Jeffrey L. Schnipper, MD, MPH

In the outpatient setting, medication adherence (defined as percentage of prescribed medication doses taken by a patient during a specific time period) ranges between 40% and 80% for chronic conditions.1 During acute care hospitalization, changes are often made to patients' medication regimens, which can be confusing and contribute to nonadherence, medication errors, and harmful adverse events.2 Indeed, it is estimated that almost half of patients encounter a medication error after discharge, and approximately 12%17% experience an adverse drug event after returning home.36 It is likely that some of these adverse events may be the result of medication nonadherence.7 Improved patientprovider communication, systems to reconcile prehospitalization and posthospitalization medications, as well as development of mechanisms to enhance adherence, may prevent many of these errors and have become new targets for quality improvement.4, 8 Although postdischarge medication adherence is a crucial target for avoiding adverse events and rehospitalization, few studies have focused on understanding its incidence and predictors, in particular, patient demographic factors such as age and insurance status.911

In addition, few studies have looked at general and posthospital adherence in a population where health literacy is measured, an important area because medication changes during hospitalization may be particularly confusing for patients with low health literacy.11, 12 Health literacy is defined as the degree to which an individual has the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.13 Prior outpatient research shows that low health literacy is associated with poor patient understanding of the medication regimen and instructions for medication use, which may contribute to postdischarge medication nonadherence.14, 15 Understanding the factors associated with postdischarge medication adherence could help refine interventions that are oriented toward improving transitions in care, patient safety, and reducing unnecessary rehospitalization.

We report here on factors associated with postdischarge medication adherence using data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study.16

METHODS

RESULTS

Table 1 shows descriptive baseline patient characteristics of study sample (responders) as well as nonresponders at 30 days. For the responders, the mean age of the 646 patients was 61.2 years, 94.7% were insured, and 19.3% had inadequate or marginal health literacy. Patients were prescribed an average of 8 preadmission medications. Most patients (92.3%) had a regular PCP prior to admission. Nonresponders had nonsignificant trends towards having lower health literacy, medication understanding, and baseline medication adherence.

The average postdischarge adherence score was 95% (standard deviation [SD] = 10.2%), and less than 10% of patients had an adherence score of less than 85%; overall the distribution was left‐skewed. Table 2 illustrates crude and adjusted parameter estimates for variables in the model. Table 3 shows significant findings in the fully adjusted model, which used multiple imputation techniques to account for missing data.

Intervention arm was of borderline statistical significance in predicting postdischarge adherence (P = 0.052), and so was removed from the final model. Study site, age, insurance, and baseline adherence were the only significant independent predictors of postdischarge adherence in the fully adjusted model (Table 3). For example, for every 10‐year increase in age, patients had, on average, an adjusted 1% absolute increase in their adherence score (95% confidence interval [CI] 0.4% to 2.0%). For every 1‐point increase in baseline medication adherence (based on the Morisky scale), there was a 1.6% absolute increase in medication adherence (95% CI 0.8% to 2.4%). In unadjusted analyses, patients with Medicaid were less adherent with medications after discharge than were patients with private insurance. This difference became nonsignificant in adjusted analyses, but when analyses were repeated using multiple imputation techniques, the results again became statistically significantMedicaid insurance was associated with a 4.5% absolute decrease in postdischarge adherence compared with private insurance (95% CI 7.6% to 1.4%). Study site (specifically, Brigham and Women's Hospital) was also a significant predictor of greater postdischarge medication adherence. Years of education was a significant predictor of adherence in unadjusted analyses, but was not an independent predictor when adjusted for other factors. When baseline adherence was removed from the multiple imputation model, there were no changes in which factors were significant predictors of adherence.

DISCUSSION

In this study, we found that low baseline adherence, younger age, Medicaid insurance, and study site were significant predictors of lower 30‐day medication adherence. Of particular interest is our finding regarding baseline adherence, a simple measure to obtain on hospitalized patients. It is notable that in our study, education was not an independent significant predictor of postdischarge adherence, even when baseline adherence was removed from the model. The same is true for medication understanding, cognitive function, and health literacy.

Older patients appeared more adherent with medications in the month after hospital discharge, perhaps reflecting increased interaction with the healthcare system (appointments, number of physician interactions), a greater belief in the importance of chronic medication management, or a higher level of experience with managing medications. A similar relationship between age and adherence has been shown in outpatient studies of patients with hypertension, diabetes, and other chronic diseases.2427

Medicaid patients may be less likely to remain adherent because of the plan's limited coverage of medications relative to patients' ability to pay. For example, Medicaid in Tennessee covers the first 5 generic medications at no cost to the patient but has co‐payments for additional medications and for brand name drugs. Medicaid in Massachusetts has co‐payments of $1 to $3 for each medication. Alternatively, Medicaid insurance may be a marker for other patient characteristics associated with low adherence for which we were not fully able to adjust.

Site differences were also notable in this study; these differences could have been due to differences in insurance coverage in Tennessee versus Massachusetts (which has near‐universal coverage), differences in types of insurance (eg, fewer patients at Brigham and Women's Hospital had Medicaid than at Vanderbilt), cultural and geographic differences between the 2 locations, or other differences in transitional care between the 2 sites.

This study corroborates previous literature on medication adherence (specifically unintentional nonadherence) in the outpatient setting,4, 811 for example, on the association of younger age with low adherence in certain populations. On the other hand, it may contrast with previous literature which has sometimes shown a relationship between patient education or health literacy and medication adherence.14, 15, 2835 However, previous studies have not focused on the transition from inpatient to outpatient settings. Perhaps intensive medication education in the hospital, even under usual care, mitigates the effects of these factors on postdischarge adherence. Finally, baseline adherence seems to correlate with postdischarge adherence, a finding which makes intuitive sense and has been previously reported for specific medications.36

There are several limitations to this study. Although large, the study was performed at only 2 clinical sites where most patients were white and fairly well‐educated, perhaps because patients admitted to a tertiary care center with ACS or ADHF are more affluent than general medical inpatients as a whole; this may limit generalizability. Postdischarge medication adherence might have been higher than in other patient populations given the nature of the population, possible loss‐to‐follow‐up bias, and the fact that half of the subjects received an intervention designed to improve medication management after discharge; such low rates of nonadherence in our study may have reduced our ability to detect important predictors in our models. In addition, the period of follow‐up was 30 days, thus limiting our findings to short‐term postdischarge medication adherence. Postdischarge medication adherence was based on patient self‐report, which not only assumed that the patient was still managing his/her own medications after discharge, but may also be susceptible to both recall and social acceptability bias, which might overestimate our adherence scores, again limiting our ability to detect important predictors of nonadherence. However, other studies have shown a good correlation between self‐reported medication adherence and other more objective measures,37, 38 and recall was only for 7 days, a measure used previously in the literature39, 40 and one designed to reduce recall bias. Systematic underreporting in certain patient populations is less likely but possible.

In the future, research should focus on targeting patients who have low baseline adherence to evaluate the effects of various interventions on postdischarge medication outcomes. Repeating the study in a population with a high prevalence of low health literacy might be illuminating, given that previous studies have shown that patients with low health literacy have less ability to identify their medications and have less refill adherence.29, 30

In conclusion, in patients hospitalized with cardiovascular disease, predictors of lower postdischarge adherence include younger age, Medicaid insurance, and low baseline adherence. It may be prudent to assess baseline adherence and insurance type in hospitalized patients in order to identify those who may benefit from additional assistance to improve medication adherence and medication safety during transitions in care.

In the outpatient setting, medication adherence (defined as percentage of prescribed medication doses taken by a patient during a specific time period) ranges between 40% and 80% for chronic conditions.1 During acute care hospitalization, changes are often made to patients' medication regimens, which can be confusing and contribute to nonadherence, medication errors, and harmful adverse events.2 Indeed, it is estimated that almost half of patients encounter a medication error after discharge, and approximately 12%17% experience an adverse drug event after returning home.36 It is likely that some of these adverse events may be the result of medication nonadherence.7 Improved patientprovider communication, systems to reconcile prehospitalization and posthospitalization medications, as well as development of mechanisms to enhance adherence, may prevent many of these errors and have become new targets for quality improvement.4, 8 Although postdischarge medication adherence is a crucial target for avoiding adverse events and rehospitalization, few studies have focused on understanding its incidence and predictors, in particular, patient demographic factors such as age and insurance status.911

In addition, few studies have looked at general and posthospital adherence in a population where health literacy is measured, an important area because medication changes during hospitalization may be particularly confusing for patients with low health literacy.11, 12 Health literacy is defined as the degree to which an individual has the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.13 Prior outpatient research shows that low health literacy is associated with poor patient understanding of the medication regimen and instructions for medication use, which may contribute to postdischarge medication nonadherence.14, 15 Understanding the factors associated with postdischarge medication adherence could help refine interventions that are oriented toward improving transitions in care, patient safety, and reducing unnecessary rehospitalization.

We report here on factors associated with postdischarge medication adherence using data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study.16

METHODS

RESULTS

Table 1 shows descriptive baseline patient characteristics of study sample (responders) as well as nonresponders at 30 days. For the responders, the mean age of the 646 patients was 61.2 years, 94.7% were insured, and 19.3% had inadequate or marginal health literacy. Patients were prescribed an average of 8 preadmission medications. Most patients (92.3%) had a regular PCP prior to admission. Nonresponders had nonsignificant trends towards having lower health literacy, medication understanding, and baseline medication adherence.

The average postdischarge adherence score was 95% (standard deviation [SD] = 10.2%), and less than 10% of patients had an adherence score of less than 85%; overall the distribution was left‐skewed. Table 2 illustrates crude and adjusted parameter estimates for variables in the model. Table 3 shows significant findings in the fully adjusted model, which used multiple imputation techniques to account for missing data.

Intervention arm was of borderline statistical significance in predicting postdischarge adherence (P = 0.052), and so was removed from the final model. Study site, age, insurance, and baseline adherence were the only significant independent predictors of postdischarge adherence in the fully adjusted model (Table 3). For example, for every 10‐year increase in age, patients had, on average, an adjusted 1% absolute increase in their adherence score (95% confidence interval [CI] 0.4% to 2.0%). For every 1‐point increase in baseline medication adherence (based on the Morisky scale), there was a 1.6% absolute increase in medication adherence (95% CI 0.8% to 2.4%). In unadjusted analyses, patients with Medicaid were less adherent with medications after discharge than were patients with private insurance. This difference became nonsignificant in adjusted analyses, but when analyses were repeated using multiple imputation techniques, the results again became statistically significantMedicaid insurance was associated with a 4.5% absolute decrease in postdischarge adherence compared with private insurance (95% CI 7.6% to 1.4%). Study site (specifically, Brigham and Women's Hospital) was also a significant predictor of greater postdischarge medication adherence. Years of education was a significant predictor of adherence in unadjusted analyses, but was not an independent predictor when adjusted for other factors. When baseline adherence was removed from the multiple imputation model, there were no changes in which factors were significant predictors of adherence.

DISCUSSION

In this study, we found that low baseline adherence, younger age, Medicaid insurance, and study site were significant predictors of lower 30‐day medication adherence. Of particular interest is our finding regarding baseline adherence, a simple measure to obtain on hospitalized patients. It is notable that in our study, education was not an independent significant predictor of postdischarge adherence, even when baseline adherence was removed from the model. The same is true for medication understanding, cognitive function, and health literacy.

Older patients appeared more adherent with medications in the month after hospital discharge, perhaps reflecting increased interaction with the healthcare system (appointments, number of physician interactions), a greater belief in the importance of chronic medication management, or a higher level of experience with managing medications. A similar relationship between age and adherence has been shown in outpatient studies of patients with hypertension, diabetes, and other chronic diseases.2427

Medicaid patients may be less likely to remain adherent because of the plan's limited coverage of medications relative to patients' ability to pay. For example, Medicaid in Tennessee covers the first 5 generic medications at no cost to the patient but has co‐payments for additional medications and for brand name drugs. Medicaid in Massachusetts has co‐payments of $1 to $3 for each medication. Alternatively, Medicaid insurance may be a marker for other patient characteristics associated with low adherence for which we were not fully able to adjust.

Site differences were also notable in this study; these differences could have been due to differences in insurance coverage in Tennessee versus Massachusetts (which has near‐universal coverage), differences in types of insurance (eg, fewer patients at Brigham and Women's Hospital had Medicaid than at Vanderbilt), cultural and geographic differences between the 2 locations, or other differences in transitional care between the 2 sites.

This study corroborates previous literature on medication adherence (specifically unintentional nonadherence) in the outpatient setting,4, 811 for example, on the association of younger age with low adherence in certain populations. On the other hand, it may contrast with previous literature which has sometimes shown a relationship between patient education or health literacy and medication adherence.14, 15, 2835 However, previous studies have not focused on the transition from inpatient to outpatient settings. Perhaps intensive medication education in the hospital, even under usual care, mitigates the effects of these factors on postdischarge adherence. Finally, baseline adherence seems to correlate with postdischarge adherence, a finding which makes intuitive sense and has been previously reported for specific medications.36

There are several limitations to this study. Although large, the study was performed at only 2 clinical sites where most patients were white and fairly well‐educated, perhaps because patients admitted to a tertiary care center with ACS or ADHF are more affluent than general medical inpatients as a whole; this may limit generalizability. Postdischarge medication adherence might have been higher than in other patient populations given the nature of the population, possible loss‐to‐follow‐up bias, and the fact that half of the subjects received an intervention designed to improve medication management after discharge; such low rates of nonadherence in our study may have reduced our ability to detect important predictors in our models. In addition, the period of follow‐up was 30 days, thus limiting our findings to short‐term postdischarge medication adherence. Postdischarge medication adherence was based on patient self‐report, which not only assumed that the patient was still managing his/her own medications after discharge, but may also be susceptible to both recall and social acceptability bias, which might overestimate our adherence scores, again limiting our ability to detect important predictors of nonadherence. However, other studies have shown a good correlation between self‐reported medication adherence and other more objective measures,37, 38 and recall was only for 7 days, a measure used previously in the literature39, 40 and one designed to reduce recall bias. Systematic underreporting in certain patient populations is less likely but possible.

In the future, research should focus on targeting patients who have low baseline adherence to evaluate the effects of various interventions on postdischarge medication outcomes. Repeating the study in a population with a high prevalence of low health literacy might be illuminating, given that previous studies have shown that patients with low health literacy have less ability to identify their medications and have less refill adherence.29, 30

In conclusion, in patients hospitalized with cardiovascular disease, predictors of lower postdischarge adherence include younger age, Medicaid insurance, and low baseline adherence. It may be prudent to assess baseline adherence and insurance type in hospitalized patients in order to identify those who may benefit from additional assistance to improve medication adherence and medication safety during transitions in care.

In the outpatient setting, medication adherence (defined as percentage of prescribed medication doses taken by a patient during a specific time period) ranges between 40% and 80% for chronic conditions.1 During acute care hospitalization, changes are often made to patients' medication regimens, which can be confusing and contribute to nonadherence, medication errors, and harmful adverse events.2 Indeed, it is estimated that almost half of patients encounter a medication error after discharge, and approximately 12%17% experience an adverse drug event after returning home.36 It is likely that some of these adverse events may be the result of medication nonadherence.7 Improved patientprovider communication, systems to reconcile prehospitalization and posthospitalization medications, as well as development of mechanisms to enhance adherence, may prevent many of these errors and have become new targets for quality improvement.4, 8 Although postdischarge medication adherence is a crucial target for avoiding adverse events and rehospitalization, few studies have focused on understanding its incidence and predictors, in particular, patient demographic factors such as age and insurance status.911

In addition, few studies have looked at general and posthospital adherence in a population where health literacy is measured, an important area because medication changes during hospitalization may be particularly confusing for patients with low health literacy.11, 12 Health literacy is defined as the degree to which an individual has the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.13 Prior outpatient research shows that low health literacy is associated with poor patient understanding of the medication regimen and instructions for medication use, which may contribute to postdischarge medication nonadherence.14, 15 Understanding the factors associated with postdischarge medication adherence could help refine interventions that are oriented toward improving transitions in care, patient safety, and reducing unnecessary rehospitalization.

We report here on factors associated with postdischarge medication adherence using data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study.16

METHODS

RESULTS

Table 1 shows descriptive baseline patient characteristics of study sample (responders) as well as nonresponders at 30 days. For the responders, the mean age of the 646 patients was 61.2 years, 94.7% were insured, and 19.3% had inadequate or marginal health literacy. Patients were prescribed an average of 8 preadmission medications. Most patients (92.3%) had a regular PCP prior to admission. Nonresponders had nonsignificant trends towards having lower health literacy, medication understanding, and baseline medication adherence.

The average postdischarge adherence score was 95% (standard deviation [SD] = 10.2%), and less than 10% of patients had an adherence score of less than 85%; overall the distribution was left‐skewed. Table 2 illustrates crude and adjusted parameter estimates for variables in the model. Table 3 shows significant findings in the fully adjusted model, which used multiple imputation techniques to account for missing data.

Intervention arm was of borderline statistical significance in predicting postdischarge adherence (P = 0.052), and so was removed from the final model. Study site, age, insurance, and baseline adherence were the only significant independent predictors of postdischarge adherence in the fully adjusted model (Table 3). For example, for every 10‐year increase in age, patients had, on average, an adjusted 1% absolute increase in their adherence score (95% confidence interval [CI] 0.4% to 2.0%). For every 1‐point increase in baseline medication adherence (based on the Morisky scale), there was a 1.6% absolute increase in medication adherence (95% CI 0.8% to 2.4%). In unadjusted analyses, patients with Medicaid were less adherent with medications after discharge than were patients with private insurance. This difference became nonsignificant in adjusted analyses, but when analyses were repeated using multiple imputation techniques, the results again became statistically significantMedicaid insurance was associated with a 4.5% absolute decrease in postdischarge adherence compared with private insurance (95% CI 7.6% to 1.4%). Study site (specifically, Brigham and Women's Hospital) was also a significant predictor of greater postdischarge medication adherence. Years of education was a significant predictor of adherence in unadjusted analyses, but was not an independent predictor when adjusted for other factors. When baseline adherence was removed from the multiple imputation model, there were no changes in which factors were significant predictors of adherence.

DISCUSSION

In this study, we found that low baseline adherence, younger age, Medicaid insurance, and study site were significant predictors of lower 30‐day medication adherence. Of particular interest is our finding regarding baseline adherence, a simple measure to obtain on hospitalized patients. It is notable that in our study, education was not an independent significant predictor of postdischarge adherence, even when baseline adherence was removed from the model. The same is true for medication understanding, cognitive function, and health literacy.

Older patients appeared more adherent with medications in the month after hospital discharge, perhaps reflecting increased interaction with the healthcare system (appointments, number of physician interactions), a greater belief in the importance of chronic medication management, or a higher level of experience with managing medications. A similar relationship between age and adherence has been shown in outpatient studies of patients with hypertension, diabetes, and other chronic diseases.2427

Medicaid patients may be less likely to remain adherent because of the plan's limited coverage of medications relative to patients' ability to pay. For example, Medicaid in Tennessee covers the first 5 generic medications at no cost to the patient but has co‐payments for additional medications and for brand name drugs. Medicaid in Massachusetts has co‐payments of $1 to $3 for each medication. Alternatively, Medicaid insurance may be a marker for other patient characteristics associated with low adherence for which we were not fully able to adjust.

Site differences were also notable in this study; these differences could have been due to differences in insurance coverage in Tennessee versus Massachusetts (which has near‐universal coverage), differences in types of insurance (eg, fewer patients at Brigham and Women's Hospital had Medicaid than at Vanderbilt), cultural and geographic differences between the 2 locations, or other differences in transitional care between the 2 sites.

This study corroborates previous literature on medication adherence (specifically unintentional nonadherence) in the outpatient setting,4, 811 for example, on the association of younger age with low adherence in certain populations. On the other hand, it may contrast with previous literature which has sometimes shown a relationship between patient education or health literacy and medication adherence.14, 15, 2835 However, previous studies have not focused on the transition from inpatient to outpatient settings. Perhaps intensive medication education in the hospital, even under usual care, mitigates the effects of these factors on postdischarge adherence. Finally, baseline adherence seems to correlate with postdischarge adherence, a finding which makes intuitive sense and has been previously reported for specific medications.36

There are several limitations to this study. Although large, the study was performed at only 2 clinical sites where most patients were white and fairly well‐educated, perhaps because patients admitted to a tertiary care center with ACS or ADHF are more affluent than general medical inpatients as a whole; this may limit generalizability. Postdischarge medication adherence might have been higher than in other patient populations given the nature of the population, possible loss‐to‐follow‐up bias, and the fact that half of the subjects received an intervention designed to improve medication management after discharge; such low rates of nonadherence in our study may have reduced our ability to detect important predictors in our models. In addition, the period of follow‐up was 30 days, thus limiting our findings to short‐term postdischarge medication adherence. Postdischarge medication adherence was based on patient self‐report, which not only assumed that the patient was still managing his/her own medications after discharge, but may also be susceptible to both recall and social acceptability bias, which might overestimate our adherence scores, again limiting our ability to detect important predictors of nonadherence. However, other studies have shown a good correlation between self‐reported medication adherence and other more objective measures,37, 38 and recall was only for 7 days, a measure used previously in the literature39, 40 and one designed to reduce recall bias. Systematic underreporting in certain patient populations is less likely but possible.

In the future, research should focus on targeting patients who have low baseline adherence to evaluate the effects of various interventions on postdischarge medication outcomes. Repeating the study in a population with a high prevalence of low health literacy might be illuminating, given that previous studies have shown that patients with low health literacy have less ability to identify their medications and have less refill adherence.29, 30

In conclusion, in patients hospitalized with cardiovascular disease, predictors of lower postdischarge adherence include younger age, Medicaid insurance, and low baseline adherence. It may be prudent to assess baseline adherence and insurance type in hospitalized patients in order to identify those who may benefit from additional assistance to improve medication adherence and medication safety during transitions in care.

If you wish to receive credit for this activity, please refer to the website: www.wileyblackwellcme.com.

Accreditation and Designation Statement

Blackwell Futura Media Services designates this journal‐based CME activity for a maximum of 1 AMA PRA Category 1 Credit. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Educational Objectives

Upon completion of this activity, participants will be better able to:

• Assess the factors associated with reduced medication adherence.

• Distinguish which components of medication understanding are assessed by the Medication Understanding Questionnaire.

This manuscript underwent peer review in line with the standards of editorial integrity and publication ethics maintained by Journal of Hospital Medicine. The peer reviewers have no relevant financial relationships. The peer review process for Journal of Hospital Medicine is single‐blinded. As such, the identities of the reviewers are not disclosed in line with the standard accepted practices of medical journal peer review.

Conflicts of interest have been identified and resolved in accordance with Blackwell Futura Media Services's Policy on Activity Disclosure and Conflict of Interest. The primary resolution method used was peer review and review by a non‐conflicted expert.

Instructions on Receiving Credit

For information on applicability and acceptance of CME credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within an hour; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period, which is up to two years from initial publication.

Follow these steps to earn credit:

• Log on to www.wileyblackwellcme.com

• Read the target audience, learning objectives, and author disclosures.

• Read the article in print or online format.

• Reflect on the article.

• Access the CME Exam, and choose the best answer to each question.

• Complete the required evaluation component of the activity.

This activity will be available for CME credit for twelve months following its publication date. At that time, it will be reviewed and potentially updated and extended for an additional twelve months.

If you wish to receive credit for this activity, please refer to the website: www.wileyblackwellcme.com.

Accreditation and Designation Statement

Blackwell Futura Media Services designates this journal‐based CME activity for a maximum of 1 AMA PRA Category 1 Credit. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Educational Objectives

Upon completion of this activity, participants will be better able to:

• Assess the factors associated with reduced medication adherence.

• Distinguish which components of medication understanding are assessed by the Medication Understanding Questionnaire.

This manuscript underwent peer review in line with the standards of editorial integrity and publication ethics maintained by Journal of Hospital Medicine. The peer reviewers have no relevant financial relationships. The peer review process for Journal of Hospital Medicine is single‐blinded. As such, the identities of the reviewers are not disclosed in line with the standard accepted practices of medical journal peer review.

Conflicts of interest have been identified and resolved in accordance with Blackwell Futura Media Services's Policy on Activity Disclosure and Conflict of Interest. The primary resolution method used was peer review and review by a non‐conflicted expert.

Instructions on Receiving Credit

For information on applicability and acceptance of CME credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within an hour; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period, which is up to two years from initial publication.

Follow these steps to earn credit:

• Log on to www.wileyblackwellcme.com

• Read the target audience, learning objectives, and author disclosures.

• Read the article in print or online format.

• Reflect on the article.

• Access the CME Exam, and choose the best answer to each question.

• Complete the required evaluation component of the activity.

This activity will be available for CME credit for twelve months following its publication date. At that time, it will be reviewed and potentially updated and extended for an additional twelve months.

If you wish to receive credit for this activity, please refer to the website: www.wileyblackwellcme.com.

Accreditation and Designation Statement

Blackwell Futura Media Services designates this journal‐based CME activity for a maximum of 1 AMA PRA Category 1 Credit. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Educational Objectives

Upon completion of this activity, participants will be better able to:

• Assess the factors associated with reduced medication adherence.

• Distinguish which components of medication understanding are assessed by the Medication Understanding Questionnaire.

This manuscript underwent peer review in line with the standards of editorial integrity and publication ethics maintained by Journal of Hospital Medicine. The peer reviewers have no relevant financial relationships. The peer review process for Journal of Hospital Medicine is single‐blinded. As such, the identities of the reviewers are not disclosed in line with the standard accepted practices of medical journal peer review.

Conflicts of interest have been identified and resolved in accordance with Blackwell Futura Media Services's Policy on Activity Disclosure and Conflict of Interest. The primary resolution method used was peer review and review by a non‐conflicted expert.

Instructions on Receiving Credit

For information on applicability and acceptance of CME credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within an hour; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period, which is up to two years from initial publication.

Follow these steps to earn credit:

• Log on to www.wileyblackwellcme.com

• Read the target audience, learning objectives, and author disclosures.

• Read the article in print or online format.

• Reflect on the article.

• Access the CME Exam, and choose the best answer to each question.

• Complete the required evaluation component of the activity.

This activity will be available for CME credit for twelve months following its publication date. At that time, it will be reviewed and potentially updated and extended for an additional twelve months.

With the aging of the US population, complex medication regimens to treat multiple comorbidities are increasingly common.1 Nevertheless, patients often do not fully understand the instructions for safe and effective medication use. Aspects of medication understanding include knowledge of the drug indication, dose, frequency, and for certain medications, special instructions.2 Medication understanding is associated with better medication adherence, fewer drug‐related problems, and fewer emergency department visits.3 Among patients with chronic conditions, such as cardiovascular disease (CVD), understanding and adherence to the medication regimen are critical for successful disease control and clinical outcomes.4

Patients' understanding of their medication regimen is also vitally important upon admission to the hospital. Patients often are the main source of information for the admission medication history and subsequent medication reconciliation.5 Poor patient understanding of the preadmission medication regimen can contribute to errors in inpatient and postdischarge medication orders, and adversely affect patient safety.6 However, little research has examined patients' understanding of the preadmission medication regimen and factors that affect it.

In the outpatient setting, previous investigations have suggested that low health literacy, advanced age, and impaired cognitive function adversely affect patients' understanding of medication instructions.2, 7, 8 These studies were limited by a small sample size, single site, or focus on a specific population, such as inner‐city patients. Additionally, the measures used to assess medication understanding were time‐consuming and required patients' medications to be present for testing, thus limiting their utility.2

To address these gaps in the literature, we developed and implemented the Medication Understanding Questionnaire (MUQ), an original and relatively rapid measure that does not require patients' medications be present for testing. In a study of adults at 2 large teaching hospitals, we examined the association of health literacy, age, cognitive function, number of preadmission medications, and other factors on patients' understanding of their preadmission medication regimen. We hypothesized that lower health literacy would be independently associated with lower medication understanding as assessed using the MUQ.

METHODS

The present study was a cross‐sectional assessment conducted using baseline interview data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) Study (ClinicalTrials.gov Registration #NCT00632021; available at: http://clinicaltrials.gov/show/NCT00632021). The PILL‐CVD Study is a randomized controlled trial of a pharmacist‐based intervention, consisting of pharmacist‐assisted medication reconciliation, inpatient counseling, low‐literacy adherence aids, and postdischarge telephone follow‐up. It was conducted at 2 academic medical centersVanderbilt University Hospital (VUH) in Nashville, Tennessee, and Brigham and Women's Hospital (BWH) in Boston, Massachusetts.9 This study was approved by the Institutional Review Board at each site, and all participants provided written informed consent.

RESULTS

Predictors of Medication Understanding

Unadjusted relationships of health literacy, cognition, and number of medications with medication understanding are shown in Figure 1 (panels A, B, and C, respectively). The figure demonstrates a linear relationship with both health literacy (Figure 1A) and cognition (Figure 1B), and a nonlinear relationship between number of preadmission medications and MUQ score (Figure 1C).

Figure 1

Adjusted relationships using imputed data for missing covariates are shown in Figure 2. Lower health literacy, cognitive impairment, male gender, and black race were independently associated with lower understanding of preadmission medications. Each 1 point increase in s‐TOFHLA or Mini‐Cog score led to an increase in medication understanding (OR = 1.04; 95% CI, 1.02 to 1.06; P = 0.0001; and OR = 1.24; 95% CI, 1.1 to 1.4; P = 0.001; respectively). Patients with marginal or inadequate health literacy had lower odds of understanding their regimen (OR = 0.53; 95% CI, 0.34 to 0.84; and OR = 0.49; 95% CI, 0.31 to 0.78, respectively) compared to those with adequate health literacy. Impaired cognitive function (Mini‐Cog score <3, indicating dementia) was also associated with lower odds of medication understanding (OR = 0.57; 95% CI, 0.38 to 0.86) compared to those with no cognitive impairment. An increase in the number of preadmission medications (up to 10) was also strongly associated with lower MUQ scores. For each increase by 1 medication, there was a significant decrease in medication understanding, up to 10 medications, beyond which understanding did not significantly decrease further. Patients on 6 medications were about half as likely to understand their medication regimen as patients on only 1 medication (OR = 0.52; 95% CI, 0.36 to 0.75). For patients on 11 medications, the odds of medication understanding were 24% lower than for patients on 6 medications (OR = 0.76; 95% CI, 0.65 to 0.89). Patients' age, years of schooling, and household income were not independently associated with medication understanding. Results were similar using data without multiple imputation.

Figure 2

DISCUSSION

We used a novel four‐component medication understanding questionnaire, developed for this study, to assess patients' understanding of up to 5 drugs selected randomly from the participant's preadmission medication list. The MUQ proved to be easy to administer by nonmedical staff within a short period of time (approximately 5 minutes per patient). It was well understood by patients. By limiting the assessment to 5 or fewer medications, the MUQ has a distinct advantage over existing measures of medication understanding that require testing the entire regimen. We did not find any limitations related to cutting off the assessment at 5 medications. In addition, this tool affords assessment of medication understanding without requiring medication bottles be present, enhancing its utility in the inpatient setting.

MUQ scores were associated with health literacy and other patient characteristics in an expected manner. We demonstrated that inadequate or marginal health literacy, as well as impaired cognitive function, were associated with low medication understanding. We also were able to demonstrate a relationship between increasing number of medications and lower medication understanding. Interestingly, in our patient population, understanding continued to decrease until reaching 10 medications, beyond which further increases in the number of medications had no additional detrimental effect on medication understanding. This nonlinear relationship between number of medications and medication understanding has potential implications for prescribing practice.

Our findings which utilize the MUQ among inpatients are consistent with prior literature in other settings.2, 7, 8 In a previous outpatient study, we identified that health literacy plays an important role in a patient's ability to successfully report and manage their daily medications.2 Other studies have also shown that patients with low health literacy have more difficulty understanding prescription drug information, and that they often experience medication‐related problems after hospital discharge.15, 16 The number and often the types of medications an individual takes have also been shown to increase the risk for adverse events and nonadherence to the treatment plan.1720 We postulate that this risk of adverse drug events is related at least in part to a patient's understanding of their medication regimen.

There are several limitations to this study. First, the MUQ did not assess certain aspects of medication understanding, such as knowledge of pill appearance or side effects, nor did it assess components of patients' actual drug‐taking behavior, such as organization of medications or behavioral cues. Thus, adaptive behaviors that patients may perform to improve their medication management, such as writing on labels or memory cues, are not captured by this test. Second, in administering and scoring the MUQ, we used the patient's preadmission medication list documented in the EHR as the reference standard. This was the best available reference list, and was generally accurate, as both hospitals had medication reconciliation systems in use at the time of the study21; nevertheless, it may contain inaccuracies. Documentation for certain medications, such as warfarin, in which dose can change frequently, often did not reflect the latest prescribed dose. In such cases, we scored the patient's answer as correct if the dose appeared reasonable and appropriate to the clinical pharmacist. As a result, a patient's MUQ score may have been overestimated in these cases.

Additional research will be needed to further validate the MUQ in other settings. In particular, studies should establish the relationship between the MUQ, serious medication errors after discharge, and potential to benefit from educational interventions. Also, as noted above, the nonlinear relationship between number of medications and medication understanding should be confirmed in other studies.

In conclusion, we demonstrated that patients with low health literacy, impaired cognition, or a higher number of medications had significantly poorer understanding of their preadmission medication regimen. These findings have important clinical implications. It would be appropriate to exercise greater caution when taking a medication history from patients who cannot readily provide the purpose, strength, units, and frequency of their medications. Attempts to validate the information obtained from patients with other sources of data, such as family members, inpatient or outpatient health records, and community pharmacy records should be considered. Patients at high risk for poor medication understanding, either measured directly using the MUQ or identified via risk factors such as polypharmacy, low cognition, or low health literacy, may warrant more intensive medication reconciliation interventions and/or educational counseling and follow‐up to prevent postdischarge adverse drug events. Further research is needed to determine if targeting these populations for interventions improves medication safety during transitions in care.

With the aging of the US population, complex medication regimens to treat multiple comorbidities are increasingly common.1 Nevertheless, patients often do not fully understand the instructions for safe and effective medication use. Aspects of medication understanding include knowledge of the drug indication, dose, frequency, and for certain medications, special instructions.2 Medication understanding is associated with better medication adherence, fewer drug‐related problems, and fewer emergency department visits.3 Among patients with chronic conditions, such as cardiovascular disease (CVD), understanding and adherence to the medication regimen are critical for successful disease control and clinical outcomes.4

Patients' understanding of their medication regimen is also vitally important upon admission to the hospital. Patients often are the main source of information for the admission medication history and subsequent medication reconciliation.5 Poor patient understanding of the preadmission medication regimen can contribute to errors in inpatient and postdischarge medication orders, and adversely affect patient safety.6 However, little research has examined patients' understanding of the preadmission medication regimen and factors that affect it.

In the outpatient setting, previous investigations have suggested that low health literacy, advanced age, and impaired cognitive function adversely affect patients' understanding of medication instructions.2, 7, 8 These studies were limited by a small sample size, single site, or focus on a specific population, such as inner‐city patients. Additionally, the measures used to assess medication understanding were time‐consuming and required patients' medications to be present for testing, thus limiting their utility.2

To address these gaps in the literature, we developed and implemented the Medication Understanding Questionnaire (MUQ), an original and relatively rapid measure that does not require patients' medications be present for testing. In a study of adults at 2 large teaching hospitals, we examined the association of health literacy, age, cognitive function, number of preadmission medications, and other factors on patients' understanding of their preadmission medication regimen. We hypothesized that lower health literacy would be independently associated with lower medication understanding as assessed using the MUQ.

METHODS

The present study was a cross‐sectional assessment conducted using baseline interview data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) Study (ClinicalTrials.gov Registration #NCT00632021; available at: http://clinicaltrials.gov/show/NCT00632021). The PILL‐CVD Study is a randomized controlled trial of a pharmacist‐based intervention, consisting of pharmacist‐assisted medication reconciliation, inpatient counseling, low‐literacy adherence aids, and postdischarge telephone follow‐up. It was conducted at 2 academic medical centersVanderbilt University Hospital (VUH) in Nashville, Tennessee, and Brigham and Women's Hospital (BWH) in Boston, Massachusetts.9 This study was approved by the Institutional Review Board at each site, and all participants provided written informed consent.

RESULTS

Predictors of Medication Understanding

Unadjusted relationships of health literacy, cognition, and number of medications with medication understanding are shown in Figure 1 (panels A, B, and C, respectively). The figure demonstrates a linear relationship with both health literacy (Figure 1A) and cognition (Figure 1B), and a nonlinear relationship between number of preadmission medications and MUQ score (Figure 1C).

Figure 1

Adjusted relationships using imputed data for missing covariates are shown in Figure 2. Lower health literacy, cognitive impairment, male gender, and black race were independently associated with lower understanding of preadmission medications. Each 1 point increase in s‐TOFHLA or Mini‐Cog score led to an increase in medication understanding (OR = 1.04; 95% CI, 1.02 to 1.06; P = 0.0001; and OR = 1.24; 95% CI, 1.1 to 1.4; P = 0.001; respectively). Patients with marginal or inadequate health literacy had lower odds of understanding their regimen (OR = 0.53; 95% CI, 0.34 to 0.84; and OR = 0.49; 95% CI, 0.31 to 0.78, respectively) compared to those with adequate health literacy. Impaired cognitive function (Mini‐Cog score <3, indicating dementia) was also associated with lower odds of medication understanding (OR = 0.57; 95% CI, 0.38 to 0.86) compared to those with no cognitive impairment. An increase in the number of preadmission medications (up to 10) was also strongly associated with lower MUQ scores. For each increase by 1 medication, there was a significant decrease in medication understanding, up to 10 medications, beyond which understanding did not significantly decrease further. Patients on 6 medications were about half as likely to understand their medication regimen as patients on only 1 medication (OR = 0.52; 95% CI, 0.36 to 0.75). For patients on 11 medications, the odds of medication understanding were 24% lower than for patients on 6 medications (OR = 0.76; 95% CI, 0.65 to 0.89). Patients' age, years of schooling, and household income were not independently associated with medication understanding. Results were similar using data without multiple imputation.

Figure 2

DISCUSSION

We used a novel four‐component medication understanding questionnaire, developed for this study, to assess patients' understanding of up to 5 drugs selected randomly from the participant's preadmission medication list. The MUQ proved to be easy to administer by nonmedical staff within a short period of time (approximately 5 minutes per patient). It was well understood by patients. By limiting the assessment to 5 or fewer medications, the MUQ has a distinct advantage over existing measures of medication understanding that require testing the entire regimen. We did not find any limitations related to cutting off the assessment at 5 medications. In addition, this tool affords assessment of medication understanding without requiring medication bottles be present, enhancing its utility in the inpatient setting.

MUQ scores were associated with health literacy and other patient characteristics in an expected manner. We demonstrated that inadequate or marginal health literacy, as well as impaired cognitive function, were associated with low medication understanding. We also were able to demonstrate a relationship between increasing number of medications and lower medication understanding. Interestingly, in our patient population, understanding continued to decrease until reaching 10 medications, beyond which further increases in the number of medications had no additional detrimental effect on medication understanding. This nonlinear relationship between number of medications and medication understanding has potential implications for prescribing practice.

Our findings which utilize the MUQ among inpatients are consistent with prior literature in other settings.2, 7, 8 In a previous outpatient study, we identified that health literacy plays an important role in a patient's ability to successfully report and manage their daily medications.2 Other studies have also shown that patients with low health literacy have more difficulty understanding prescription drug information, and that they often experience medication‐related problems after hospital discharge.15, 16 The number and often the types of medications an individual takes have also been shown to increase the risk for adverse events and nonadherence to the treatment plan.1720 We postulate that this risk of adverse drug events is related at least in part to a patient's understanding of their medication regimen.

There are several limitations to this study. First, the MUQ did not assess certain aspects of medication understanding, such as knowledge of pill appearance or side effects, nor did it assess components of patients' actual drug‐taking behavior, such as organization of medications or behavioral cues. Thus, adaptive behaviors that patients may perform to improve their medication management, such as writing on labels or memory cues, are not captured by this test. Second, in administering and scoring the MUQ, we used the patient's preadmission medication list documented in the EHR as the reference standard. This was the best available reference list, and was generally accurate, as both hospitals had medication reconciliation systems in use at the time of the study21; nevertheless, it may contain inaccuracies. Documentation for certain medications, such as warfarin, in which dose can change frequently, often did not reflect the latest prescribed dose. In such cases, we scored the patient's answer as correct if the dose appeared reasonable and appropriate to the clinical pharmacist. As a result, a patient's MUQ score may have been overestimated in these cases.

Additional research will be needed to further validate the MUQ in other settings. In particular, studies should establish the relationship between the MUQ, serious medication errors after discharge, and potential to benefit from educational interventions. Also, as noted above, the nonlinear relationship between number of medications and medication understanding should be confirmed in other studies.

In conclusion, we demonstrated that patients with low health literacy, impaired cognition, or a higher number of medications had significantly poorer understanding of their preadmission medication regimen. These findings have important clinical implications. It would be appropriate to exercise greater caution when taking a medication history from patients who cannot readily provide the purpose, strength, units, and frequency of their medications. Attempts to validate the information obtained from patients with other sources of data, such as family members, inpatient or outpatient health records, and community pharmacy records should be considered. Patients at high risk for poor medication understanding, either measured directly using the MUQ or identified via risk factors such as polypharmacy, low cognition, or low health literacy, may warrant more intensive medication reconciliation interventions and/or educational counseling and follow‐up to prevent postdischarge adverse drug events. Further research is needed to determine if targeting these populations for interventions improves medication safety during transitions in care.

With the aging of the US population, complex medication regimens to treat multiple comorbidities are increasingly common.1 Nevertheless, patients often do not fully understand the instructions for safe and effective medication use. Aspects of medication understanding include knowledge of the drug indication, dose, frequency, and for certain medications, special instructions.2 Medication understanding is associated with better medication adherence, fewer drug‐related problems, and fewer emergency department visits.3 Among patients with chronic conditions, such as cardiovascular disease (CVD), understanding and adherence to the medication regimen are critical for successful disease control and clinical outcomes.4

Patients' understanding of their medication regimen is also vitally important upon admission to the hospital. Patients often are the main source of information for the admission medication history and subsequent medication reconciliation.5 Poor patient understanding of the preadmission medication regimen can contribute to errors in inpatient and postdischarge medication orders, and adversely affect patient safety.6 However, little research has examined patients' understanding of the preadmission medication regimen and factors that affect it.

In the outpatient setting, previous investigations have suggested that low health literacy, advanced age, and impaired cognitive function adversely affect patients' understanding of medication instructions.2, 7, 8 These studies were limited by a small sample size, single site, or focus on a specific population, such as inner‐city patients. Additionally, the measures used to assess medication understanding were time‐consuming and required patients' medications to be present for testing, thus limiting their utility.2

To address these gaps in the literature, we developed and implemented the Medication Understanding Questionnaire (MUQ), an original and relatively rapid measure that does not require patients' medications be present for testing. In a study of adults at 2 large teaching hospitals, we examined the association of health literacy, age, cognitive function, number of preadmission medications, and other factors on patients' understanding of their preadmission medication regimen. We hypothesized that lower health literacy would be independently associated with lower medication understanding as assessed using the MUQ.

METHODS

The present study was a cross‐sectional assessment conducted using baseline interview data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) Study (ClinicalTrials.gov Registration #NCT00632021; available at: http://clinicaltrials.gov/show/NCT00632021). The PILL‐CVD Study is a randomized controlled trial of a pharmacist‐based intervention, consisting of pharmacist‐assisted medication reconciliation, inpatient counseling, low‐literacy adherence aids, and postdischarge telephone follow‐up. It was conducted at 2 academic medical centersVanderbilt University Hospital (VUH) in Nashville, Tennessee, and Brigham and Women's Hospital (BWH) in Boston, Massachusetts.9 This study was approved by the Institutional Review Board at each site, and all participants provided written informed consent.

RESULTS

Predictors of Medication Understanding

Unadjusted relationships of health literacy, cognition, and number of medications with medication understanding are shown in Figure 1 (panels A, B, and C, respectively). The figure demonstrates a linear relationship with both health literacy (Figure 1A) and cognition (Figure 1B), and a nonlinear relationship between number of preadmission medications and MUQ score (Figure 1C).

Figure 1

Adjusted relationships using imputed data for missing covariates are shown in Figure 2. Lower health literacy, cognitive impairment, male gender, and black race were independently associated with lower understanding of preadmission medications. Each 1 point increase in s‐TOFHLA or Mini‐Cog score led to an increase in medication understanding (OR = 1.04; 95% CI, 1.02 to 1.06; P = 0.0001; and OR = 1.24; 95% CI, 1.1 to 1.4; P = 0.001; respectively). Patients with marginal or inadequate health literacy had lower odds of understanding their regimen (OR = 0.53; 95% CI, 0.34 to 0.84; and OR = 0.49; 95% CI, 0.31 to 0.78, respectively) compared to those with adequate health literacy. Impaired cognitive function (Mini‐Cog score <3, indicating dementia) was also associated with lower odds of medication understanding (OR = 0.57; 95% CI, 0.38 to 0.86) compared to those with no cognitive impairment. An increase in the number of preadmission medications (up to 10) was also strongly associated with lower MUQ scores. For each increase by 1 medication, there was a significant decrease in medication understanding, up to 10 medications, beyond which understanding did not significantly decrease further. Patients on 6 medications were about half as likely to understand their medication regimen as patients on only 1 medication (OR = 0.52; 95% CI, 0.36 to 0.75). For patients on 11 medications, the odds of medication understanding were 24% lower than for patients on 6 medications (OR = 0.76; 95% CI, 0.65 to 0.89). Patients' age, years of schooling, and household income were not independently associated with medication understanding. Results were similar using data without multiple imputation.

Figure 2

DISCUSSION

We used a novel four‐component medication understanding questionnaire, developed for this study, to assess patients' understanding of up to 5 drugs selected randomly from the participant's preadmission medication list. The MUQ proved to be easy to administer by nonmedical staff within a short period of time (approximately 5 minutes per patient). It was well understood by patients. By limiting the assessment to 5 or fewer medications, the MUQ has a distinct advantage over existing measures of medication understanding that require testing the entire regimen. We did not find any limitations related to cutting off the assessment at 5 medications. In addition, this tool affords assessment of medication understanding without requiring medication bottles be present, enhancing its utility in the inpatient setting.

MUQ scores were associated with health literacy and other patient characteristics in an expected manner. We demonstrated that inadequate or marginal health literacy, as well as impaired cognitive function, were associated with low medication understanding. We also were able to demonstrate a relationship between increasing number of medications and lower medication understanding. Interestingly, in our patient population, understanding continued to decrease until reaching 10 medications, beyond which further increases in the number of medications had no additional detrimental effect on medication understanding. This nonlinear relationship between number of medications and medication understanding has potential implications for prescribing practice.

Our findings which utilize the MUQ among inpatients are consistent with prior literature in other settings.2, 7, 8 In a previous outpatient study, we identified that health literacy plays an important role in a patient's ability to successfully report and manage their daily medications.2 Other studies have also shown that patients with low health literacy have more difficulty understanding prescription drug information, and that they often experience medication‐related problems after hospital discharge.15, 16 The number and often the types of medications an individual takes have also been shown to increase the risk for adverse events and nonadherence to the treatment plan.1720 We postulate that this risk of adverse drug events is related at least in part to a patient's understanding of their medication regimen.

There are several limitations to this study. First, the MUQ did not assess certain aspects of medication understanding, such as knowledge of pill appearance or side effects, nor did it assess components of patients' actual drug‐taking behavior, such as organization of medications or behavioral cues. Thus, adaptive behaviors that patients may perform to improve their medication management, such as writing on labels or memory cues, are not captured by this test. Second, in administering and scoring the MUQ, we used the patient's preadmission medication list documented in the EHR as the reference standard. This was the best available reference list, and was generally accurate, as both hospitals had medication reconciliation systems in use at the time of the study21; nevertheless, it may contain inaccuracies. Documentation for certain medications, such as warfarin, in which dose can change frequently, often did not reflect the latest prescribed dose. In such cases, we scored the patient's answer as correct if the dose appeared reasonable and appropriate to the clinical pharmacist. As a result, a patient's MUQ score may have been overestimated in these cases.

Additional research will be needed to further validate the MUQ in other settings. In particular, studies should establish the relationship between the MUQ, serious medication errors after discharge, and potential to benefit from educational interventions. Also, as noted above, the nonlinear relationship between number of medications and medication understanding should be confirmed in other studies.

In conclusion, we demonstrated that patients with low health literacy, impaired cognition, or a higher number of medications had significantly poorer understanding of their preadmission medication regimen. These findings have important clinical implications. It would be appropriate to exercise greater caution when taking a medication history from patients who cannot readily provide the purpose, strength, units, and frequency of their medications. Attempts to validate the information obtained from patients with other sources of data, such as family members, inpatient or outpatient health records, and community pharmacy records should be considered. Patients at high risk for poor medication understanding, either measured directly using the MUQ or identified via risk factors such as polypharmacy, low cognition, or low health literacy, may warrant more intensive medication reconciliation interventions and/or educational counseling and follow‐up to prevent postdischarge adverse drug events. Further research is needed to determine if targeting these populations for interventions improves medication safety during transitions in care.

Diabetes mellitus and/or inpatient hyperglycemia are common comorbid conditions in hospitalized patients. Recent surveys show that over 90% of hospitalized diabetic patients experience hyperglycemia (>200 mg/dL), and in nearly 1 in 5 of these patients hyperglycemia persists for 3 days or more.1 Hyperglycemia among inpatients without a previous history of diabetes mellitus is also very common.2 Observational studies have shown that hyperglycemia in hospitalized patients is associated with adverse outcomes including infectious complications, increased length of stay, and increased mortality.27 Recent randomized controlled trials have demonstrated that aggressive treatment of inpatient hyperglycemia improves outcomes in surgical and medical intensive care units.8, 9

Based on the available data, the American Diabetes Association (ADA) now advocates good metabolic control, defined as preprandial glucose levels of 90 to 130 mg/dL and peak postprandial glucose levels <180 mg/dL in hospitalized nonintensive care unit (ICU) patients.10 To reach these targets, the ADA and American College of Endocrinology (ACE) suggest that multidisciplinary teams develop and implement hyperglycemia management guidelines and protocols.11 Protocols should promote the use of continuous intravenous insulin infusions or scheduled basal‐bolus subcutaneous insulin regimens. Subcutaneous insulin protocols should include target glucose levels, basal, nutritional, and supplemental insulin, and daily dose adjustments.6 A recent randomized controlled trial of non‐ICU inpatients demonstrated that such a basal‐bolus insulin regimen results in improved glucose control compared with a sliding scale only regimen.12

To date, few published studies have investigated the best ways to implement such management protocols; those that have are often resource‐intensive, for example involving daily involvement of nurse practitioners or diabetologists.13, 14 It is therefore not known how best to implement an inpatient diabetes management program that is effective, efficient, and self‐perpetuating. At Brigham and Women's Hospital (BWH), we have been refining a subcutaneous insulin protocol, focused provider education, and more recently a computerized order set to overcome barriers related to fear of hypoglycemia, delays in insulin prescribing, and unfamiliarity with inpatient glucose management.15 The aims of this current trial were to evaluate the effects of these interventions on a geographically localized general medical service previously naive to these interventions to evaluate their effects on glycemic control, patient safety, and processes of care. We hypothesized that these interventions would improve glycemic control and increase use of basal‐bolus insulin orders without increasing the rate of hypoglycemia.

METHODS

RESULTS

We prospectively identified 248 potential patients for the study. We subsequently excluded 79 patients for the following reasons: no glucose readings beyond hospital day 1 while on PACE service (34 patients); never admitted to PACE service (15 patients); no diabetes or inpatient hyperglycemia (9 patients, mostly patients prescribed an insulin sliding scale prophylactically to avoid steroid‐induced hyperglycemia); type 1 diabetes (13 patients); TPN, DKA, or HHS (5 patients); and palliative care (3 patients). The remaining 169 patients included 63 from the preintervention period(out of 489 total admissions to the PACE service; 13%) and 106 patients in the postintervention period (out of 565 admissions; 19%). These patients had 2447 glucose readings, or an average of 3.6 glucose readings per monitored patient‐day in the preintervention period and 3.3 glucose readings per patient‐day in the postintervention period. Even including the 34 patients who were excluded for lack of glucose readings, glucose data were still available for 717 out of a potential 775 patient‐days (93%). Characteristics for all included patients are shown in Table 2. The mean admission glucose was 197 mg/dL, mean A1C was 8.4%, 54% of the patients were prescribed insulin prior to admission, and 7% had no prior diagnosis of diabetes. There were no significant differences in baseline characteristics between the 2 patient groups except for Charlson score, which was higher in the preintervention group (87% versus 74% with score 2 or higher; Table 2). The top diagnosis‐related groups for the entire cohort included: heart failure and shock (12 patients); kidney and urinary tract infections (12 patients); esophagitis, gastroenteritis, and miscellaneous digestive disorders (11 patients); chronic obstructive pulmonary disease (10 patients); renal failure (10 patients); simple pneumonia and pleurisy (7 patients); disorders of the pancreas except malignancy (6 patients); chest pain (5 patients); and cellulitis (5 patients).

With respect to insulin ordering practices, there was no significant difference in the use of basal insulin in hyperglycemic patients between the preintervention period and postintervention period (81% versus 91%; P = 0.17), nor in the dose of basal insulin prescribed (results not shown), but there was an increase in the use of scheduled nutritional insulin for those patients with hyperglycemia receiving nutrition: 40% versus 75%, P < 0.001 (Table 3). The percent of patients receiving supplemental (sliding scale) insulin by itself (ie, without ever receiving basal or nutritional insulin) was lower during the postintervention period (29% versus 8%, P < 0.001). Nonsignificant differences were seen in the rates of prescribing an appropriate dose and type of nutritional insulin. Notably, there was no difference at all in the proportion of patient‐days in which insulin adjustments were made when 2 or more episodes of hyperglycemia or hypoglycemia were present during the previous day (56% of patient‐days in both groups; P = 0.90).

The primary outcome, the mean percent of glucose readings between 60 and 180 mg/dL per patient, was 59.1% in the preintervention period and 64.7% in the postintervention (P = 0.13 in unadjusted analysis; Table 3). When adjusted for A1C, admission glucose, and insulin use prior to admission, the adjusted absolute difference in the percent of glucose readings within range was 9.7% (95% confidence interval [CI], 0.6%‐18.8%; P = 0.04; Table 3). Regarding other measures of glucose control, the patient‐day weighted mean glucose was 174.7 mg/dL in the preintervention period and 164.6 mg/dL postintervention (P = 0.02), and there was no significant difference in the percent of patient‐days with any hypoglycemia (glucose <60 mg/dL) or severe hypoglycemia (glucose <40 mg/dL; Table 3). There were also no significant differences in the mean number of hypoglycemic events per patient‐day (6.8 versus 6.6 per 100 patient‐days; relative risk, 0.95; 95% CI, 0.541.67; P = 0.87) or severe hypoglycemic events per patient‐day (1.0 versus 1.4 per 100 patient‐days; relative risk, 1.38; 95% CI, 0.355.53; P = 0.65).

We also compared hospital length of stay in hours between the study groups (Table 3). Length of stay (LOS) was shorter in the postintervention arm in unadjusted analyses (112 versus 86 hours; P < 0.001), and this difference persisted when adjusted for patient insurance, race, gender, and Charlson comorbidity score (25% shorter; 95% CI, 6%‐44%). A comparison of LOS among nonstudy patients on the PACE service during these 2 time periods revealed no difference (105 versus 101 hours). When the length of stay analysis was limited to study patients with a known diagnosis of diabetes, the adjusted effect size was a 31% relative decrease in length of stay.

Figure 1A shows the percent glucose readings within range per patient by hospital day. The greatest differences between groups can be seen on hospital days 2 and 3 (11% absolute differences on both days). Similarly, Figure 1B shows the mean glucose per patient by hospital day. Again, the biggest differences are seen on hospital days 2 and 3 (20 and 23 mg/dL difference between groups, respectively). In both cases, only the day 3 comparisons were significantly different between study groups.

Figure 1

DISCUSSION

In this before‐after study, we found that a multifaceted intervention consisting of a subcutaneous insulin protocol, focused education, and an order set built into the hospital's CPOE system was associated with a significantly higher percentage of glucose readings within range per patient in analyses adjusted for patient demographics and severity of diabetes. We also found a significant decrease in patient‐day weighted mean glucose, a marked increase in appropriate use of scheduled nutritional insulin, and a concomitant decrease in sliding scale insulin only regimens during the postintervention period. Moreover, we found a shorter length of stay during the postintervention period that persisted after adjustment for several clinical factors. Importantly, the interventions described in this study require very few resources to continue indefinitely: printing costs for the management protocol, 4 hours of education delivered per year, and routine upkeep of an electronic order set.

Because this was a before‐after study, we cannot exclude the possibility that these improvements in process and outcome were due to cointerventions and/or temporal trends. However, we know of no other interventions aimed at improving diabetes care in this self‐contained service of nurses, PAs, and hospitalists. Moreover, the process improvements, especially the increase in scheduled nutritional insulin, were rather marked, unlikely to be due to temporal trends alone, and likely capable of producing the corresponding improvements in glucose control. That glucose control stopped improving after hospital day 3 may be due to the fact that subsequent adjustment to insulin orders occurred infrequently and no more often than prior to the intervention. That we did not see greater improvements in glycemic control overall may also reflect the fact that 81% of study patients with inpatient hyperglycemia received basal insulin prior to the intervention.

The reduction in patient LOS was somewhat surprising given the relatively small sample size. However, the results are consistent with those of other studies linking hyperglycemia to LOS18, 19 and we found no evidence for a temporal trend toward lower LOS on the PACE service as a whole during the same time period. While a greater proportion of patients on the PACE service were in the study in the post‐intervention period compared with the preintervention period, we found no evidence that the difference in length of stay was due to increased surveillance for nondiabetics, especially because eligibility criteria depended on phlebotomy glucose values, which were uniformly tested in all inpatients. Also, effects on length of stay were actually stronger when limited to patients with known diabetes. Finally, we controlled for several predictors of length of stay, although we still cannot exclude the possibility of unmeasured confounding between groups.

Since ADA and ACE issued guidelines for inpatient management of diabetes and hyperglycemia, many institutions have developed subcutaneous insulin algorithms, educational curricula, and/or order sets to increase compliance with these guidelines and improve glycemic control. Some of these efforts have been studied and some have been successful in their efforts.13, 14, 2023 Unfortunately, most of these programs have not rigorously assessed their impact on process and outcomes, and the most effective studies published to date have involved interventions much more intensive than those described here. For example, Rush University's intervention was associated with a 50 mg/dL decrease in mean blood glucose but involved an endocrinologist rounding twice daily with house officers for 2 weeks at a time.13 At Northwestern University, a diabetes management service run by nurse practitioners was established, and the focus was on the conversion from intravenous to subcutaneous insulin regimens.14 The RABBIT 2 study that demonstrated the benefits of a basal‐bolus insulin regimen used daily rounding with an endocrinologist.12 More modestly, a program in Pitt County Memorial Hospital in Greenville, NC, relied mostly on diabetes nurse case managers, a strategy which reduced hospital‐wide mean glucose levels as well as LOS, although the greatest improvements in glycemic control were seen in the ICU.19 Our findings are much more consistent with those from University of California San Diego, as yet unpublished, which also used an algorithm, computerized order set, education, as well as continuous quality improvement methods to achieve its aims.22

Our study has several limitations, including being conducted on 1 general medicine service at 1 academic medical center. Moreover, this service, using a physician assistant/hospitalist model, a closed geographic unit, and fairly generous staffing ratio, is likely different from those in many settings and may limit the generalizability of our findings. However, this model allowed us to conduct the study in a laboratory relatively untouched by other cointerventions. Furthermore, the use of PAs in this way may become more common as both academic and community hospitals rely more on mid‐level providers. Our study had a relatively low percentage of patients without a known diagnosis of diabetes compared with other studies, again potentially but not necessarily limiting generalizability. This finding has been shown in other studies at our institution24 and may be due to the high rate of screening for diabetes in the community. Another limitation is that this was a nonrandomized, before‐after trial. However, all subjects were prospectively enrolled to improve comparability, and we performed rigorous adjustment for multiple potential confounding factors. Also, this study had limited statistical power to detect differences in hypoglycemia rates. The preintervention arm was smaller than planned due to fewer diabetic patients than expected on the service and a higher number of exclusions; we prolonged the postintervention period to achieve the desired sample size for that arm of the study.

Our study also has several strengths, including electronic capture of many processes of care and a methodology to operationalize them into measures of protocol adherence. Our metrics of glycemic control were rigorously designed and based on a national task force on inpatient glycemic control sponsored by the Society of Hospital Medicine, with representation from the ADA and AACE.25

Potential future improvements to this intervention include modifications to the daily adjustment algorithm to improve its usability and ability to improve glucose control. Another is the use of high‐reliability methods to improve order set use and daily insulin adjustment, including alerts within the CPOE system and nurse empowerment to contact medical teams if glucose levels are out of range (eg, if greater than 180 mg/dL, not just if greater than 350 or 400 mg/dL). Future research directions include multicenter, randomized controlled trials of these types of interventions and an analysis of more distal patient outcomes including total healthcare utilization, infection rates, end‐organ damage, and mortality.

In conclusion, we found a relationship between a relatively low‐cost quality improvement intervention and improved glycemic control in the non‐ICU general medical setting. Such a finding suggests the benefits of the algorithm itself to improve glucose control and of our implementation strategy. Other institutions may find this intervention a useful starting point for their own quality improvement efforts. Both the algorithm and implementation strategy are deserving of further improvements and future study.

Diabetes mellitus and/or inpatient hyperglycemia are common comorbid conditions in hospitalized patients. Recent surveys show that over 90% of hospitalized diabetic patients experience hyperglycemia (>200 mg/dL), and in nearly 1 in 5 of these patients hyperglycemia persists for 3 days or more.1 Hyperglycemia among inpatients without a previous history of diabetes mellitus is also very common.2 Observational studies have shown that hyperglycemia in hospitalized patients is associated with adverse outcomes including infectious complications, increased length of stay, and increased mortality.27 Recent randomized controlled trials have demonstrated that aggressive treatment of inpatient hyperglycemia improves outcomes in surgical and medical intensive care units.8, 9

Based on the available data, the American Diabetes Association (ADA) now advocates good metabolic control, defined as preprandial glucose levels of 90 to 130 mg/dL and peak postprandial glucose levels <180 mg/dL in hospitalized nonintensive care unit (ICU) patients.10 To reach these targets, the ADA and American College of Endocrinology (ACE) suggest that multidisciplinary teams develop and implement hyperglycemia management guidelines and protocols.11 Protocols should promote the use of continuous intravenous insulin infusions or scheduled basal‐bolus subcutaneous insulin regimens. Subcutaneous insulin protocols should include target glucose levels, basal, nutritional, and supplemental insulin, and daily dose adjustments.6 A recent randomized controlled trial of non‐ICU inpatients demonstrated that such a basal‐bolus insulin regimen results in improved glucose control compared with a sliding scale only regimen.12

To date, few published studies have investigated the best ways to implement such management protocols; those that have are often resource‐intensive, for example involving daily involvement of nurse practitioners or diabetologists.13, 14 It is therefore not known how best to implement an inpatient diabetes management program that is effective, efficient, and self‐perpetuating. At Brigham and Women's Hospital (BWH), we have been refining a subcutaneous insulin protocol, focused provider education, and more recently a computerized order set to overcome barriers related to fear of hypoglycemia, delays in insulin prescribing, and unfamiliarity with inpatient glucose management.15 The aims of this current trial were to evaluate the effects of these interventions on a geographically localized general medical service previously naive to these interventions to evaluate their effects on glycemic control, patient safety, and processes of care. We hypothesized that these interventions would improve glycemic control and increase use of basal‐bolus insulin orders without increasing the rate of hypoglycemia.

METHODS

RESULTS

We prospectively identified 248 potential patients for the study. We subsequently excluded 79 patients for the following reasons: no glucose readings beyond hospital day 1 while on PACE service (34 patients); never admitted to PACE service (15 patients); no diabetes or inpatient hyperglycemia (9 patients, mostly patients prescribed an insulin sliding scale prophylactically to avoid steroid‐induced hyperglycemia); type 1 diabetes (13 patients); TPN, DKA, or HHS (5 patients); and palliative care (3 patients). The remaining 169 patients included 63 from the preintervention period(out of 489 total admissions to the PACE service; 13%) and 106 patients in the postintervention period (out of 565 admissions; 19%). These patients had 2447 glucose readings, or an average of 3.6 glucose readings per monitored patient‐day in the preintervention period and 3.3 glucose readings per patient‐day in the postintervention period. Even including the 34 patients who were excluded for lack of glucose readings, glucose data were still available for 717 out of a potential 775 patient‐days (93%). Characteristics for all included patients are shown in Table 2. The mean admission glucose was 197 mg/dL, mean A1C was 8.4%, 54% of the patients were prescribed insulin prior to admission, and 7% had no prior diagnosis of diabetes. There were no significant differences in baseline characteristics between the 2 patient groups except for Charlson score, which was higher in the preintervention group (87% versus 74% with score 2 or higher; Table 2). The top diagnosis‐related groups for the entire cohort included: heart failure and shock (12 patients); kidney and urinary tract infections (12 patients); esophagitis, gastroenteritis, and miscellaneous digestive disorders (11 patients); chronic obstructive pulmonary disease (10 patients); renal failure (10 patients); simple pneumonia and pleurisy (7 patients); disorders of the pancreas except malignancy (6 patients); chest pain (5 patients); and cellulitis (5 patients).

With respect to insulin ordering practices, there was no significant difference in the use of basal insulin in hyperglycemic patients between the preintervention period and postintervention period (81% versus 91%; P = 0.17), nor in the dose of basal insulin prescribed (results not shown), but there was an increase in the use of scheduled nutritional insulin for those patients with hyperglycemia receiving nutrition: 40% versus 75%, P < 0.001 (Table 3). The percent of patients receiving supplemental (sliding scale) insulin by itself (ie, without ever receiving basal or nutritional insulin) was lower during the postintervention period (29% versus 8%, P < 0.001). Nonsignificant differences were seen in the rates of prescribing an appropriate dose and type of nutritional insulin. Notably, there was no difference at all in the proportion of patient‐days in which insulin adjustments were made when 2 or more episodes of hyperglycemia or hypoglycemia were present during the previous day (56% of patient‐days in both groups; P = 0.90).

The primary outcome, the mean percent of glucose readings between 60 and 180 mg/dL per patient, was 59.1% in the preintervention period and 64.7% in the postintervention (P = 0.13 in unadjusted analysis; Table 3). When adjusted for A1C, admission glucose, and insulin use prior to admission, the adjusted absolute difference in the percent of glucose readings within range was 9.7% (95% confidence interval [CI], 0.6%‐18.8%; P = 0.04; Table 3). Regarding other measures of glucose control, the patient‐day weighted mean glucose was 174.7 mg/dL in the preintervention period and 164.6 mg/dL postintervention (P = 0.02), and there was no significant difference in the percent of patient‐days with any hypoglycemia (glucose <60 mg/dL) or severe hypoglycemia (glucose <40 mg/dL; Table 3). There were also no significant differences in the mean number of hypoglycemic events per patient‐day (6.8 versus 6.6 per 100 patient‐days; relative risk, 0.95; 95% CI, 0.541.67; P = 0.87) or severe hypoglycemic events per patient‐day (1.0 versus 1.4 per 100 patient‐days; relative risk, 1.38; 95% CI, 0.355.53; P = 0.65).

We also compared hospital length of stay in hours between the study groups (Table 3). Length of stay (LOS) was shorter in the postintervention arm in unadjusted analyses (112 versus 86 hours; P < 0.001), and this difference persisted when adjusted for patient insurance, race, gender, and Charlson comorbidity score (25% shorter; 95% CI, 6%‐44%). A comparison of LOS among nonstudy patients on the PACE service during these 2 time periods revealed no difference (105 versus 101 hours). When the length of stay analysis was limited to study patients with a known diagnosis of diabetes, the adjusted effect size was a 31% relative decrease in length of stay.

Figure 1A shows the percent glucose readings within range per patient by hospital day. The greatest differences between groups can be seen on hospital days 2 and 3 (11% absolute differences on both days). Similarly, Figure 1B shows the mean glucose per patient by hospital day. Again, the biggest differences are seen on hospital days 2 and 3 (20 and 23 mg/dL difference between groups, respectively). In both cases, only the day 3 comparisons were significantly different between study groups.

Figure 1

DISCUSSION

In this before‐after study, we found that a multifaceted intervention consisting of a subcutaneous insulin protocol, focused education, and an order set built into the hospital's CPOE system was associated with a significantly higher percentage of glucose readings within range per patient in analyses adjusted for patient demographics and severity of diabetes. We also found a significant decrease in patient‐day weighted mean glucose, a marked increase in appropriate use of scheduled nutritional insulin, and a concomitant decrease in sliding scale insulin only regimens during the postintervention period. Moreover, we found a shorter length of stay during the postintervention period that persisted after adjustment for several clinical factors. Importantly, the interventions described in this study require very few resources to continue indefinitely: printing costs for the management protocol, 4 hours of education delivered per year, and routine upkeep of an electronic order set.

Because this was a before‐after study, we cannot exclude the possibility that these improvements in process and outcome were due to cointerventions and/or temporal trends. However, we know of no other interventions aimed at improving diabetes care in this self‐contained service of nurses, PAs, and hospitalists. Moreover, the process improvements, especially the increase in scheduled nutritional insulin, were rather marked, unlikely to be due to temporal trends alone, and likely capable of producing the corresponding improvements in glucose control. That glucose control stopped improving after hospital day 3 may be due to the fact that subsequent adjustment to insulin orders occurred infrequently and no more often than prior to the intervention. That we did not see greater improvements in glycemic control overall may also reflect the fact that 81% of study patients with inpatient hyperglycemia received basal insulin prior to the intervention.

The reduction in patient LOS was somewhat surprising given the relatively small sample size. However, the results are consistent with those of other studies linking hyperglycemia to LOS18, 19 and we found no evidence for a temporal trend toward lower LOS on the PACE service as a whole during the same time period. While a greater proportion of patients on the PACE service were in the study in the post‐intervention period compared with the preintervention period, we found no evidence that the difference in length of stay was due to increased surveillance for nondiabetics, especially because eligibility criteria depended on phlebotomy glucose values, which were uniformly tested in all inpatients. Also, effects on length of stay were actually stronger when limited to patients with known diabetes. Finally, we controlled for several predictors of length of stay, although we still cannot exclude the possibility of unmeasured confounding between groups.

Since ADA and ACE issued guidelines for inpatient management of diabetes and hyperglycemia, many institutions have developed subcutaneous insulin algorithms, educational curricula, and/or order sets to increase compliance with these guidelines and improve glycemic control. Some of these efforts have been studied and some have been successful in their efforts.13, 14, 2023 Unfortunately, most of these programs have not rigorously assessed their impact on process and outcomes, and the most effective studies published to date have involved interventions much more intensive than those described here. For example, Rush University's intervention was associated with a 50 mg/dL decrease in mean blood glucose but involved an endocrinologist rounding twice daily with house officers for 2 weeks at a time.13 At Northwestern University, a diabetes management service run by nurse practitioners was established, and the focus was on the conversion from intravenous to subcutaneous insulin regimens.14 The RABBIT 2 study that demonstrated the benefits of a basal‐bolus insulin regimen used daily rounding with an endocrinologist.12 More modestly, a program in Pitt County Memorial Hospital in Greenville, NC, relied mostly on diabetes nurse case managers, a strategy which reduced hospital‐wide mean glucose levels as well as LOS, although the greatest improvements in glycemic control were seen in the ICU.19 Our findings are much more consistent with those from University of California San Diego, as yet unpublished, which also used an algorithm, computerized order set, education, as well as continuous quality improvement methods to achieve its aims.22

Our study has several limitations, including being conducted on 1 general medicine service at 1 academic medical center. Moreover, this service, using a physician assistant/hospitalist model, a closed geographic unit, and fairly generous staffing ratio, is likely different from those in many settings and may limit the generalizability of our findings. However, this model allowed us to conduct the study in a laboratory relatively untouched by other cointerventions. Furthermore, the use of PAs in this way may become more common as both academic and community hospitals rely more on mid‐level providers. Our study had a relatively low percentage of patients without a known diagnosis of diabetes compared with other studies, again potentially but not necessarily limiting generalizability. This finding has been shown in other studies at our institution24 and may be due to the high rate of screening for diabetes in the community. Another limitation is that this was a nonrandomized, before‐after trial. However, all subjects were prospectively enrolled to improve comparability, and we performed rigorous adjustment for multiple potential confounding factors. Also, this study had limited statistical power to detect differences in hypoglycemia rates. The preintervention arm was smaller than planned due to fewer diabetic patients than expected on the service and a higher number of exclusions; we prolonged the postintervention period to achieve the desired sample size for that arm of the study.

Our study also has several strengths, including electronic capture of many processes of care and a methodology to operationalize them into measures of protocol adherence. Our metrics of glycemic control were rigorously designed and based on a national task force on inpatient glycemic control sponsored by the Society of Hospital Medicine, with representation from the ADA and AACE.25

Potential future improvements to this intervention include modifications to the daily adjustment algorithm to improve its usability and ability to improve glucose control. Another is the use of high‐reliability methods to improve order set use and daily insulin adjustment, including alerts within the CPOE system and nurse empowerment to contact medical teams if glucose levels are out of range (eg, if greater than 180 mg/dL, not just if greater than 350 or 400 mg/dL). Future research directions include multicenter, randomized controlled trials of these types of interventions and an analysis of more distal patient outcomes including total healthcare utilization, infection rates, end‐organ damage, and mortality.

In conclusion, we found a relationship between a relatively low‐cost quality improvement intervention and improved glycemic control in the non‐ICU general medical setting. Such a finding suggests the benefits of the algorithm itself to improve glucose control and of our implementation strategy. Other institutions may find this intervention a useful starting point for their own quality improvement efforts. Both the algorithm and implementation strategy are deserving of further improvements and future study.

Diabetes mellitus and/or inpatient hyperglycemia are common comorbid conditions in hospitalized patients. Recent surveys show that over 90% of hospitalized diabetic patients experience hyperglycemia (>200 mg/dL), and in nearly 1 in 5 of these patients hyperglycemia persists for 3 days or more.1 Hyperglycemia among inpatients without a previous history of diabetes mellitus is also very common.2 Observational studies have shown that hyperglycemia in hospitalized patients is associated with adverse outcomes including infectious complications, increased length of stay, and increased mortality.27 Recent randomized controlled trials have demonstrated that aggressive treatment of inpatient hyperglycemia improves outcomes in surgical and medical intensive care units.8, 9

Based on the available data, the American Diabetes Association (ADA) now advocates good metabolic control, defined as preprandial glucose levels of 90 to 130 mg/dL and peak postprandial glucose levels <180 mg/dL in hospitalized nonintensive care unit (ICU) patients.10 To reach these targets, the ADA and American College of Endocrinology (ACE) suggest that multidisciplinary teams develop and implement hyperglycemia management guidelines and protocols.11 Protocols should promote the use of continuous intravenous insulin infusions or scheduled basal‐bolus subcutaneous insulin regimens. Subcutaneous insulin protocols should include target glucose levels, basal, nutritional, and supplemental insulin, and daily dose adjustments.6 A recent randomized controlled trial of non‐ICU inpatients demonstrated that such a basal‐bolus insulin regimen results in improved glucose control compared with a sliding scale only regimen.12

To date, few published studies have investigated the best ways to implement such management protocols; those that have are often resource‐intensive, for example involving daily involvement of nurse practitioners or diabetologists.13, 14 It is therefore not known how best to implement an inpatient diabetes management program that is effective, efficient, and self‐perpetuating. At Brigham and Women's Hospital (BWH), we have been refining a subcutaneous insulin protocol, focused provider education, and more recently a computerized order set to overcome barriers related to fear of hypoglycemia, delays in insulin prescribing, and unfamiliarity with inpatient glucose management.15 The aims of this current trial were to evaluate the effects of these interventions on a geographically localized general medical service previously naive to these interventions to evaluate their effects on glycemic control, patient safety, and processes of care. We hypothesized that these interventions would improve glycemic control and increase use of basal‐bolus insulin orders without increasing the rate of hypoglycemia.

METHODS

RESULTS

We prospectively identified 248 potential patients for the study. We subsequently excluded 79 patients for the following reasons: no glucose readings beyond hospital day 1 while on PACE service (34 patients); never admitted to PACE service (15 patients); no diabetes or inpatient hyperglycemia (9 patients, mostly patients prescribed an insulin sliding scale prophylactically to avoid steroid‐induced hyperglycemia); type 1 diabetes (13 patients); TPN, DKA, or HHS (5 patients); and palliative care (3 patients). The remaining 169 patients included 63 from the preintervention period(out of 489 total admissions to the PACE service; 13%) and 106 patients in the postintervention period (out of 565 admissions; 19%). These patients had 2447 glucose readings, or an average of 3.6 glucose readings per monitored patient‐day in the preintervention period and 3.3 glucose readings per patient‐day in the postintervention period. Even including the 34 patients who were excluded for lack of glucose readings, glucose data were still available for 717 out of a potential 775 patient‐days (93%). Characteristics for all included patients are shown in Table 2. The mean admission glucose was 197 mg/dL, mean A1C was 8.4%, 54% of the patients were prescribed insulin prior to admission, and 7% had no prior diagnosis of diabetes. There were no significant differences in baseline characteristics between the 2 patient groups except for Charlson score, which was higher in the preintervention group (87% versus 74% with score 2 or higher; Table 2). The top diagnosis‐related groups for the entire cohort included: heart failure and shock (12 patients); kidney and urinary tract infections (12 patients); esophagitis, gastroenteritis, and miscellaneous digestive disorders (11 patients); chronic obstructive pulmonary disease (10 patients); renal failure (10 patients); simple pneumonia and pleurisy (7 patients); disorders of the pancreas except malignancy (6 patients); chest pain (5 patients); and cellulitis (5 patients).