω² = .020). We compared the risk that patients with high preoperative VR-12 MCS scores (top third of cohort) would end up with ASES Total scores below 70, below 80, or below 90 with the risk of patients with low preoperative VR-12 MCS scores (bottom third). Results appear in Table 4.

A significant part of the predictive ability of our model for postoperative ASES Function scores stems from the fact that a patient who undergoes rTSA (vs TSA) is predicted to have an ASES Function score 3.8 points lower 1 year after surgery (P < .001, ω² = .083). With type of arthroplasty controlled for, female sex is associated with an ASES Function score 1.6 points lower 1 year after surgery (P < .001, ω² = .016).

Preoperative SANE score did not predict 1-year postoperative VAS pain score or ASES Function score. In addition, when our complete model was run with 1-year postoperative SANE score as the dependent variable, preoperative SANE score did not predict 1-year postoperative SANE score. Our data provide no supporting evidence for the use of SANE scores for predictive modeling for shoulder arthroplasty.

Discussion

We prospectively gathered data to determine which factors would predict patient subjective outcomes of primary TSA and primary rTSA. We hypothesized that preoperative VR-12 MCS scores and preoperative VAS pain scores would predict postoperative pain and function as measured with those PROMs. Second, we hypothesized that better preoperative mental health (as measured with VR-12 MCS scores) would predict lower postoperative pain (VAS pain scores) and better postoperative function (ASES Function scores). Third, we hypothesized that higher preoperative pain (VAS pain scores) would correlate with higher postoperative pain (VAS pain scores) and worse postoperative function (ASES Function scores).

Our main goal is to provide patients and surgeons with a predictive model that generates insights into what patients can expect after surgery. This model is not intended to be a screening tool for operative indications, but a clinical tool for helping set expectations.

Our results showed that patients with more pain before surgery were more likely to have more pain 1 year after surgery—confirming the hypothesized relationship between pain before and after surgery. Contrary to the hypothesis, however, degree of pain before surgery was not associated with function 1 year after surgery. Our mental health hypothesis was confirmed: Patients with better preoperative mental health scores had on average less pain and better function 1 year after surgery. Not surprisingly, our model demonstrated that patients with better self-reported function before surgery had better self-reported function after surgery. Patient-reported function before surgery did not significantly affect how much pain the patient had 1 year after surgery. Although we did not hypothesize about the role of function in predicting 1-year outcomes, function is a significant factor to be considered when setting patient expectations regarding shoulder arthroplasty outcomes (Table 5).

Although the effect sizes of each analyzed factor are small, together our models for 1-year postoperative pain and function provide significant insight into patients’ likely outcomes 1 year after TSA and rTSA.

Many studies have found that mental health correlated with pain and function during recovery from orthopedic trauma.^12-18 For example, Wylie and colleagues¹⁹ found that preoperative mental health, as measured with the 36-Item Short Form Health Survey (SF-36) MCS score, predicted patient-reported pain and function in the setting of rotator cuff injury, regardless of treatment type (operative, nonoperative). Others have found that mental health may play a role in how patients report their pain and function on various PROMs.^20,21 Modalities for improving patients’ emotional health baseline may even become a preoperative requirement as the healthcare industry moves toward value-based medicine and collection of patient-related outcomes as part of reimbursement schemes. 

By contrast, some studies have found that preoperative mental health did not predict postoperative outcomes. For example, Kennedy and colleagues²² found that preoperative mental health (as measured with SF-36 MCS scores) did not predict functional outcome in patients with ankle arthritis treated with ankle arthroplasty or arthrodesis. Likewise, Styron and colleagues²³ found no correlation between preoperative mental health (SF-12 MCS scores) and postoperative mental health and function in TSA. Their findings contradict those of the present study and many other studies.^12-18 The contradiction in findings demonstrates the need for well-designed, sufficiently powered studies of the link between preoperative mental health and postoperative outcome. Our study, with its large sample and heterogeneous population, is a start.

Two other groups (Simmen and colleagues,¹⁸ Matsen and colleagues²⁴) have attempted to develop a model predicting outcomes of shoulder arthroplasty. Simmen and colleagues¹⁸ estimated the probability of “treatment success” 1 year after TSA. Their model had 4 factors predictive of patient outcomes. Previous shoulder surgery and age over 75 years were significantly associated with lower probability of success, whereas higher preoperative SF-36 MCS scores and higher preoperative DASH (Disabilities of the Arm, Shoulder, and Hand) Function scores were associated with higher probability of success. The authors deemed TSA successful if the patient achieved a Constant score of ≥80 out of 100. Their model predicts probability of TSA “success,” whereas our models predict particular outcome scores. Both their results and ours support the hypothesis that preoperative mental health and function scores can predict how well a patient fares after surgery. Simmen and colleagues18 based their model on a cohort of only 140 patients and reported a 33.6% success rate (47/140 surgeries).

Matsen and colleagues²⁴ used a 1-practice cohort of 337 patients who underwent different types of arthroplasties, including TSA, rTSA, hemiarthroplasty, and ream-and-run arthroplasty. Although their focus was not preoperative PROMs predicting postoperative PROMs, they used the Simple Shoulder Test (SST) baseline score as a predictive variable. They found that 6 baseline characteristics—American Society of Anesthesiologists class I, shoulder problem unrelated to work, no prior shoulder surgery, glenoid type other than A1, humeral head not superiorly displaced on anteroposterior radiograph, and lower baseline SST score—were statistically associated with better outcomes, and they developed a model driven by these characteristics. They urged other investigators to perform the same kind of analysis with larger patient populations from multiple practices. One of the strengths of our study is its large patient population. We collected data on 1004 patients for modeling 1-year postoperative VAS pain scores and 986 patients for modeling 1-year postoperative ASES Function scores.

Our study had several limitations. First, its data came from a 42-surgeon database, and there may be variations in how these surgeons enroll patients in the registry. If some surgeons did not enroll all their surgical patients, our sample could have been subject to selection bias. Second, in developing our model, we used only patient characteristics that were available in the database. On the other hand, the heterogeneity of the surgeon sample lended external validity to the model. A third limitation was that the model always predicts better pain and function outcomes after TSA than after rTSA. In other words, it does not consider whether TSA is appropriate for a particular patient. Instead, it predicts 1-year shoulder arthroplasty outcomes. 

Our goal here is not to provide outcomes information or a surgical screening tool, but to report on our use of a simple data-driven tool for setting expectations. When appropriate data become available, tools like this should be expanded to predict longer-term shoulder arthroplasty outcomes. We need more studies that combine preoperative PROMs, more baseline clinical and patient characteristics (following the Matsen and colleagues²⁴ model), and large sample sizes.

Conclusion

The educational models presented here can help patients and surgeons learn what to expect after surgery. These models reveal the value in collecting preoperative subjective PROMs and show how a quantitative tool can help facilitate shared decision-making and set patient expectations. Separately, the effect size of each factor is small, but together a patient’s preoperative VAS pain score, ASES Function score, VR-12 MCS score, age, sex, and type of arthroplasty can provide information predictive of the patient’s self-reported pain and function 1 year after surgery.

Take-Home Points

• Shared decision-making tools, such as predictive models, can help empower the patient to make decisions for or against surgery equipped with more information about the expected outcome.
• There is a role for preoperative collection of PROMs in the clinical decision-making process.
• Mental health state, as reported by the VR-12 MCS, is a significant predictor of postoperative pain and function as reported by the VAS pain and ASES function scores.
• A significant portion of the predictive ability of this model comes from the fact that at 1-year postoperatively, patients receiving a rTSA will on average have a 3.8 point lower on ASES function score than those receiving a TSA (P < .001, ω²=.083).
• Future studies to discern the role of different modalities to improve a patient’s emotional health preoperatively will be beneficial as the healthcare industry trends toward value based medicine collecting PROMs as part of reimbursement schemes.

Over the past few decades, decisions regarding patients’ care have gradually transitioned from a paternalistic model to a more cooperative exchange between patient and physician. Shared decision-making provides patients a measure of autonomy in making choices for their health and their future. Patient participation may mitigate uncertainty and discomfort during selection of a course of treatment, which may lead to increased satisfaction levels after surgery.¹ Moreover, shared decision-making may help patients better manage postoperative expectations through evidenced-based discussions of preoperative health levels and their corresponding outcomes. Patient-reported outcome measures (PROMs) use clinically sensitive and specific metrics to evaluate a patient’s self-reported pain, functional ability, and mental state.² These metrics are useful in setting patient expectations for potential outcomes of treatment options. Use of evidence-based clinical decision-making tools, such as PROM-based predictive models, can facilitate a collaborative decision-making environment for patient and physician. Given the present cost-containment era, and the need for preoperative metrics that can assist in predictive analysis of postoperative improvement, models are clearly valuable.

In attempts to help patients set well-informed and reasonable expectations, physicians have turned to PROMs to facilitate preoperative evidence-based discussions. Although PROMs have been in use for almost 30 years, only recently have they been used to create tools that can aid quantitatively in the surgical decision-making process.^2-6 Combining physical examination findings, imaging studies, comorbidities, and quantitative tools, such as this model, may enhance patients’ understanding of their preoperative condition and expected prognosis and thereby guide their surgical decisions.

We conducted a study to determine whether certain preoperative PROMs can predict 1-year postoperative visual analog scale (VAS) pain scores and American Shoulder and Elbow Surgeons (ASES) Function scores in total shoulder arthroplasty (TSA) and reverse TSA (rTSA). We hypothesized that preoperative mental health status as captured by Veterans RAND 12-Item Health Survey (VR-12) mental health component summary (MCS) score and preoperative VAS pain score would predict both VAS pain score and ASES Function score 1 year after surgery. Specifically, we hypothesized that a higher preoperative VR-12 MCS score would predict less pain and better function 1 year after surgery and that a higher preoperative VAS pain score would predict more pain and worse function 1 year after surgery.

Methods

This study was approved by the Institutional Review Board of Partners Healthcare. The study used the Surgical Outcome System (Arthrex), a comprehensive prospective database that stores preoperative and 1-year postoperative patient demographics and TSA-PROM data. Surveys are emailed to all enrolled patients before surgery and 1 year after surgery. As indicated by the Institutional Review Boards of all participating institutions, patients in the Surgical Outcome System have to sign a consent form to permit use of their responses in research.

The database includes patient data from 42 orthopedic surgeons across the United States. All primary TSAs and primary rTSAs in the database were included in this study, regardless of arthroplasty indication. Revisions were excluded. Also excluded were cases in which the 1-year postoperative questionnaire was not completed. Of the 1681 patients eligible for 1-year follow-up, 1225 (73%) completed the 1-year postoperative questionnaire. PROMs used in the study were VAS pain score, ASES Function score, VR-12 MCS score, and Single Assessment Numerical Evaluation (SANE). Unfortunately, not all surgeons use every measure in the 1-year postoperative questionnaire set. Thus, in our complete models, total number of observations was 1004 for modeling 1-year postoperative VAS pain scores and 986 for modeling 1-year postoperative ASES Function scores. 

Metrics

On VAS, pain is rated from 0 (no pain) to 10 (pain as bad as it can be). Tashjian and colleagues⁷ estimated that the minimal clinically important difference (MCID) for postoperative VAS pain scores was 1.4 in a cohort of 326 patients who had TSA, rTSA, or shoulder hemiarthroplasty. ASES Function score is scaled from 0 to 30, with 30 representing best function.⁸ Wong and colleagues⁹ identified an MCID of 6.5 for ASES Function scores in a cohort of 107 patients who had TSA or rTSA. SANE ratings range from 0% to 100%, with 100% indicating the patient’s shoulder was totally “normal.”10 VR-12 MCS scores appear on a logarithmic scale, with higher numbers representing better mental health. The population mean estimate for VR-12 MCS scores is 50.1 (SD, 11.49; overall possible range, –2.47 to 76.1).11 Our patient population’s scores ranged from 12.5 to 73.8.

Statistical Analysis

Simple bivariate and multivariate linear regressions were performed to evaluate the predictive value of each of the outlined PROMs. Our complete model controls for patient sex, age, and type of arthroplasty. Categorical variables were dummy-coded. Both 1-year postoperative VAS pain score and 1-year postoperative ASES Function score were investigated as dependent variables. Regression coefficients and P and ω² values are reported. Omega square represents how much of the variance in an outcome variable a model explains, like R², and ω² values can also be calculated for individual factors to see how much variance a given factor accounts for. For a simple relative risk calculation, we divided our cohort into 3 equal-sized groups based on preoperative VR-12 MCS scores and compared the risk that patients with scores in the top third (better mental health) would end up below certain ASES Total scores with the risk of patients with scores in the bottom third (worse mental health). All statistical analyses were performed with Stata (StataCorp).

Results

Table 1 lists summary statistics for the population used in these models.

Predicting VAS Pain Score (Table 2)

Preoperative VAS pain score and VR-12 MCS score both predicted 1-year postoperative VAS pain score (P < .001). Preoperative ASES Function score did not predict pain 1 year after surgery. By contrast, higher preoperative VAS pain scores were associated with higher VAS pain scores 1 year after surgery. Higher preoperative VR-12 MCS scores were significantly associated with lower VAS pain scores 1 year after surgery, indicating that better preoperative mental health is significantly associated with better self-reported outcomes in terms of pain 1 year after surgery. These associations remained statistically significant when controlling for age at time of surgery, sex, and type of arthroplasty.

Preoperative VR-12 MCS score was more predictive of 1-year postoperative VAS pain score than preoperative VAS pain score was. In other words, preoperative VR-12 MCS score accounted for more variability in outcome for 1-year postoperative VAS pain score (2.4%; ω² = .023) than preoperative VAS pain score did (1.6%; ω² = .015). 

Predicting ASES Function Score (Table 3)

By contrast, preoperative VAS pain score did not predict 1-year postoperative ASES Function score. Preoperative ASES Function and VR-12 MCS scores both predicted 1-year postoperative ASES Function score (P < .001). Higher preoperative ASES Function scores were associated with higher 1-year postoperative ASES Function scores. In other words, reporting better shoulder function before surgery was associated with reporting better shoulder function after surgery.

An example gives a sense of the effect size associated with the coefficient for preoperative ASES Function score. Our model predicts that, compared with a patient who reports 5 points lower on preoperative ASES Function (which ranges from 0-30), a patient who reports 5 points higher will report on average about 1 point higher on 1-year postoperative ASES Function. As in the model for postoperative pain, these associations with preoperative function and mental health scores held when controlling for age, sex, and type of arthroplasty. 

As in the postoperative pain model, preoperative VR-12 MCS score was more predictive of 1-year postoperative ASES Function score than preoperative ASES Function score was. Preoperative VR-12 MCS score accounted for more of the variation that our model predicts (ω² = .029) than preoperative ASES Function score did (ω² = .020). We compared the risk that patients with high preoperative VR-12 MCS scores (top third of cohort) would end up with ASES Total scores below 70, below 80, or below 90 with the risk of patients with low preoperative VR-12 MCS scores (bottom third). Results appear in Table 4.

A significant part of the predictive ability of our model for postoperative ASES Function scores stems from the fact that a patient who undergoes rTSA (vs TSA) is predicted to have an ASES Function score 3.8 points lower 1 year after surgery (P < .001, ω² = .083). With type of arthroplasty controlled for, female sex is associated with an ASES Function score 1.6 points lower 1 year after surgery (P < .001, ω² = .016).

Preoperative SANE score did not predict 1-year postoperative VAS pain score or ASES Function score. In addition, when our complete model was run with 1-year postoperative SANE score as the dependent variable, preoperative SANE score did not predict 1-year postoperative SANE score. Our data provide no supporting evidence for the use of SANE scores for predictive modeling for shoulder arthroplasty.

Discussion

We prospectively gathered data to determine which factors would predict patient subjective outcomes of primary TSA and primary rTSA. We hypothesized that preoperative VR-12 MCS scores and preoperative VAS pain scores would predict postoperative pain and function as measured with those PROMs. Second, we hypothesized that better preoperative mental health (as measured with VR-12 MCS scores) would predict lower postoperative pain (VAS pain scores) and better postoperative function (ASES Function scores). Third, we hypothesized that higher preoperative pain (VAS pain scores) would correlate with higher postoperative pain (VAS pain scores) and worse postoperative function (ASES Function scores).

Our main goal is to provide patients and surgeons with a predictive model that generates insights into what patients can expect after surgery. This model is not intended to be a screening tool for operative indications, but a clinical tool for helping set expectations.

Our results showed that patients with more pain before surgery were more likely to have more pain 1 year after surgery—confirming the hypothesized relationship between pain before and after surgery. Contrary to the hypothesis, however, degree of pain before surgery was not associated with function 1 year after surgery. Our mental health hypothesis was confirmed: Patients with better preoperative mental health scores had on average less pain and better function 1 year after surgery. Not surprisingly, our model demonstrated that patients with better self-reported function before surgery had better self-reported function after surgery. Patient-reported function before surgery did not significantly affect how much pain the patient had 1 year after surgery. Although we did not hypothesize about the role of function in predicting 1-year outcomes, function is a significant factor to be considered when setting patient expectations regarding shoulder arthroplasty outcomes (Table 5).

Although the effect sizes of each analyzed factor are small, together our models for 1-year postoperative pain and function provide significant insight into patients’ likely outcomes 1 year after TSA and rTSA.

Many studies have found that mental health correlated with pain and function during recovery from orthopedic trauma.^12-18 For example, Wylie and colleagues¹⁹ found that preoperative mental health, as measured with the 36-Item Short Form Health Survey (SF-36) MCS score, predicted patient-reported pain and function in the setting of rotator cuff injury, regardless of treatment type (operative, nonoperative). Others have found that mental health may play a role in how patients report their pain and function on various PROMs.^20,21 Modalities for improving patients’ emotional health baseline may even become a preoperative requirement as the healthcare industry moves toward value-based medicine and collection of patient-related outcomes as part of reimbursement schemes. 

By contrast, some studies have found that preoperative mental health did not predict postoperative outcomes. For example, Kennedy and colleagues²² found that preoperative mental health (as measured with SF-36 MCS scores) did not predict functional outcome in patients with ankle arthritis treated with ankle arthroplasty or arthrodesis. Likewise, Styron and colleagues²³ found no correlation between preoperative mental health (SF-12 MCS scores) and postoperative mental health and function in TSA. Their findings contradict those of the present study and many other studies.^12-18 The contradiction in findings demonstrates the need for well-designed, sufficiently powered studies of the link between preoperative mental health and postoperative outcome. Our study, with its large sample and heterogeneous population, is a start.

Two other groups (Simmen and colleagues,¹⁸ Matsen and colleagues²⁴) have attempted to develop a model predicting outcomes of shoulder arthroplasty. Simmen and colleagues¹⁸ estimated the probability of “treatment success” 1 year after TSA. Their model had 4 factors predictive of patient outcomes. Previous shoulder surgery and age over 75 years were significantly associated with lower probability of success, whereas higher preoperative SF-36 MCS scores and higher preoperative DASH (Disabilities of the Arm, Shoulder, and Hand) Function scores were associated with higher probability of success. The authors deemed TSA successful if the patient achieved a Constant score of ≥80 out of 100. Their model predicts probability of TSA “success,” whereas our models predict particular outcome scores. Both their results and ours support the hypothesis that preoperative mental health and function scores can predict how well a patient fares after surgery. Simmen and colleagues18 based their model on a cohort of only 140 patients and reported a 33.6% success rate (47/140 surgeries).

Matsen and colleagues²⁴ used a 1-practice cohort of 337 patients who underwent different types of arthroplasties, including TSA, rTSA, hemiarthroplasty, and ream-and-run arthroplasty. Although their focus was not preoperative PROMs predicting postoperative PROMs, they used the Simple Shoulder Test (SST) baseline score as a predictive variable. They found that 6 baseline characteristics—American Society of Anesthesiologists class I, shoulder problem unrelated to work, no prior shoulder surgery, glenoid type other than A1, humeral head not superiorly displaced on anteroposterior radiograph, and lower baseline SST score—were statistically associated with better outcomes, and they developed a model driven by these characteristics. They urged other investigators to perform the same kind of analysis with larger patient populations from multiple practices. One of the strengths of our study is its large patient population. We collected data on 1004 patients for modeling 1-year postoperative VAS pain scores and 986 patients for modeling 1-year postoperative ASES Function scores.

Our study had several limitations. First, its data came from a 42-surgeon database, and there may be variations in how these surgeons enroll patients in the registry. If some surgeons did not enroll all their surgical patients, our sample could have been subject to selection bias. Second, in developing our model, we used only patient characteristics that were available in the database. On the other hand, the heterogeneity of the surgeon sample lended external validity to the model. A third limitation was that the model always predicts better pain and function outcomes after TSA than after rTSA. In other words, it does not consider whether TSA is appropriate for a particular patient. Instead, it predicts 1-year shoulder arthroplasty outcomes. 

Our goal here is not to provide outcomes information or a surgical screening tool, but to report on our use of a simple data-driven tool for setting expectations. When appropriate data become available, tools like this should be expanded to predict longer-term shoulder arthroplasty outcomes. We need more studies that combine preoperative PROMs, more baseline clinical and patient characteristics (following the Matsen and colleagues²⁴ model), and large sample sizes.

Conclusion

The educational models presented here can help patients and surgeons learn what to expect after surgery. These models reveal the value in collecting preoperative subjective PROMs and show how a quantitative tool can help facilitate shared decision-making and set patient expectations. Separately, the effect size of each factor is small, but together a patient’s preoperative VAS pain score, ASES Function score, VR-12 MCS score, age, sex, and type of arthroplasty can provide information predictive of the patient’s self-reported pain and function 1 year after surgery.

A new tool can help where hospitalists need it most: at the bedside.

The focus on providing high-value care (HVC) continues to grow and expand in health care today. Still, most education around HVC currently happens in a formalized setting – lectures, modules, and so on, says Carolyn D. Sy, MD, interim director of the Hospital Medicine Service at the University of Washington, Seattle, and coauthor of a recent abstract about a new tool to address this shortcoming. “There are no instruments for measuring HVC discussions or practices at the bedside, confounding efforts to assess behavior changes associated with curricular interventions,” she said.

So she and other doctors undertook a study to identify 10 HVC topics in three domains (quality, cost, patient values), then measured their reliability with the goal of designing an HVC Rounding Tool and showing that it is an effective tool to measure observable markers of HVC at the bedside. “This is critical as it addresses an important educational gap in translating HVC from theoretical knowledge to bedside practice,” Dr. Sy said.

The tool is designed to capture multidisciplinary participation, she says, including involvement from not only faculty, fellows, or trainees, but also nursing, pharmacists, families, and other members of the health care team. The tool can be used as a peer feedback instrument to help physicians integrate HVC topics during bedside rounds or as a metric to assess the educational efficacy of future curriculum.

“The HVC Rounding Tool provides an opportunity for faculty development through peer observation and feedback on the integration and role modeling of HVC at the bedside,” Dr. Sy said. “It also is an instrument to help assess the educational efficacy of formal HVC curriculum and translation into bedside practice. Lastly, it is a tool that could be used to measure the relationship between HVC behaviors and actual patient outcomes such as length of stay, readmissions, cost of hospitalization – a feature with increasing importance given our move toward value-based health care.”

Reference

Sy CD, McDaniel C, Bradford M, et al. The Development and Validation of a High Value Care Rounding Tool Using the Delphi Method [abstract]. J Hosp Med. 2017; 12 (suppl 2). http://www.shmabstracts.com/abstract/the-development-and-validation-of-a-high-value-care-rounding-tool-using-the-delphi-method/. Accessed June 6, 2017.
 

A new tool can help where hospitalists need it most: at the bedside.

The focus on providing high-value care (HVC) continues to grow and expand in health care today. Still, most education around HVC currently happens in a formalized setting – lectures, modules, and so on, says Carolyn D. Sy, MD, interim director of the Hospital Medicine Service at the University of Washington, Seattle, and coauthor of a recent abstract about a new tool to address this shortcoming. “There are no instruments for measuring HVC discussions or practices at the bedside, confounding efforts to assess behavior changes associated with curricular interventions,” she said.

So she and other doctors undertook a study to identify 10 HVC topics in three domains (quality, cost, patient values), then measured their reliability with the goal of designing an HVC Rounding Tool and showing that it is an effective tool to measure observable markers of HVC at the bedside. “This is critical as it addresses an important educational gap in translating HVC from theoretical knowledge to bedside practice,” Dr. Sy said.

The tool is designed to capture multidisciplinary participation, she says, including involvement from not only faculty, fellows, or trainees, but also nursing, pharmacists, families, and other members of the health care team. The tool can be used as a peer feedback instrument to help physicians integrate HVC topics during bedside rounds or as a metric to assess the educational efficacy of future curriculum.

“The HVC Rounding Tool provides an opportunity for faculty development through peer observation and feedback on the integration and role modeling of HVC at the bedside,” Dr. Sy said. “It also is an instrument to help assess the educational efficacy of formal HVC curriculum and translation into bedside practice. Lastly, it is a tool that could be used to measure the relationship between HVC behaviors and actual patient outcomes such as length of stay, readmissions, cost of hospitalization – a feature with increasing importance given our move toward value-based health care.”

Reference

Sy CD, McDaniel C, Bradford M, et al. The Development and Validation of a High Value Care Rounding Tool Using the Delphi Method [abstract]. J Hosp Med. 2017; 12 (suppl 2). http://www.shmabstracts.com/abstract/the-development-and-validation-of-a-high-value-care-rounding-tool-using-the-delphi-method/. Accessed June 6, 2017.
 

A new tool can help where hospitalists need it most: at the bedside.

The focus on providing high-value care (HVC) continues to grow and expand in health care today. Still, most education around HVC currently happens in a formalized setting – lectures, modules, and so on, says Carolyn D. Sy, MD, interim director of the Hospital Medicine Service at the University of Washington, Seattle, and coauthor of a recent abstract about a new tool to address this shortcoming. “There are no instruments for measuring HVC discussions or practices at the bedside, confounding efforts to assess behavior changes associated with curricular interventions,” she said.

So she and other doctors undertook a study to identify 10 HVC topics in three domains (quality, cost, patient values), then measured their reliability with the goal of designing an HVC Rounding Tool and showing that it is an effective tool to measure observable markers of HVC at the bedside. “This is critical as it addresses an important educational gap in translating HVC from theoretical knowledge to bedside practice,” Dr. Sy said.

The tool is designed to capture multidisciplinary participation, she says, including involvement from not only faculty, fellows, or trainees, but also nursing, pharmacists, families, and other members of the health care team. The tool can be used as a peer feedback instrument to help physicians integrate HVC topics during bedside rounds or as a metric to assess the educational efficacy of future curriculum.

“The HVC Rounding Tool provides an opportunity for faculty development through peer observation and feedback on the integration and role modeling of HVC at the bedside,” Dr. Sy said. “It also is an instrument to help assess the educational efficacy of formal HVC curriculum and translation into bedside practice. Lastly, it is a tool that could be used to measure the relationship between HVC behaviors and actual patient outcomes such as length of stay, readmissions, cost of hospitalization – a feature with increasing importance given our move toward value-based health care.”

Reference

Sy CD, McDaniel C, Bradford M, et al. The Development and Validation of a High Value Care Rounding Tool Using the Delphi Method [abstract]. J Hosp Med. 2017; 12 (suppl 2). http://www.shmabstracts.com/abstract/the-development-and-validation-of-a-high-value-care-rounding-tool-using-the-delphi-method/. Accessed June 6, 2017.
 

SAN DIEGO – A new study offers a double message about the potential impact of obesity on systemic lupus erythematosus (SLE) in women: Excess pounds are linked to a higher risk of patient-reported outcomes such as pain and fatigue, and body mass index may be an appropriate tool to study weight issues in this population.

Researchers found “a strong relationship between body composition and worse outcomes,” Sarah Patterson, MD, a fellow in rheumatology at the University of California, San Francisco, and the lead study author, said at the annual meeting of the American College of Rheumatology.

wildpixel/Thinkstock

SAN DIEGO – A new study offers a double message about the potential impact of obesity on systemic lupus erythematosus (SLE) in women: Excess pounds are linked to a higher risk of patient-reported outcomes such as pain and fatigue, and body mass index may be an appropriate tool to study weight issues in this population.

Researchers found “a strong relationship between body composition and worse outcomes,” Sarah Patterson, MD, a fellow in rheumatology at the University of California, San Francisco, and the lead study author, said at the annual meeting of the American College of Rheumatology.

wildpixel/Thinkstock

SAN DIEGO – A new study offers a double message about the potential impact of obesity on systemic lupus erythematosus (SLE) in women: Excess pounds are linked to a higher risk of patient-reported outcomes such as pain and fatigue, and body mass index may be an appropriate tool to study weight issues in this population.

Researchers found “a strong relationship between body composition and worse outcomes,” Sarah Patterson, MD, a fellow in rheumatology at the University of California, San Francisco, and the lead study author, said at the annual meeting of the American College of Rheumatology.

wildpixel/Thinkstock

SAN DIEGO – The incidence of gout is strongly linked to patients’ concentration of serum uric acid over time, but even so, less than half of patients with levels of 10 mg/dL or above develop the condition by 15 years, according to the largest individual person-level analysis to examine the relationship.

The incidence of gout rose from about 1% after 15 years in patients with a serum uric acid (sUA) level of less than 6 mg/dL to almost 49% in those with 10 mg/dL or higher in the study, which implies “a long period of hyperuricemia preceding the onset of clinical gout” and also “supports a role for additional factors in the pathogenesis of gout,” Nicola Dalbeth, MD, said in her presentation of the results at the annual meeting of the American College of Rheumatology.

jarun011/Thinkstock

[email protected]

SAN DIEGO – The incidence of gout is strongly linked to patients’ concentration of serum uric acid over time, but even so, less than half of patients with levels of 10 mg/dL or above develop the condition by 15 years, according to the largest individual person-level analysis to examine the relationship.

The incidence of gout rose from about 1% after 15 years in patients with a serum uric acid (sUA) level of less than 6 mg/dL to almost 49% in those with 10 mg/dL or higher in the study, which implies “a long period of hyperuricemia preceding the onset of clinical gout” and also “supports a role for additional factors in the pathogenesis of gout,” Nicola Dalbeth, MD, said in her presentation of the results at the annual meeting of the American College of Rheumatology.

jarun011/Thinkstock

[email protected]

SAN DIEGO – The incidence of gout is strongly linked to patients’ concentration of serum uric acid over time, but even so, less than half of patients with levels of 10 mg/dL or above develop the condition by 15 years, according to the largest individual person-level analysis to examine the relationship.

The incidence of gout rose from about 1% after 15 years in patients with a serum uric acid (sUA) level of less than 6 mg/dL to almost 49% in those with 10 mg/dL or higher in the study, which implies “a long period of hyperuricemia preceding the onset of clinical gout” and also “supports a role for additional factors in the pathogenesis of gout,” Nicola Dalbeth, MD, said in her presentation of the results at the annual meeting of the American College of Rheumatology.

jarun011/Thinkstock

[email protected]