Overuse of inpatient stat laboratory orders (stat is an abbreviation of the Latin word statim, meaning immediately, without delay; alternatively, some consider it an acronym for short turnaround time) is a major problem in the modern healthcare system.[1, 2, 3, 4, 5] Ordering laboratory tests stat is a common way to expedite processing, with expectation of results being reported within 1 hour from the time ordered, according to the College of American Pathologists.[6] However, stat orders are also requested for convenience,[2] to expedite discharge,[7] or to meet expectation of turnaround times.[8, 9, 10] Overuse of stat orders increases cost and may reduce the effectiveness of a system. Reduction of excessive stat order requests helps support safe and efficient patient care[11, 12] and may reduce laboratory costs.[13, 14]

Several studies have examined interventions to optimize stat laboratory utilization.[14, 15] Potentially effective interventions include establishment of stat ordering guidelines, utilization of point‐of‐care testing, and prompt feedback via computerized physician order entry (CPOE) systems.[16, 17, 18] However, limited evidence is available regarding the effectiveness of audit and feedback in reducing stat ordering frequency.

Our institution shared the challenge of a high frequency of stat laboratory test orders. An interdisciplinary working group comprising leadership in the medicine, surgery, informatics, laboratory medicine, and quality and patient safety departments was formed to approach this problem and identify potential interventions. The objectives of this study are to describe the patterns of stat orders at our institution as well as to assess the effectiveness of the targeted individual feedback intervention in reducing utilization of stat laboratory test orders.

METHODS

RESULTS

We identified 1045 providers who ordered 1 laboratory test from September 2009 to June 2010. Of those, 716 were nontrainee providers and 329 were trainee providers. Among the trainee providers, 126 were Medicine residents, 33 were General Surgery residents, and 103 were PGY‐1. A total of 772,734 laboratory tests were ordered during the study period, and 349,658 (45.2%) tests were ordered as stat. Of all stat orders, 179,901 (51.5%) were ordered by Medicine residents and 52,225 (14.9%) were ordered by General Surgery residents.

Thirty‐seven providers received individual feedback during the intervention period. This group consisted of 8 nontrainee providers (nurse practitioners and physician assistants), 21 Medicine residents (5 were PGY‐1), and 8 General Surgery residents (all PGY‐1). This group ordered a total of 84,435 stat laboratory tests from September 2009 to June 2010 and was responsible for 24.2% of all stat laboratory test orders at the institution.

Provider Analysis

After exclusion of providers who ordered <10 laboratory tests from September 2009 to June 2010, a total of 807 providers remained. The median proportion of stat orders out of total orders was 40% among all providers and 41.6% for nontrainee providers (N = 500), 38.7% for Medicine residents (N = 125), 80.2% for General Surgery residents (N = 32), and 24.2% for other trainee providers (N = 150). The proportion of stat orders differed significantly by specialty and educational level, but also even among providers in the same specialty at the same educational level. Among PGY‐1 residents, the stat‐ordering proportion ranged from 6.9% to 49.1% for Medicine (N = 54) and 69.0% to 97.1% for General Surgery (N = 16). The proportion of stat orders was significantly higher among providers who received feedback compared with those who did not (median, 72.4% [IQR, 55.0%89.5%] vs 39.0% [IQR, 14.9%65.7%], P < 0.001). When stratified by specialty and educational level, the statistical significance remained in nontrainee providers and trainee providers with higher educational level, but not in PGY‐1 residents (Table 1).

Table 1.Proportion of Stat Laboratory Orders by Provider, Comparison by Feedback Status

	All Providers		Feedback Given		Feedback Not Given
	N	Stat %	N	Stat %	N	Stat %	P Valuea
NOTE: Values for Stat % are given as median (IQR). Abbreviations: IQR, interquartile range; PGY, postgraduate year; Stat, immediately. a P value is for comparison between providers who received feedback vs those who did not. b Nontrainee providers are attending physicians, nurse practitioners, and physician assistants. c Trainee providers are residents and fellows.
Total	807	40 (15.869.0)	37	72.4 (55.089.5)	770	39.0 (14.965.7)	<0.001
Nontrainee providersb	500	41.6 (13.571.5)	8	91.7 (64.097.5)	492	40.2 (13.270.9)	<0.001
Trainee providersc	307	37.8 (19.162.7)	29	69.3 (44.380.9)	278	35.1 (17.655.6)	<0.001
Medicine	125	38.7 (26.850.4)	21	58.8 (36.872.6)	104	36.1 (25.945.6)	<0.001
PGY‐1	54	28.1 (23.935.2)	5	32.0 (25.536.8)	49	27.9 (23.534.6)	0.52
PGY‐2 and higher	71	46.5 (39.160.4)	16	63.9 (54.575.7)	55	45.1 (36.554.9)	<0.001
General surgery	32	80.2 (69.690.1)	8	89.5 (79.392.7)	24	78.7 (67.987.4)	<0.05
PGY‐1	16	86.4 (79.191.1)	8	89.5 (79.392.7)	8	84.0 (73.289.1)	0.25
PGY‐2 and higher	16	74.4 (65.485.3)
Other	150	24.2 (9.055.0)
PGY‐1	31	28.2 (18.478.3)
PGY‐2 or higher	119	20.9 (5.651.3)

Stat Ordering Pattern Change by Individual Feedback

Among 37 providers who received individual feedback, 8 providers were ranked in the top 10 more than once and received repeated feedback. Twenty‐seven of 37 providers had both pre‐feedback and post‐feedback data and were included in the analysis. Of those, 7 were nontrainee providers, 16 were Medicine residents (5 were PGY‐1), and 4 were General Surgery residents (all PGY‐1). The proportion of stat laboratory orders per provider decreased by 15.7% (95% confidence interval [CI]: 5.6% to 25.9%, P = 0.004) after feedback (Table 2). The decrease remained significant after excluding the months in which providers were ranked in the top 10 (11.4%; 95% CI: 0.7% to 22.1%, P = 0.04).

Table 2.Stat Laboratory Ordering Practice Changes Among Providers Receiving Feedback and Those Not Receiving Feedback

	Top 10 Providers (Received Feedback)					Providers Ranked in 1130 (No Feedback)
	N	Mean Stat %		Mean Difference (95% CI)	P Value	N	Mean Stat %		Mean Difference (95% CI)	P Value
Pre		Post	Pre				Post
NOTE: Abbreviations: CI, confidence interval; PGY, postgraduate year; Stat, immediately.
Total	27	71.2	55.5	15.7 (25.9 to 5.6)	0.004	39	64.6	60.2	4.5 (11.0 to 2.1)	0.18
Nontrainee providers	7	94.6	73.2	21.4 (46.9 to 4.1)	0.09	12	84.4	80.6	3.8 (11.9 to 4.3)	0.32
Trainee providers	20	63.0	49.3	13.7 (25.6 to 1.9)	0.03	27	55.8	51.1	4.7 (13.9 to 4.4)	0.30
Medicine	16	55.8	45.0	10.8 (23.3 to 1.6)	0.08	21	46.2	41.3	4.8 (16.3 to 6.7)	0.39
General Surgery	4	91.9	66.4	25.4 (78.9 to 28.0)	0.23	6	89.6	85.2	4.4 (20.5 to 11.6)	0.51
PGY‐1	9	58.9	47.7	11.2 (32.0 to 9.5)	0.25	15	55.2	49.2	6.0 (18.9 to 6.9)	0.33
PGY‐2 or Higher	11	66.4	50.6	15.8 (32.7 to 1.1)	0.06	12	56.6	53.5	3.1 (18.3 to 12.1)	0.66

In comparison, a total of 57 providers who did not receive feedback were in the 11 to 30 range during the intervention period. Three Obstetrics and Gynecology residents and 3 Family Medicine residents were excluded from the analysis to match specialty with providers who received feedback. Thirty‐nine of 51 providers had adequate data and were included in the analysis, comprising 12 nontrainee providers, 21 Medicine residents (10 were PGY‐1), and 6 General Surgery residents (5 were PGY‐1). Among them, the proportion of stat laboratory orders per provider did not change significantly, with a 4.5% decrease (95% CI: 2.1% to 11.0%, P = 0.18; Table 2).

Stat Ordering Trends Among Trainee Providers

After exclusion of data for the month with <10 total laboratory tests per provider, a total of 303 trainee providers remained, providing 2322 data points for analysis. Of the 303, 125 were Medicine residents (54 were PGY‐1), 32 were General Surgery residents (16 were PGY‐1), and 146 were others (31 were PGY‐1). The monthly trends for the average proportion of stat orders among those providers are shown in Figure 1. The decrease in the proportion of stat orders was observed after January 2010 in Medicine and General Surgery residents both in PGY‐1 and PGY‐2 or higher, but no change was observed in other trainee providers.

Figure 1

DISCUSSION

We describe a series of interventions implemented at our institution to decrease the utilization of stat laboratory orders. Based on an audit of laboratory‐ordering data, we decided to target high utilizers of stat laboratory tests, especially Medicine and General Surgery residents. After presenting an educational session to those residents, we gave individual feedback to the highest utilizers of stat laboratory orders. Providers who received feedback decreased their utilization of stat laboratory orders, but the stat ordering pattern did not change among those who did not receive feedback.

The individual feedback intervention involved key stakeholders for resident and nontrainee provider education (directors of the Medicine and General Surgery residency programs and other direct clinical supervisors). The targeted feedback was delivered via direct supervisors and was provided more than once as needed, which are key factors for effective feedback in modifying behavior in professional practice.[19] Allowing the supervisors to choose the most appropriate form of feedback for each individual (meetings, phone calls, or e‐mail) enabled timely and individually tailored feedback and contributed to successful implementation. We feel intervention had high educational value for residents, as it promoted residents' engagement in proper systems‐based practice, one of the 6 core competencies of the Accreditation Council for Graduate Medical Education (ACGME).

We utilized the EMR to obtain provider‐specific data for feedback and analysis. As previously suggested, the use of the EMR for audit and feedback was effective in providing timely, actionable, and individualized feedback with peer benchmarking.[20, 21] We used the raw number of stat laboratory orders for audit and the proportion of stat orders out of total orders to assess the individual behavioral patterns. Although the proportional use of stat orders is affected by patient acuity and workplace or rotation site, it also seems largely affected by provider's preference or practice patterns, as we saw the variance among providers of the same specialty and educational level. The changes in the stat ordering trends only seen among Medicine and General Surgery residents suggests that our interventions successfully decreased the overall utilization of stat laboratory orders among targeted providers, and it seems less likely that those decreases are due to changes in patient acuity, changes in rotation sites, or learning curve among trainee providers. When averaged over the 10‐month study period, as shown in Table 1, the providers who received feedback ordered a higher proportion of stat tests than those who did not receive feedback, except for PGY‐1 residents. This suggests that although auditing based on the number of stat laboratory orders identified providers who tended to order more stat tests than others, it may not be a reliable indicator for PGY‐1 residents, whose number of laboratory orders highly fluctuates by rotation.

There are certain limitations to our study. First, we assumed that the top utilizers were inappropriately ordering stat laboratory tests. Because there is no clear consensus as to what constitutes appropriate stat testing,[7] it was difficult, if not impossible, to determine which specific orders were inappropriate. However, high variability of the stat ordering pattern in the analysis provides some evidence that high stat utilizers customarily order more stat testing as compared with others. A recent study also revealed that the median stat ordering percentage was 35.9% among 52 US institutions.[13] At our institution, 47.8% of laboratory tests were ordered stat prior to the intervention, higher than the benchmark, providing the rationale for our intervention.

Second, the intervention was conducted in a time‐series fashion and no randomization was employed. The comparison of providers who received feedback with those who did not is subject to selection bias, and the difference in the change in stat ordering pattern between these 2 groups may be partially due to variability of work location, rotation type, or acuity of patients. However, we performed a sensitivity analysis excluding the months when the providers were ranked in the top 10, assuming that they may have ordered an unusually high proportion of stat tests due to high acuity of patients (eg, rotation in the intensive care units) during those months. Robust results in this analysis support our contention that individual feedback was effective. In addition, we cannot completely rule out the possibility that the changes in stat ordering practice may be solely due to natural maturation effects within an academic year among trainee providers, especially PGY‐1 residents. However, relatively acute changes in the stat ordering trends only among targeted provider groups around January 2010, corresponding to the timing of interventions, suggest otherwise.

Third, we were not able to test if the intervention or decrease in stat orders adversely affected patient care. For example, if, after receiving feedback, providers did not order some tests stat that should have been ordered that way, this could have negatively affected patient care. Additionally, we did not evaluate whether reduction in stat laboratory orders improved timeliness of the reporting of stat laboratory results.

Lastly, the sustained effect and feasibility of this intervention were not tested. Past studies suggest educational interventions in laboratory ordering behavior would most likely need to be continued to maintain its effectiveness.[22, 23] Although we acknowledge that sustainability of this type of intervention may be difficult, we feel we have demonstrated that there is still value associated with giving personalized feedback.

This study has implications for future interventions and research. Use of automated, EMR‐based feedback on laboratory ordering performance may be effective in reducing excessive stat ordering and may obviate the need for time‐consuming efforts by supervisors. Development of quality indicators that more accurately assess stat ordering patterns, potentially adjusted for working sites and patient acuity, may be necessary. Studies that measure the impact of decreasing stat laboratory orders on turnaround times and cost may be of value.

CONCLUSION

At our urban, tertiary‐care teaching institution, stat ordering frequency was highly variable among providers. Targeted individual feedback to providers who ordered a large number of stat laboratory tests decreased their stat laboratory order utilization.

Overuse of inpatient stat laboratory orders (stat is an abbreviation of the Latin word statim, meaning immediately, without delay; alternatively, some consider it an acronym for short turnaround time) is a major problem in the modern healthcare system.[1, 2, 3, 4, 5] Ordering laboratory tests stat is a common way to expedite processing, with expectation of results being reported within 1 hour from the time ordered, according to the College of American Pathologists.[6] However, stat orders are also requested for convenience,[2] to expedite discharge,[7] or to meet expectation of turnaround times.[8, 9, 10] Overuse of stat orders increases cost and may reduce the effectiveness of a system. Reduction of excessive stat order requests helps support safe and efficient patient care[11, 12] and may reduce laboratory costs.[13, 14]

Several studies have examined interventions to optimize stat laboratory utilization.[14, 15] Potentially effective interventions include establishment of stat ordering guidelines, utilization of point‐of‐care testing, and prompt feedback via computerized physician order entry (CPOE) systems.[16, 17, 18] However, limited evidence is available regarding the effectiveness of audit and feedback in reducing stat ordering frequency.

Our institution shared the challenge of a high frequency of stat laboratory test orders. An interdisciplinary working group comprising leadership in the medicine, surgery, informatics, laboratory medicine, and quality and patient safety departments was formed to approach this problem and identify potential interventions. The objectives of this study are to describe the patterns of stat orders at our institution as well as to assess the effectiveness of the targeted individual feedback intervention in reducing utilization of stat laboratory test orders.

METHODS

RESULTS

We identified 1045 providers who ordered 1 laboratory test from September 2009 to June 2010. Of those, 716 were nontrainee providers and 329 were trainee providers. Among the trainee providers, 126 were Medicine residents, 33 were General Surgery residents, and 103 were PGY‐1. A total of 772,734 laboratory tests were ordered during the study period, and 349,658 (45.2%) tests were ordered as stat. Of all stat orders, 179,901 (51.5%) were ordered by Medicine residents and 52,225 (14.9%) were ordered by General Surgery residents.

Thirty‐seven providers received individual feedback during the intervention period. This group consisted of 8 nontrainee providers (nurse practitioners and physician assistants), 21 Medicine residents (5 were PGY‐1), and 8 General Surgery residents (all PGY‐1). This group ordered a total of 84,435 stat laboratory tests from September 2009 to June 2010 and was responsible for 24.2% of all stat laboratory test orders at the institution.

Provider Analysis

After exclusion of providers who ordered <10 laboratory tests from September 2009 to June 2010, a total of 807 providers remained. The median proportion of stat orders out of total orders was 40% among all providers and 41.6% for nontrainee providers (N = 500), 38.7% for Medicine residents (N = 125), 80.2% for General Surgery residents (N = 32), and 24.2% for other trainee providers (N = 150). The proportion of stat orders differed significantly by specialty and educational level, but also even among providers in the same specialty at the same educational level. Among PGY‐1 residents, the stat‐ordering proportion ranged from 6.9% to 49.1% for Medicine (N = 54) and 69.0% to 97.1% for General Surgery (N = 16). The proportion of stat orders was significantly higher among providers who received feedback compared with those who did not (median, 72.4% [IQR, 55.0%89.5%] vs 39.0% [IQR, 14.9%65.7%], P < 0.001). When stratified by specialty and educational level, the statistical significance remained in nontrainee providers and trainee providers with higher educational level, but not in PGY‐1 residents (Table 1).

Table 1.Proportion of Stat Laboratory Orders by Provider, Comparison by Feedback Status

	All Providers		Feedback Given		Feedback Not Given
	N	Stat %	N	Stat %	N	Stat %	P Valuea
NOTE: Values for Stat % are given as median (IQR). Abbreviations: IQR, interquartile range; PGY, postgraduate year; Stat, immediately. a P value is for comparison between providers who received feedback vs those who did not. b Nontrainee providers are attending physicians, nurse practitioners, and physician assistants. c Trainee providers are residents and fellows.
Total	807	40 (15.869.0)	37	72.4 (55.089.5)	770	39.0 (14.965.7)	<0.001
Nontrainee providersb	500	41.6 (13.571.5)	8	91.7 (64.097.5)	492	40.2 (13.270.9)	<0.001
Trainee providersc	307	37.8 (19.162.7)	29	69.3 (44.380.9)	278	35.1 (17.655.6)	<0.001
Medicine	125	38.7 (26.850.4)	21	58.8 (36.872.6)	104	36.1 (25.945.6)	<0.001
PGY‐1	54	28.1 (23.935.2)	5	32.0 (25.536.8)	49	27.9 (23.534.6)	0.52
PGY‐2 and higher	71	46.5 (39.160.4)	16	63.9 (54.575.7)	55	45.1 (36.554.9)	<0.001
General surgery	32	80.2 (69.690.1)	8	89.5 (79.392.7)	24	78.7 (67.987.4)	<0.05
PGY‐1	16	86.4 (79.191.1)	8	89.5 (79.392.7)	8	84.0 (73.289.1)	0.25
PGY‐2 and higher	16	74.4 (65.485.3)
Other	150	24.2 (9.055.0)
PGY‐1	31	28.2 (18.478.3)
PGY‐2 or higher	119	20.9 (5.651.3)

Stat Ordering Pattern Change by Individual Feedback

Among 37 providers who received individual feedback, 8 providers were ranked in the top 10 more than once and received repeated feedback. Twenty‐seven of 37 providers had both pre‐feedback and post‐feedback data and were included in the analysis. Of those, 7 were nontrainee providers, 16 were Medicine residents (5 were PGY‐1), and 4 were General Surgery residents (all PGY‐1). The proportion of stat laboratory orders per provider decreased by 15.7% (95% confidence interval [CI]: 5.6% to 25.9%, P = 0.004) after feedback (Table 2). The decrease remained significant after excluding the months in which providers were ranked in the top 10 (11.4%; 95% CI: 0.7% to 22.1%, P = 0.04).

Table 2.Stat Laboratory Ordering Practice Changes Among Providers Receiving Feedback and Those Not Receiving Feedback

	Top 10 Providers (Received Feedback)					Providers Ranked in 1130 (No Feedback)
	N	Mean Stat %		Mean Difference (95% CI)	P Value	N	Mean Stat %		Mean Difference (95% CI)	P Value
Pre		Post	Pre				Post
NOTE: Abbreviations: CI, confidence interval; PGY, postgraduate year; Stat, immediately.
Total	27	71.2	55.5	15.7 (25.9 to 5.6)	0.004	39	64.6	60.2	4.5 (11.0 to 2.1)	0.18
Nontrainee providers	7	94.6	73.2	21.4 (46.9 to 4.1)	0.09	12	84.4	80.6	3.8 (11.9 to 4.3)	0.32
Trainee providers	20	63.0	49.3	13.7 (25.6 to 1.9)	0.03	27	55.8	51.1	4.7 (13.9 to 4.4)	0.30
Medicine	16	55.8	45.0	10.8 (23.3 to 1.6)	0.08	21	46.2	41.3	4.8 (16.3 to 6.7)	0.39
General Surgery	4	91.9	66.4	25.4 (78.9 to 28.0)	0.23	6	89.6	85.2	4.4 (20.5 to 11.6)	0.51
PGY‐1	9	58.9	47.7	11.2 (32.0 to 9.5)	0.25	15	55.2	49.2	6.0 (18.9 to 6.9)	0.33
PGY‐2 or Higher	11	66.4	50.6	15.8 (32.7 to 1.1)	0.06	12	56.6	53.5	3.1 (18.3 to 12.1)	0.66

In comparison, a total of 57 providers who did not receive feedback were in the 11 to 30 range during the intervention period. Three Obstetrics and Gynecology residents and 3 Family Medicine residents were excluded from the analysis to match specialty with providers who received feedback. Thirty‐nine of 51 providers had adequate data and were included in the analysis, comprising 12 nontrainee providers, 21 Medicine residents (10 were PGY‐1), and 6 General Surgery residents (5 were PGY‐1). Among them, the proportion of stat laboratory orders per provider did not change significantly, with a 4.5% decrease (95% CI: 2.1% to 11.0%, P = 0.18; Table 2).

Stat Ordering Trends Among Trainee Providers

After exclusion of data for the month with <10 total laboratory tests per provider, a total of 303 trainee providers remained, providing 2322 data points for analysis. Of the 303, 125 were Medicine residents (54 were PGY‐1), 32 were General Surgery residents (16 were PGY‐1), and 146 were others (31 were PGY‐1). The monthly trends for the average proportion of stat orders among those providers are shown in Figure 1. The decrease in the proportion of stat orders was observed after January 2010 in Medicine and General Surgery residents both in PGY‐1 and PGY‐2 or higher, but no change was observed in other trainee providers.

Figure 1

DISCUSSION

We describe a series of interventions implemented at our institution to decrease the utilization of stat laboratory orders. Based on an audit of laboratory‐ordering data, we decided to target high utilizers of stat laboratory tests, especially Medicine and General Surgery residents. After presenting an educational session to those residents, we gave individual feedback to the highest utilizers of stat laboratory orders. Providers who received feedback decreased their utilization of stat laboratory orders, but the stat ordering pattern did not change among those who did not receive feedback.

The individual feedback intervention involved key stakeholders for resident and nontrainee provider education (directors of the Medicine and General Surgery residency programs and other direct clinical supervisors). The targeted feedback was delivered via direct supervisors and was provided more than once as needed, which are key factors for effective feedback in modifying behavior in professional practice.[19] Allowing the supervisors to choose the most appropriate form of feedback for each individual (meetings, phone calls, or e‐mail) enabled timely and individually tailored feedback and contributed to successful implementation. We feel intervention had high educational value for residents, as it promoted residents' engagement in proper systems‐based practice, one of the 6 core competencies of the Accreditation Council for Graduate Medical Education (ACGME).

We utilized the EMR to obtain provider‐specific data for feedback and analysis. As previously suggested, the use of the EMR for audit and feedback was effective in providing timely, actionable, and individualized feedback with peer benchmarking.[20, 21] We used the raw number of stat laboratory orders for audit and the proportion of stat orders out of total orders to assess the individual behavioral patterns. Although the proportional use of stat orders is affected by patient acuity and workplace or rotation site, it also seems largely affected by provider's preference or practice patterns, as we saw the variance among providers of the same specialty and educational level. The changes in the stat ordering trends only seen among Medicine and General Surgery residents suggests that our interventions successfully decreased the overall utilization of stat laboratory orders among targeted providers, and it seems less likely that those decreases are due to changes in patient acuity, changes in rotation sites, or learning curve among trainee providers. When averaged over the 10‐month study period, as shown in Table 1, the providers who received feedback ordered a higher proportion of stat tests than those who did not receive feedback, except for PGY‐1 residents. This suggests that although auditing based on the number of stat laboratory orders identified providers who tended to order more stat tests than others, it may not be a reliable indicator for PGY‐1 residents, whose number of laboratory orders highly fluctuates by rotation.

There are certain limitations to our study. First, we assumed that the top utilizers were inappropriately ordering stat laboratory tests. Because there is no clear consensus as to what constitutes appropriate stat testing,[7] it was difficult, if not impossible, to determine which specific orders were inappropriate. However, high variability of the stat ordering pattern in the analysis provides some evidence that high stat utilizers customarily order more stat testing as compared with others. A recent study also revealed that the median stat ordering percentage was 35.9% among 52 US institutions.[13] At our institution, 47.8% of laboratory tests were ordered stat prior to the intervention, higher than the benchmark, providing the rationale for our intervention.

Second, the intervention was conducted in a time‐series fashion and no randomization was employed. The comparison of providers who received feedback with those who did not is subject to selection bias, and the difference in the change in stat ordering pattern between these 2 groups may be partially due to variability of work location, rotation type, or acuity of patients. However, we performed a sensitivity analysis excluding the months when the providers were ranked in the top 10, assuming that they may have ordered an unusually high proportion of stat tests due to high acuity of patients (eg, rotation in the intensive care units) during those months. Robust results in this analysis support our contention that individual feedback was effective. In addition, we cannot completely rule out the possibility that the changes in stat ordering practice may be solely due to natural maturation effects within an academic year among trainee providers, especially PGY‐1 residents. However, relatively acute changes in the stat ordering trends only among targeted provider groups around January 2010, corresponding to the timing of interventions, suggest otherwise.

Third, we were not able to test if the intervention or decrease in stat orders adversely affected patient care. For example, if, after receiving feedback, providers did not order some tests stat that should have been ordered that way, this could have negatively affected patient care. Additionally, we did not evaluate whether reduction in stat laboratory orders improved timeliness of the reporting of stat laboratory results.

Lastly, the sustained effect and feasibility of this intervention were not tested. Past studies suggest educational interventions in laboratory ordering behavior would most likely need to be continued to maintain its effectiveness.[22, 23] Although we acknowledge that sustainability of this type of intervention may be difficult, we feel we have demonstrated that there is still value associated with giving personalized feedback.

This study has implications for future interventions and research. Use of automated, EMR‐based feedback on laboratory ordering performance may be effective in reducing excessive stat ordering and may obviate the need for time‐consuming efforts by supervisors. Development of quality indicators that more accurately assess stat ordering patterns, potentially adjusted for working sites and patient acuity, may be necessary. Studies that measure the impact of decreasing stat laboratory orders on turnaround times and cost may be of value.

CONCLUSION

At our urban, tertiary‐care teaching institution, stat ordering frequency was highly variable among providers. Targeted individual feedback to providers who ordered a large number of stat laboratory tests decreased their stat laboratory order utilization.

Overuse of inpatient stat laboratory orders (stat is an abbreviation of the Latin word statim, meaning immediately, without delay; alternatively, some consider it an acronym for short turnaround time) is a major problem in the modern healthcare system.[1, 2, 3, 4, 5] Ordering laboratory tests stat is a common way to expedite processing, with expectation of results being reported within 1 hour from the time ordered, according to the College of American Pathologists.[6] However, stat orders are also requested for convenience,[2] to expedite discharge,[7] or to meet expectation of turnaround times.[8, 9, 10] Overuse of stat orders increases cost and may reduce the effectiveness of a system. Reduction of excessive stat order requests helps support safe and efficient patient care[11, 12] and may reduce laboratory costs.[13, 14]

Several studies have examined interventions to optimize stat laboratory utilization.[14, 15] Potentially effective interventions include establishment of stat ordering guidelines, utilization of point‐of‐care testing, and prompt feedback via computerized physician order entry (CPOE) systems.[16, 17, 18] However, limited evidence is available regarding the effectiveness of audit and feedback in reducing stat ordering frequency.

Our institution shared the challenge of a high frequency of stat laboratory test orders. An interdisciplinary working group comprising leadership in the medicine, surgery, informatics, laboratory medicine, and quality and patient safety departments was formed to approach this problem and identify potential interventions. The objectives of this study are to describe the patterns of stat orders at our institution as well as to assess the effectiveness of the targeted individual feedback intervention in reducing utilization of stat laboratory test orders.

METHODS

RESULTS

We identified 1045 providers who ordered 1 laboratory test from September 2009 to June 2010. Of those, 716 were nontrainee providers and 329 were trainee providers. Among the trainee providers, 126 were Medicine residents, 33 were General Surgery residents, and 103 were PGY‐1. A total of 772,734 laboratory tests were ordered during the study period, and 349,658 (45.2%) tests were ordered as stat. Of all stat orders, 179,901 (51.5%) were ordered by Medicine residents and 52,225 (14.9%) were ordered by General Surgery residents.

Thirty‐seven providers received individual feedback during the intervention period. This group consisted of 8 nontrainee providers (nurse practitioners and physician assistants), 21 Medicine residents (5 were PGY‐1), and 8 General Surgery residents (all PGY‐1). This group ordered a total of 84,435 stat laboratory tests from September 2009 to June 2010 and was responsible for 24.2% of all stat laboratory test orders at the institution.

Provider Analysis

After exclusion of providers who ordered <10 laboratory tests from September 2009 to June 2010, a total of 807 providers remained. The median proportion of stat orders out of total orders was 40% among all providers and 41.6% for nontrainee providers (N = 500), 38.7% for Medicine residents (N = 125), 80.2% for General Surgery residents (N = 32), and 24.2% for other trainee providers (N = 150). The proportion of stat orders differed significantly by specialty and educational level, but also even among providers in the same specialty at the same educational level. Among PGY‐1 residents, the stat‐ordering proportion ranged from 6.9% to 49.1% for Medicine (N = 54) and 69.0% to 97.1% for General Surgery (N = 16). The proportion of stat orders was significantly higher among providers who received feedback compared with those who did not (median, 72.4% [IQR, 55.0%89.5%] vs 39.0% [IQR, 14.9%65.7%], P < 0.001). When stratified by specialty and educational level, the statistical significance remained in nontrainee providers and trainee providers with higher educational level, but not in PGY‐1 residents (Table 1).

Table 1.Proportion of Stat Laboratory Orders by Provider, Comparison by Feedback Status

	All Providers		Feedback Given		Feedback Not Given
	N	Stat %	N	Stat %	N	Stat %	P Valuea
NOTE: Values for Stat % are given as median (IQR). Abbreviations: IQR, interquartile range; PGY, postgraduate year; Stat, immediately. a P value is for comparison between providers who received feedback vs those who did not. b Nontrainee providers are attending physicians, nurse practitioners, and physician assistants. c Trainee providers are residents and fellows.
Total	807	40 (15.869.0)	37	72.4 (55.089.5)	770	39.0 (14.965.7)	<0.001
Nontrainee providersb	500	41.6 (13.571.5)	8	91.7 (64.097.5)	492	40.2 (13.270.9)	<0.001
Trainee providersc	307	37.8 (19.162.7)	29	69.3 (44.380.9)	278	35.1 (17.655.6)	<0.001
Medicine	125	38.7 (26.850.4)	21	58.8 (36.872.6)	104	36.1 (25.945.6)	<0.001
PGY‐1	54	28.1 (23.935.2)	5	32.0 (25.536.8)	49	27.9 (23.534.6)	0.52
PGY‐2 and higher	71	46.5 (39.160.4)	16	63.9 (54.575.7)	55	45.1 (36.554.9)	<0.001
General surgery	32	80.2 (69.690.1)	8	89.5 (79.392.7)	24	78.7 (67.987.4)	<0.05
PGY‐1	16	86.4 (79.191.1)	8	89.5 (79.392.7)	8	84.0 (73.289.1)	0.25
PGY‐2 and higher	16	74.4 (65.485.3)
Other	150	24.2 (9.055.0)
PGY‐1	31	28.2 (18.478.3)
PGY‐2 or higher	119	20.9 (5.651.3)

Stat Ordering Pattern Change by Individual Feedback

Among 37 providers who received individual feedback, 8 providers were ranked in the top 10 more than once and received repeated feedback. Twenty‐seven of 37 providers had both pre‐feedback and post‐feedback data and were included in the analysis. Of those, 7 were nontrainee providers, 16 were Medicine residents (5 were PGY‐1), and 4 were General Surgery residents (all PGY‐1). The proportion of stat laboratory orders per provider decreased by 15.7% (95% confidence interval [CI]: 5.6% to 25.9%, P = 0.004) after feedback (Table 2). The decrease remained significant after excluding the months in which providers were ranked in the top 10 (11.4%; 95% CI: 0.7% to 22.1%, P = 0.04).

Table 2.Stat Laboratory Ordering Practice Changes Among Providers Receiving Feedback and Those Not Receiving Feedback

	Top 10 Providers (Received Feedback)					Providers Ranked in 1130 (No Feedback)
	N	Mean Stat %		Mean Difference (95% CI)	P Value	N	Mean Stat %		Mean Difference (95% CI)	P Value
Pre		Post	Pre				Post
NOTE: Abbreviations: CI, confidence interval; PGY, postgraduate year; Stat, immediately.
Total	27	71.2	55.5	15.7 (25.9 to 5.6)	0.004	39	64.6	60.2	4.5 (11.0 to 2.1)	0.18
Nontrainee providers	7	94.6	73.2	21.4 (46.9 to 4.1)	0.09	12	84.4	80.6	3.8 (11.9 to 4.3)	0.32
Trainee providers	20	63.0	49.3	13.7 (25.6 to 1.9)	0.03	27	55.8	51.1	4.7 (13.9 to 4.4)	0.30
Medicine	16	55.8	45.0	10.8 (23.3 to 1.6)	0.08	21	46.2	41.3	4.8 (16.3 to 6.7)	0.39
General Surgery	4	91.9	66.4	25.4 (78.9 to 28.0)	0.23	6	89.6	85.2	4.4 (20.5 to 11.6)	0.51
PGY‐1	9	58.9	47.7	11.2 (32.0 to 9.5)	0.25	15	55.2	49.2	6.0 (18.9 to 6.9)	0.33
PGY‐2 or Higher	11	66.4	50.6	15.8 (32.7 to 1.1)	0.06	12	56.6	53.5	3.1 (18.3 to 12.1)	0.66

In comparison, a total of 57 providers who did not receive feedback were in the 11 to 30 range during the intervention period. Three Obstetrics and Gynecology residents and 3 Family Medicine residents were excluded from the analysis to match specialty with providers who received feedback. Thirty‐nine of 51 providers had adequate data and were included in the analysis, comprising 12 nontrainee providers, 21 Medicine residents (10 were PGY‐1), and 6 General Surgery residents (5 were PGY‐1). Among them, the proportion of stat laboratory orders per provider did not change significantly, with a 4.5% decrease (95% CI: 2.1% to 11.0%, P = 0.18; Table 2).

Stat Ordering Trends Among Trainee Providers

After exclusion of data for the month with <10 total laboratory tests per provider, a total of 303 trainee providers remained, providing 2322 data points for analysis. Of the 303, 125 were Medicine residents (54 were PGY‐1), 32 were General Surgery residents (16 were PGY‐1), and 146 were others (31 were PGY‐1). The monthly trends for the average proportion of stat orders among those providers are shown in Figure 1. The decrease in the proportion of stat orders was observed after January 2010 in Medicine and General Surgery residents both in PGY‐1 and PGY‐2 or higher, but no change was observed in other trainee providers.

Figure 1

DISCUSSION

We describe a series of interventions implemented at our institution to decrease the utilization of stat laboratory orders. Based on an audit of laboratory‐ordering data, we decided to target high utilizers of stat laboratory tests, especially Medicine and General Surgery residents. After presenting an educational session to those residents, we gave individual feedback to the highest utilizers of stat laboratory orders. Providers who received feedback decreased their utilization of stat laboratory orders, but the stat ordering pattern did not change among those who did not receive feedback.

The individual feedback intervention involved key stakeholders for resident and nontrainee provider education (directors of the Medicine and General Surgery residency programs and other direct clinical supervisors). The targeted feedback was delivered via direct supervisors and was provided more than once as needed, which are key factors for effective feedback in modifying behavior in professional practice.[19] Allowing the supervisors to choose the most appropriate form of feedback for each individual (meetings, phone calls, or e‐mail) enabled timely and individually tailored feedback and contributed to successful implementation. We feel intervention had high educational value for residents, as it promoted residents' engagement in proper systems‐based practice, one of the 6 core competencies of the Accreditation Council for Graduate Medical Education (ACGME).

We utilized the EMR to obtain provider‐specific data for feedback and analysis. As previously suggested, the use of the EMR for audit and feedback was effective in providing timely, actionable, and individualized feedback with peer benchmarking.[20, 21] We used the raw number of stat laboratory orders for audit and the proportion of stat orders out of total orders to assess the individual behavioral patterns. Although the proportional use of stat orders is affected by patient acuity and workplace or rotation site, it also seems largely affected by provider's preference or practice patterns, as we saw the variance among providers of the same specialty and educational level. The changes in the stat ordering trends only seen among Medicine and General Surgery residents suggests that our interventions successfully decreased the overall utilization of stat laboratory orders among targeted providers, and it seems less likely that those decreases are due to changes in patient acuity, changes in rotation sites, or learning curve among trainee providers. When averaged over the 10‐month study period, as shown in Table 1, the providers who received feedback ordered a higher proportion of stat tests than those who did not receive feedback, except for PGY‐1 residents. This suggests that although auditing based on the number of stat laboratory orders identified providers who tended to order more stat tests than others, it may not be a reliable indicator for PGY‐1 residents, whose number of laboratory orders highly fluctuates by rotation.

There are certain limitations to our study. First, we assumed that the top utilizers were inappropriately ordering stat laboratory tests. Because there is no clear consensus as to what constitutes appropriate stat testing,[7] it was difficult, if not impossible, to determine which specific orders were inappropriate. However, high variability of the stat ordering pattern in the analysis provides some evidence that high stat utilizers customarily order more stat testing as compared with others. A recent study also revealed that the median stat ordering percentage was 35.9% among 52 US institutions.[13] At our institution, 47.8% of laboratory tests were ordered stat prior to the intervention, higher than the benchmark, providing the rationale for our intervention.

Second, the intervention was conducted in a time‐series fashion and no randomization was employed. The comparison of providers who received feedback with those who did not is subject to selection bias, and the difference in the change in stat ordering pattern between these 2 groups may be partially due to variability of work location, rotation type, or acuity of patients. However, we performed a sensitivity analysis excluding the months when the providers were ranked in the top 10, assuming that they may have ordered an unusually high proportion of stat tests due to high acuity of patients (eg, rotation in the intensive care units) during those months. Robust results in this analysis support our contention that individual feedback was effective. In addition, we cannot completely rule out the possibility that the changes in stat ordering practice may be solely due to natural maturation effects within an academic year among trainee providers, especially PGY‐1 residents. However, relatively acute changes in the stat ordering trends only among targeted provider groups around January 2010, corresponding to the timing of interventions, suggest otherwise.

Third, we were not able to test if the intervention or decrease in stat orders adversely affected patient care. For example, if, after receiving feedback, providers did not order some tests stat that should have been ordered that way, this could have negatively affected patient care. Additionally, we did not evaluate whether reduction in stat laboratory orders improved timeliness of the reporting of stat laboratory results.

Lastly, the sustained effect and feasibility of this intervention were not tested. Past studies suggest educational interventions in laboratory ordering behavior would most likely need to be continued to maintain its effectiveness.[22, 23] Although we acknowledge that sustainability of this type of intervention may be difficult, we feel we have demonstrated that there is still value associated with giving personalized feedback.

This study has implications for future interventions and research. Use of automated, EMR‐based feedback on laboratory ordering performance may be effective in reducing excessive stat ordering and may obviate the need for time‐consuming efforts by supervisors. Development of quality indicators that more accurately assess stat ordering patterns, potentially adjusted for working sites and patient acuity, may be necessary. Studies that measure the impact of decreasing stat laboratory orders on turnaround times and cost may be of value.

CONCLUSION

At our urban, tertiary‐care teaching institution, stat ordering frequency was highly variable among providers. Targeted individual feedback to providers who ordered a large number of stat laboratory tests decreased their stat laboratory order utilization.

We read with interest the report by Rimawi et al.[1] They showed convincing evidence that with a negative penicillin skin test, a course of ‐lactam is safe 2 hours after a negative challenge. However, we advise caution in generalizing these data to the outpatient setting where resensitization is a possibility. One study showed that 4.9% of patients who had negative skin tests and drug challenges reacted on rechallenges 3 weeks later.[2]

In our center, ‐lactam allergy assessment is carried out according to European Academy of Allergy and Clinical Immunology guidelines.[3] We encountered a patient who had life‐threatening anaphylaxis with co‐amoxiclav 1 month after negative allergy investigations.

A 43‐year‐old woman was referred with a history of non‐drug related urticarial episodes and urticaria and angioedema of face, neck, and arms 30 minutes after a first dose of oral co‐amoxiclav 2 years previously. Specific immunoglobulin E tests to penicillin and amoxicillin, skin tests, and oral co‐amoxiclav challenge were negative. A month later, she developed anaphylaxis (intraoral angioedema, wheeze, hypotension [70/30 mm Hg], oxygen desaturation to 60% on room air, becoming unresponsive) within minutes of an intravenous dose of co‐amoxiclav for acute cholecystitis.

Our case illustrates that despite a detailed negative allergy assessment, severe anaphylaxis can occur requiring prompt identification and appropriate treatment.

We read with interest the report by Rimawi et al.[1] They showed convincing evidence that with a negative penicillin skin test, a course of ‐lactam is safe 2 hours after a negative challenge. However, we advise caution in generalizing these data to the outpatient setting where resensitization is a possibility. One study showed that 4.9% of patients who had negative skin tests and drug challenges reacted on rechallenges 3 weeks later.[2]

In our center, ‐lactam allergy assessment is carried out according to European Academy of Allergy and Clinical Immunology guidelines.[3] We encountered a patient who had life‐threatening anaphylaxis with co‐amoxiclav 1 month after negative allergy investigations.

A 43‐year‐old woman was referred with a history of non‐drug related urticarial episodes and urticaria and angioedema of face, neck, and arms 30 minutes after a first dose of oral co‐amoxiclav 2 years previously. Specific immunoglobulin E tests to penicillin and amoxicillin, skin tests, and oral co‐amoxiclav challenge were negative. A month later, she developed anaphylaxis (intraoral angioedema, wheeze, hypotension [70/30 mm Hg], oxygen desaturation to 60% on room air, becoming unresponsive) within minutes of an intravenous dose of co‐amoxiclav for acute cholecystitis.

Our case illustrates that despite a detailed negative allergy assessment, severe anaphylaxis can occur requiring prompt identification and appropriate treatment.

We read with interest the report by Rimawi et al.[1] They showed convincing evidence that with a negative penicillin skin test, a course of ‐lactam is safe 2 hours after a negative challenge. However, we advise caution in generalizing these data to the outpatient setting where resensitization is a possibility. One study showed that 4.9% of patients who had negative skin tests and drug challenges reacted on rechallenges 3 weeks later.[2]

In our center, ‐lactam allergy assessment is carried out according to European Academy of Allergy and Clinical Immunology guidelines.[3] We encountered a patient who had life‐threatening anaphylaxis with co‐amoxiclav 1 month after negative allergy investigations.

A 43‐year‐old woman was referred with a history of non‐drug related urticarial episodes and urticaria and angioedema of face, neck, and arms 30 minutes after a first dose of oral co‐amoxiclav 2 years previously. Specific immunoglobulin E tests to penicillin and amoxicillin, skin tests, and oral co‐amoxiclav challenge were negative. A month later, she developed anaphylaxis (intraoral angioedema, wheeze, hypotension [70/30 mm Hg], oxygen desaturation to 60% on room air, becoming unresponsive) within minutes of an intravenous dose of co‐amoxiclav for acute cholecystitis.

Our case illustrates that despite a detailed negative allergy assessment, severe anaphylaxis can occur requiring prompt identification and appropriate treatment.

Stroke is the fourth leading cause of death in the United States.[1] Though actual stroke‐related death has declined nationally by 19.4%, stroke‐related morbidity is still a significant burden.[2, 3] Hospital certification programs have been developed to improve the quality of stroke care on state and national levels. The Brain Attack Coalition (BAC) proposed 2 levels of stroke hospitals: primary stroke centers (PSCs) and comprehensive stroke centers (CSCs).[3, 4] Although most stroke patients can be cared for at PSCs, CSCs are able to care for more complex stroke patients.[4, 5] Using BAC recommendations the Joint Commission (JC) and American Heart Association/American Stroke Association created a certification program for PSCs. Eight evidence‐based performance measures are currently required for JC PSC certification.[6]

At the state level, New Jersey and Florida began designating PSCs and CSCs.[7, 8] New Jersey PSC and CSC designation criteria incorporate the elements of JC PSC certification, despite preceding them by several years.[6, 9] New Jersey CSC certification consists of more comprehensive requirements (Table 1). All New Jersey‐designated stroke centers submit data in the New Jersey Acute Stroke Registry (NJASR),[7] which closely matches the Centers for Disease Control and Prevention's Paul Coverdell National Acute Stroke Registry and includes the JC‐required core measures.

There is a paucity of data comparing state‐designated CSCs and PSCs, largely because few states have state designation programs. Although a recent observational study from Finland showed better outcomes in patients treated at CSCs, measures in that study were limited to mortality and institutionalization at 1 year.[10] In this study, we examined adherence of all New Jersey state‐designated stroke centers to the JC PSC measures and compared CSCs to PSCs in this regard. We posited that better compliance with these evidence‐based measures might translate into better quality of stroke care in the state and may lend support to future, larger studies that may be conducted because of the recent certification of CSCs by the JC.

METHODS

Components of the NJASR, key components of PSCs and CSCs in the BAC report, and the 8 JC's core stroke measures for PSC certification were assessed.[3, 4, 6]

First responders in New Jersey are required to bring suspected stroke patients to the nearest stroke‐designated hospital, regardless of whether it is a PSC or CSC, unless the patient is too medically unstable and needs to be taken to the nearest hospital. From there, decisions can be made to transfer a patient to a higher level hospital (CSC) depending on the complexity of the patient's condition.

New Jersey state‐designated PSCs and CSCs are required to abstract patient‐level data, evaluate outcomes, and initiate quality improvement activities on all patients evaluated for ischemic stroke, hemorrhagic stroke, transient ischemic attack (TIA), and those who undergo acute interventional therapy. Data are submitted quarterly to the New Jersey Department of Health and Senior Services (NJDHSS).[7] Hospital data are imported into the Acute Stroke Registry Database. The NJASR statewide dataset used for this analysis included all stroke admissions for the calendar years 2010 and 2011 and contains patient demographic information, health history, clinical investigations performed, treatments provided, and outcome measures that allow for risk‐adjusted assessment of outcomes.

The JC core stroke measures (STK‐1 thru 10 [except STK‐7 and 9]: venous thromboembolism [VTE] prophylaxis, discharged on antithrombotic therapy, anticoagulation therapy for atrial fibrillation/flutter, thrombolytic therapy, antithrombotic therapy by the end of hospital day 2, discharged on statin medication, stroke education, and assessment for rehabilitation) (except STK‐7 [dysphagia screening] and STK‐9 [smoking cessation/advice counseling]) apply only to acute ischemic and/or hemorrhagic stroke patients.

In our analysis, transferred patients and patients with a diagnosis of TIA, stroke not otherwise specified, and nonstroke‐related diagnoses were excluded. Hospital identity was kept anonymous through assignment of random numeric codes by the NJDHSS. Hospitals were categorized as CSC or PSC based on NJDHSS designation. Stroke severity on admission was assessed by categorizing National Institutes of Health Stroke Scale (NIHSS) scores into: no stroke when NIHSS=0, mild stroke when NIHSS=14, and moderate to severe stroke when NIHSS>5. Median door‐to‐thrombolytic drug times were assessed for both patients who arrived to the hospital within 2 hours of stroke symptom onset and received thrombolytic therapy within 3 hours, as well as patients who arrived within 3.5 hours (210 minutes) and received treatment within 4.5 hours (270 minutes).

RESULTS

There were 36,892 acute stroke cases treated at the 53 New Jersey PSCs and 12 CSCs in the calendar year 2010 and 2011 (Table 2). Sixty percent were treated at PSCs and 40% at CSCs. There were significant differences in the distribution of patients' characteristics (race, age, and gender) between the 2 hospital levels. At both PSCs and CSCs, the majority of patients were white, distantly followed by blacks. Patients at PSCs were statistically significantly older than CSCs. The most prevalent comorbid conditions in both PSCs and CSCs were hypertension, diabetes mellitus, and dyslipidemia. Based on our categorization, we found that 45% of patients admitted to CSCs had moderate‐to‐severe stroke (NIHSS>5). The median door‐to‐thrombolytic drug times were significantly shorter at CSCs than PSCs for both the 3‐hour (65 vs 74 minutes, P<0.0001) and 4.5 hour (65 vs 76 minutes, P<0.0001) IV tPA time windows.

The incidences of stroke diagnosis types are also detailed in Table 2. Seventy percent of patients at CSCs had either an ischemic or hemorrhagic stroke diagnosis versus 57.1% of patients admitted at PSCs. Hemorrhagic stroke patients were twice as likely to be admitted at CSCs compared to PSCs.

After excluding 13,964 patients with a diagnosis of TIA, stroke not otherwise specified, and those with nonstroke‐related diagnosis, the likelihood of stroke patients' receiving the JC's performance measure services at either of these hospital levels was assessed (Table 3). In general, the adjusted odds ratio estimates of patients receiving a JC core performance measure at PSCs were lower than CSCs, indicating better compliance with the measures at CSCs. For example, 19.5% of eligible patients received thrombolytic therapy at CSCs compared to 9.6% at PSCs. CSCs also were more likely to provide VTE prophylaxis, anticoagulation for atrial fibrillation, and assessment for rehabilitation. Stroke education and antithrombotic therapy by the end of hospital day 2 were more likely to be provided at PSCs, but the results were not statistically significant.

DISCUSSION

In New Jersey, CSCs were more likely to adhere better to JC core performance measures than PSCs. Median door‐to‐thrombolytic drug times were also significantly lower at CSCs. Such differences may be due to several factors including the fact that CSCs have generally been state designated for a longer period of time than PSCs. CSCs are likely to have higher volumes of stroke admissions, are more likely to be JC certified, provide more staff education, and have more staff and resources. The New Jersey stroke designation program began in 2006, and 11 of the 12 CSCs were designated by the end of 2007. However, the PSC designation process has been more gradual, with several of them designated in 2010 and 2011 as the data for this study were being collected.

The New York State Stroke Center Designation Project prospectively showed that stroke center designation improved the quality of acute stroke patient care and administration of thrombolytic therapy; however, differing levels of hospital designation were not present in New York at that time.[11] Participation in a data measurement program such as Get With The Guidelines has also been examined. It is evident that the amount of time in a program is predictive of process measure compliance.[12] JC certification as a PSC is also associated with increased thrombolytic rates for acute stroke over time.[13] New Jersey does not require that stroke‐designated hospitals have JC stroke certification. Although 11 New Jersey CSCs have been certified as JC PSCs since 2009, only 21 of the 53 state‐designated PSCs are JC certified. It may be that the highest performing sites pursue state CSC designation and JC PSC certification/recertification repeatedly. CSCs in New Jersey may also have a greater focus on quality measures by virtue of having been in quality programs such as Get With The Guidelines or by having been state designated and JC certified for a longer period of time.

The New Jersey requirements for CSCs, like those of the JC, include a large number of highly trained stroke experts, which ensures more continuous coverage. Although a disparity in mortality on weekends versus weekdays has been reported,[14] such a difference in mortality has not been seen at CSCs in New Jersey.[15] This lack of a weekend effect is felt to be related to the 24/7 availability of stroke specialists, advanced neuroimaging, ongoing training, and surveillance of specialized nursing care available at CSCs.[4, 16]

In our study, New Jersey CSCs overall had significantly higher rates of thrombolysis compared to PSCs (19.5% vs 9.6%) when looking at the 3‐hour window. This is higher overall than the national rate of 3.4% to 5.2%.[17] The number of patients treated in the expanded thrombolytic window were also significantly higher at CSCs, increasing thrombolysis rates to 22.4% at CSCs versus the 11% at PSCs. Door‐to‐drug times were also shorter at CSCs than PSCs in the 3‐ and 4.5‐hour windows (65 vs 74 minutes and 65 minutes vs 76 minutes, respectively). After we excluded transferred patients and those with a diagnosis of TIA, stroke not otherwise specified, and those with nonstroke‐related diagnoses, the total number of ischemic and hemorrhagic stroke patients seen at each of the 12 CSCs (n=11,505) was on average 4 times higher than the number seen at each of the 53 PSCs (n=12,732). High annual hospital stroke volume has been shown to be associated with higher rates of thrombolysis and lower stroke mortality.[14] A study of US academic centers found that although the same percentage of patients presented within 2 hours of stroke symptom onset in 2001 and 2004, the use of IV tPA more than doubled over this time period.[18] Improved system organization at the prehospital and hospital levels as well as greater comfort and experience with use of thrombolytic therapy likely contribute to all of these findings.[11]

CSCs did not outperform PSCs with regard to stroke education and antithrombotics by end of hospital day 2, but these results were not statistically significant. The former measure includes only stroke patients who are discharged home and is considered complete when all 5 of the following are addressed: risk factors for stroke, warning signs and symptoms for stroke, activation of emergency medical systems, follow‐up after discharge, and medications prescribed at discharge. CSCs were more likely to provide education for the first 3 and last component but less likely for the fourth element. These findings should be considered in the context of CSCs having higher volumes of more ill and complex patients who are more likely to be discharged to a rehabilitation hospital, nursing home, or other facility than to home. In our registry, CSCs discharged 46% of patients' to home versus 54% at PSCs. We speculate that CSCs may be less likely to habitually address follow‐up care and discharge medications as compared to PSCs. As far as provision of antithrombotics by hospital day 2, it is possible again that because CSCs have a higher number of complicated stroke patients, many may have had contraindications to use of antithrombotics in that time period.

Limitations of this study include the fact that this was a retrospective analysis of a database. Although the 2010 and 2011 NJASR dataset was sizeable, it was not possible to capture all potentially confounding variables that may have affected our point estimates. We were not able to perform a hierarchical analysis to account for clustering at the hospital level because of limited data available in the registry. Errors in recording data, coding, and documentation cannot be excluded. The fact that not all PSCs were necessarily JC certified may have contributed to the observed differences. Also, because pursuing PSC or CSC status is voluntary, it is not clear if the hospitals that chose CSC status were different in other unmeasured factors than those that chose PSC status, and the difference may have existed even in the absence of the designation program. Over the years, there have been changes in the criteria required by the state and the JC for PSC designation, although the larger differences between hospital levels remained intact. This may have limited our findings as well. The goal for hospitals is to continue strict adherence to policies and measures and thus improve quality of care for stroke patients. Future prospective studies should be conducted to ascertain validity and generalizability of our findings. Association of stroke measure adherence and functional outcomes would also be of interest. We were not able to measure this consistently in our study because not all patients at PSCs had admission and/or discharge NIHSS or modified Rankin Score. Although some studies have not shown an association between improved outcomes and higher performance on quality measures, we would like to look at this more closely in the stroke population.[19] As our database gets larger, we would like to reexamine our findings after correcting for more specific characteristics of each hospital. In the future, if additional states designate centers by level of stroke care, it will be important to compare how such designations compare to nonprofit organization certifications in terms of impacting performance on a larger scale.

CONCLUSION

This study shows better compliance of New Jersey state‐designated CSCs with the JC PSC core stroke measures and better mean door‐to‐thrombolytic drug times. Because these measures are evidence based, these results may translate into better stroke care and outcomes for patients treated at state‐designated CSCs.

Stroke is the fourth leading cause of death in the United States.[1] Though actual stroke‐related death has declined nationally by 19.4%, stroke‐related morbidity is still a significant burden.[2, 3] Hospital certification programs have been developed to improve the quality of stroke care on state and national levels. The Brain Attack Coalition (BAC) proposed 2 levels of stroke hospitals: primary stroke centers (PSCs) and comprehensive stroke centers (CSCs).[3, 4] Although most stroke patients can be cared for at PSCs, CSCs are able to care for more complex stroke patients.[4, 5] Using BAC recommendations the Joint Commission (JC) and American Heart Association/American Stroke Association created a certification program for PSCs. Eight evidence‐based performance measures are currently required for JC PSC certification.[6]

At the state level, New Jersey and Florida began designating PSCs and CSCs.[7, 8] New Jersey PSC and CSC designation criteria incorporate the elements of JC PSC certification, despite preceding them by several years.[6, 9] New Jersey CSC certification consists of more comprehensive requirements (Table 1). All New Jersey‐designated stroke centers submit data in the New Jersey Acute Stroke Registry (NJASR),[7] which closely matches the Centers for Disease Control and Prevention's Paul Coverdell National Acute Stroke Registry and includes the JC‐required core measures.

There is a paucity of data comparing state‐designated CSCs and PSCs, largely because few states have state designation programs. Although a recent observational study from Finland showed better outcomes in patients treated at CSCs, measures in that study were limited to mortality and institutionalization at 1 year.[10] In this study, we examined adherence of all New Jersey state‐designated stroke centers to the JC PSC measures and compared CSCs to PSCs in this regard. We posited that better compliance with these evidence‐based measures might translate into better quality of stroke care in the state and may lend support to future, larger studies that may be conducted because of the recent certification of CSCs by the JC.

METHODS

Components of the NJASR, key components of PSCs and CSCs in the BAC report, and the 8 JC's core stroke measures for PSC certification were assessed.[3, 4, 6]

First responders in New Jersey are required to bring suspected stroke patients to the nearest stroke‐designated hospital, regardless of whether it is a PSC or CSC, unless the patient is too medically unstable and needs to be taken to the nearest hospital. From there, decisions can be made to transfer a patient to a higher level hospital (CSC) depending on the complexity of the patient's condition.

New Jersey state‐designated PSCs and CSCs are required to abstract patient‐level data, evaluate outcomes, and initiate quality improvement activities on all patients evaluated for ischemic stroke, hemorrhagic stroke, transient ischemic attack (TIA), and those who undergo acute interventional therapy. Data are submitted quarterly to the New Jersey Department of Health and Senior Services (NJDHSS).[7] Hospital data are imported into the Acute Stroke Registry Database. The NJASR statewide dataset used for this analysis included all stroke admissions for the calendar years 2010 and 2011 and contains patient demographic information, health history, clinical investigations performed, treatments provided, and outcome measures that allow for risk‐adjusted assessment of outcomes.

The JC core stroke measures (STK‐1 thru 10 [except STK‐7 and 9]: venous thromboembolism [VTE] prophylaxis, discharged on antithrombotic therapy, anticoagulation therapy for atrial fibrillation/flutter, thrombolytic therapy, antithrombotic therapy by the end of hospital day 2, discharged on statin medication, stroke education, and assessment for rehabilitation) (except STK‐7 [dysphagia screening] and STK‐9 [smoking cessation/advice counseling]) apply only to acute ischemic and/or hemorrhagic stroke patients.

In our analysis, transferred patients and patients with a diagnosis of TIA, stroke not otherwise specified, and nonstroke‐related diagnoses were excluded. Hospital identity was kept anonymous through assignment of random numeric codes by the NJDHSS. Hospitals were categorized as CSC or PSC based on NJDHSS designation. Stroke severity on admission was assessed by categorizing National Institutes of Health Stroke Scale (NIHSS) scores into: no stroke when NIHSS=0, mild stroke when NIHSS=14, and moderate to severe stroke when NIHSS>5. Median door‐to‐thrombolytic drug times were assessed for both patients who arrived to the hospital within 2 hours of stroke symptom onset and received thrombolytic therapy within 3 hours, as well as patients who arrived within 3.5 hours (210 minutes) and received treatment within 4.5 hours (270 minutes).

RESULTS

There were 36,892 acute stroke cases treated at the 53 New Jersey PSCs and 12 CSCs in the calendar year 2010 and 2011 (Table 2). Sixty percent were treated at PSCs and 40% at CSCs. There were significant differences in the distribution of patients' characteristics (race, age, and gender) between the 2 hospital levels. At both PSCs and CSCs, the majority of patients were white, distantly followed by blacks. Patients at PSCs were statistically significantly older than CSCs. The most prevalent comorbid conditions in both PSCs and CSCs were hypertension, diabetes mellitus, and dyslipidemia. Based on our categorization, we found that 45% of patients admitted to CSCs had moderate‐to‐severe stroke (NIHSS>5). The median door‐to‐thrombolytic drug times were significantly shorter at CSCs than PSCs for both the 3‐hour (65 vs 74 minutes, P<0.0001) and 4.5 hour (65 vs 76 minutes, P<0.0001) IV tPA time windows.

The incidences of stroke diagnosis types are also detailed in Table 2. Seventy percent of patients at CSCs had either an ischemic or hemorrhagic stroke diagnosis versus 57.1% of patients admitted at PSCs. Hemorrhagic stroke patients were twice as likely to be admitted at CSCs compared to PSCs.

After excluding 13,964 patients with a diagnosis of TIA, stroke not otherwise specified, and those with nonstroke‐related diagnosis, the likelihood of stroke patients' receiving the JC's performance measure services at either of these hospital levels was assessed (Table 3). In general, the adjusted odds ratio estimates of patients receiving a JC core performance measure at PSCs were lower than CSCs, indicating better compliance with the measures at CSCs. For example, 19.5% of eligible patients received thrombolytic therapy at CSCs compared to 9.6% at PSCs. CSCs also were more likely to provide VTE prophylaxis, anticoagulation for atrial fibrillation, and assessment for rehabilitation. Stroke education and antithrombotic therapy by the end of hospital day 2 were more likely to be provided at PSCs, but the results were not statistically significant.

DISCUSSION

In New Jersey, CSCs were more likely to adhere better to JC core performance measures than PSCs. Median door‐to‐thrombolytic drug times were also significantly lower at CSCs. Such differences may be due to several factors including the fact that CSCs have generally been state designated for a longer period of time than PSCs. CSCs are likely to have higher volumes of stroke admissions, are more likely to be JC certified, provide more staff education, and have more staff and resources. The New Jersey stroke designation program began in 2006, and 11 of the 12 CSCs were designated by the end of 2007. However, the PSC designation process has been more gradual, with several of them designated in 2010 and 2011 as the data for this study were being collected.

The New York State Stroke Center Designation Project prospectively showed that stroke center designation improved the quality of acute stroke patient care and administration of thrombolytic therapy; however, differing levels of hospital designation were not present in New York at that time.[11] Participation in a data measurement program such as Get With The Guidelines has also been examined. It is evident that the amount of time in a program is predictive of process measure compliance.[12] JC certification as a PSC is also associated with increased thrombolytic rates for acute stroke over time.[13] New Jersey does not require that stroke‐designated hospitals have JC stroke certification. Although 11 New Jersey CSCs have been certified as JC PSCs since 2009, only 21 of the 53 state‐designated PSCs are JC certified. It may be that the highest performing sites pursue state CSC designation and JC PSC certification/recertification repeatedly. CSCs in New Jersey may also have a greater focus on quality measures by virtue of having been in quality programs such as Get With The Guidelines or by having been state designated and JC certified for a longer period of time.

The New Jersey requirements for CSCs, like those of the JC, include a large number of highly trained stroke experts, which ensures more continuous coverage. Although a disparity in mortality on weekends versus weekdays has been reported,[14] such a difference in mortality has not been seen at CSCs in New Jersey.[15] This lack of a weekend effect is felt to be related to the 24/7 availability of stroke specialists, advanced neuroimaging, ongoing training, and surveillance of specialized nursing care available at CSCs.[4, 16]

In our study, New Jersey CSCs overall had significantly higher rates of thrombolysis compared to PSCs (19.5% vs 9.6%) when looking at the 3‐hour window. This is higher overall than the national rate of 3.4% to 5.2%.[17] The number of patients treated in the expanded thrombolytic window were also significantly higher at CSCs, increasing thrombolysis rates to 22.4% at CSCs versus the 11% at PSCs. Door‐to‐drug times were also shorter at CSCs than PSCs in the 3‐ and 4.5‐hour windows (65 vs 74 minutes and 65 minutes vs 76 minutes, respectively). After we excluded transferred patients and those with a diagnosis of TIA, stroke not otherwise specified, and those with nonstroke‐related diagnoses, the total number of ischemic and hemorrhagic stroke patients seen at each of the 12 CSCs (n=11,505) was on average 4 times higher than the number seen at each of the 53 PSCs (n=12,732). High annual hospital stroke volume has been shown to be associated with higher rates of thrombolysis and lower stroke mortality.[14] A study of US academic centers found that although the same percentage of patients presented within 2 hours of stroke symptom onset in 2001 and 2004, the use of IV tPA more than doubled over this time period.[18] Improved system organization at the prehospital and hospital levels as well as greater comfort and experience with use of thrombolytic therapy likely contribute to all of these findings.[11]

CSCs did not outperform PSCs with regard to stroke education and antithrombotics by end of hospital day 2, but these results were not statistically significant. The former measure includes only stroke patients who are discharged home and is considered complete when all 5 of the following are addressed: risk factors for stroke, warning signs and symptoms for stroke, activation of emergency medical systems, follow‐up after discharge, and medications prescribed at discharge. CSCs were more likely to provide education for the first 3 and last component but less likely for the fourth element. These findings should be considered in the context of CSCs having higher volumes of more ill and complex patients who are more likely to be discharged to a rehabilitation hospital, nursing home, or other facility than to home. In our registry, CSCs discharged 46% of patients' to home versus 54% at PSCs. We speculate that CSCs may be less likely to habitually address follow‐up care and discharge medications as compared to PSCs. As far as provision of antithrombotics by hospital day 2, it is possible again that because CSCs have a higher number of complicated stroke patients, many may have had contraindications to use of antithrombotics in that time period.

Limitations of this study include the fact that this was a retrospective analysis of a database. Although the 2010 and 2011 NJASR dataset was sizeable, it was not possible to capture all potentially confounding variables that may have affected our point estimates. We were not able to perform a hierarchical analysis to account for clustering at the hospital level because of limited data available in the registry. Errors in recording data, coding, and documentation cannot be excluded. The fact that not all PSCs were necessarily JC certified may have contributed to the observed differences. Also, because pursuing PSC or CSC status is voluntary, it is not clear if the hospitals that chose CSC status were different in other unmeasured factors than those that chose PSC status, and the difference may have existed even in the absence of the designation program. Over the years, there have been changes in the criteria required by the state and the JC for PSC designation, although the larger differences between hospital levels remained intact. This may have limited our findings as well. The goal for hospitals is to continue strict adherence to policies and measures and thus improve quality of care for stroke patients. Future prospective studies should be conducted to ascertain validity and generalizability of our findings. Association of stroke measure adherence and functional outcomes would also be of interest. We were not able to measure this consistently in our study because not all patients at PSCs had admission and/or discharge NIHSS or modified Rankin Score. Although some studies have not shown an association between improved outcomes and higher performance on quality measures, we would like to look at this more closely in the stroke population.[19] As our database gets larger, we would like to reexamine our findings after correcting for more specific characteristics of each hospital. In the future, if additional states designate centers by level of stroke care, it will be important to compare how such designations compare to nonprofit organization certifications in terms of impacting performance on a larger scale.

CONCLUSION

This study shows better compliance of New Jersey state‐designated CSCs with the JC PSC core stroke measures and better mean door‐to‐thrombolytic drug times. Because these measures are evidence based, these results may translate into better stroke care and outcomes for patients treated at state‐designated CSCs.

Stroke is the fourth leading cause of death in the United States.[1] Though actual stroke‐related death has declined nationally by 19.4%, stroke‐related morbidity is still a significant burden.[2, 3] Hospital certification programs have been developed to improve the quality of stroke care on state and national levels. The Brain Attack Coalition (BAC) proposed 2 levels of stroke hospitals: primary stroke centers (PSCs) and comprehensive stroke centers (CSCs).[3, 4] Although most stroke patients can be cared for at PSCs, CSCs are able to care for more complex stroke patients.[4, 5] Using BAC recommendations the Joint Commission (JC) and American Heart Association/American Stroke Association created a certification program for PSCs. Eight evidence‐based performance measures are currently required for JC PSC certification.[6]

At the state level, New Jersey and Florida began designating PSCs and CSCs.[7, 8] New Jersey PSC and CSC designation criteria incorporate the elements of JC PSC certification, despite preceding them by several years.[6, 9] New Jersey CSC certification consists of more comprehensive requirements (Table 1). All New Jersey‐designated stroke centers submit data in the New Jersey Acute Stroke Registry (NJASR),[7] which closely matches the Centers for Disease Control and Prevention's Paul Coverdell National Acute Stroke Registry and includes the JC‐required core measures.

There is a paucity of data comparing state‐designated CSCs and PSCs, largely because few states have state designation programs. Although a recent observational study from Finland showed better outcomes in patients treated at CSCs, measures in that study were limited to mortality and institutionalization at 1 year.[10] In this study, we examined adherence of all New Jersey state‐designated stroke centers to the JC PSC measures and compared CSCs to PSCs in this regard. We posited that better compliance with these evidence‐based measures might translate into better quality of stroke care in the state and may lend support to future, larger studies that may be conducted because of the recent certification of CSCs by the JC.

METHODS

Components of the NJASR, key components of PSCs and CSCs in the BAC report, and the 8 JC's core stroke measures for PSC certification were assessed.[3, 4, 6]

First responders in New Jersey are required to bring suspected stroke patients to the nearest stroke‐designated hospital, regardless of whether it is a PSC or CSC, unless the patient is too medically unstable and needs to be taken to the nearest hospital. From there, decisions can be made to transfer a patient to a higher level hospital (CSC) depending on the complexity of the patient's condition.

New Jersey state‐designated PSCs and CSCs are required to abstract patient‐level data, evaluate outcomes, and initiate quality improvement activities on all patients evaluated for ischemic stroke, hemorrhagic stroke, transient ischemic attack (TIA), and those who undergo acute interventional therapy. Data are submitted quarterly to the New Jersey Department of Health and Senior Services (NJDHSS).[7] Hospital data are imported into the Acute Stroke Registry Database. The NJASR statewide dataset used for this analysis included all stroke admissions for the calendar years 2010 and 2011 and contains patient demographic information, health history, clinical investigations performed, treatments provided, and outcome measures that allow for risk‐adjusted assessment of outcomes.

The JC core stroke measures (STK‐1 thru 10 [except STK‐7 and 9]: venous thromboembolism [VTE] prophylaxis, discharged on antithrombotic therapy, anticoagulation therapy for atrial fibrillation/flutter, thrombolytic therapy, antithrombotic therapy by the end of hospital day 2, discharged on statin medication, stroke education, and assessment for rehabilitation) (except STK‐7 [dysphagia screening] and STK‐9 [smoking cessation/advice counseling]) apply only to acute ischemic and/or hemorrhagic stroke patients.

In our analysis, transferred patients and patients with a diagnosis of TIA, stroke not otherwise specified, and nonstroke‐related diagnoses were excluded. Hospital identity was kept anonymous through assignment of random numeric codes by the NJDHSS. Hospitals were categorized as CSC or PSC based on NJDHSS designation. Stroke severity on admission was assessed by categorizing National Institutes of Health Stroke Scale (NIHSS) scores into: no stroke when NIHSS=0, mild stroke when NIHSS=14, and moderate to severe stroke when NIHSS>5. Median door‐to‐thrombolytic drug times were assessed for both patients who arrived to the hospital within 2 hours of stroke symptom onset and received thrombolytic therapy within 3 hours, as well as patients who arrived within 3.5 hours (210 minutes) and received treatment within 4.5 hours (270 minutes).

RESULTS

There were 36,892 acute stroke cases treated at the 53 New Jersey PSCs and 12 CSCs in the calendar year 2010 and 2011 (Table 2). Sixty percent were treated at PSCs and 40% at CSCs. There were significant differences in the distribution of patients' characteristics (race, age, and gender) between the 2 hospital levels. At both PSCs and CSCs, the majority of patients were white, distantly followed by blacks. Patients at PSCs were statistically significantly older than CSCs. The most prevalent comorbid conditions in both PSCs and CSCs were hypertension, diabetes mellitus, and dyslipidemia. Based on our categorization, we found that 45% of patients admitted to CSCs had moderate‐to‐severe stroke (NIHSS>5). The median door‐to‐thrombolytic drug times were significantly shorter at CSCs than PSCs for both the 3‐hour (65 vs 74 minutes, P<0.0001) and 4.5 hour (65 vs 76 minutes, P<0.0001) IV tPA time windows.

The incidences of stroke diagnosis types are also detailed in Table 2. Seventy percent of patients at CSCs had either an ischemic or hemorrhagic stroke diagnosis versus 57.1% of patients admitted at PSCs. Hemorrhagic stroke patients were twice as likely to be admitted at CSCs compared to PSCs.

After excluding 13,964 patients with a diagnosis of TIA, stroke not otherwise specified, and those with nonstroke‐related diagnosis, the likelihood of stroke patients' receiving the JC's performance measure services at either of these hospital levels was assessed (Table 3). In general, the adjusted odds ratio estimates of patients receiving a JC core performance measure at PSCs were lower than CSCs, indicating better compliance with the measures at CSCs. For example, 19.5% of eligible patients received thrombolytic therapy at CSCs compared to 9.6% at PSCs. CSCs also were more likely to provide VTE prophylaxis, anticoagulation for atrial fibrillation, and assessment for rehabilitation. Stroke education and antithrombotic therapy by the end of hospital day 2 were more likely to be provided at PSCs, but the results were not statistically significant.

DISCUSSION

In New Jersey, CSCs were more likely to adhere better to JC core performance measures than PSCs. Median door‐to‐thrombolytic drug times were also significantly lower at CSCs. Such differences may be due to several factors including the fact that CSCs have generally been state designated for a longer period of time than PSCs. CSCs are likely to have higher volumes of stroke admissions, are more likely to be JC certified, provide more staff education, and have more staff and resources. The New Jersey stroke designation program began in 2006, and 11 of the 12 CSCs were designated by the end of 2007. However, the PSC designation process has been more gradual, with several of them designated in 2010 and 2011 as the data for this study were being collected.

The New York State Stroke Center Designation Project prospectively showed that stroke center designation improved the quality of acute stroke patient care and administration of thrombolytic therapy; however, differing levels of hospital designation were not present in New York at that time.[11] Participation in a data measurement program such as Get With The Guidelines has also been examined. It is evident that the amount of time in a program is predictive of process measure compliance.[12] JC certification as a PSC is also associated with increased thrombolytic rates for acute stroke over time.[13] New Jersey does not require that stroke‐designated hospitals have JC stroke certification. Although 11 New Jersey CSCs have been certified as JC PSCs since 2009, only 21 of the 53 state‐designated PSCs are JC certified. It may be that the highest performing sites pursue state CSC designation and JC PSC certification/recertification repeatedly. CSCs in New Jersey may also have a greater focus on quality measures by virtue of having been in quality programs such as Get With The Guidelines or by having been state designated and JC certified for a longer period of time.

The New Jersey requirements for CSCs, like those of the JC, include a large number of highly trained stroke experts, which ensures more continuous coverage. Although a disparity in mortality on weekends versus weekdays has been reported,[14] such a difference in mortality has not been seen at CSCs in New Jersey.[15] This lack of a weekend effect is felt to be related to the 24/7 availability of stroke specialists, advanced neuroimaging, ongoing training, and surveillance of specialized nursing care available at CSCs.[4, 16]

In our study, New Jersey CSCs overall had significantly higher rates of thrombolysis compared to PSCs (19.5% vs 9.6%) when looking at the 3‐hour window. This is higher overall than the national rate of 3.4% to 5.2%.[17] The number of patients treated in the expanded thrombolytic window were also significantly higher at CSCs, increasing thrombolysis rates to 22.4% at CSCs versus the 11% at PSCs. Door‐to‐drug times were also shorter at CSCs than PSCs in the 3‐ and 4.5‐hour windows (65 vs 74 minutes and 65 minutes vs 76 minutes, respectively). After we excluded transferred patients and those with a diagnosis of TIA, stroke not otherwise specified, and those with nonstroke‐related diagnoses, the total number of ischemic and hemorrhagic stroke patients seen at each of the 12 CSCs (n=11,505) was on average 4 times higher than the number seen at each of the 53 PSCs (n=12,732). High annual hospital stroke volume has been shown to be associated with higher rates of thrombolysis and lower stroke mortality.[14] A study of US academic centers found that although the same percentage of patients presented within 2 hours of stroke symptom onset in 2001 and 2004, the use of IV tPA more than doubled over this time period.[18] Improved system organization at the prehospital and hospital levels as well as greater comfort and experience with use of thrombolytic therapy likely contribute to all of these findings.[11]

CSCs did not outperform PSCs with regard to stroke education and antithrombotics by end of hospital day 2, but these results were not statistically significant. The former measure includes only stroke patients who are discharged home and is considered complete when all 5 of the following are addressed: risk factors for stroke, warning signs and symptoms for stroke, activation of emergency medical systems, follow‐up after discharge, and medications prescribed at discharge. CSCs were more likely to provide education for the first 3 and last component but less likely for the fourth element. These findings should be considered in the context of CSCs having higher volumes of more ill and complex patients who are more likely to be discharged to a rehabilitation hospital, nursing home, or other facility than to home. In our registry, CSCs discharged 46% of patients' to home versus 54% at PSCs. We speculate that CSCs may be less likely to habitually address follow‐up care and discharge medications as compared to PSCs. As far as provision of antithrombotics by hospital day 2, it is possible again that because CSCs have a higher number of complicated stroke patients, many may have had contraindications to use of antithrombotics in that time period.

Limitations of this study include the fact that this was a retrospective analysis of a database. Although the 2010 and 2011 NJASR dataset was sizeable, it was not possible to capture all potentially confounding variables that may have affected our point estimates. We were not able to perform a hierarchical analysis to account for clustering at the hospital level because of limited data available in the registry. Errors in recording data, coding, and documentation cannot be excluded. The fact that not all PSCs were necessarily JC certified may have contributed to the observed differences. Also, because pursuing PSC or CSC status is voluntary, it is not clear if the hospitals that chose CSC status were different in other unmeasured factors than those that chose PSC status, and the difference may have existed even in the absence of the designation program. Over the years, there have been changes in the criteria required by the state and the JC for PSC designation, although the larger differences between hospital levels remained intact. This may have limited our findings as well. The goal for hospitals is to continue strict adherence to policies and measures and thus improve quality of care for stroke patients. Future prospective studies should be conducted to ascertain validity and generalizability of our findings. Association of stroke measure adherence and functional outcomes would also be of interest. We were not able to measure this consistently in our study because not all patients at PSCs had admission and/or discharge NIHSS or modified Rankin Score. Although some studies have not shown an association between improved outcomes and higher performance on quality measures, we would like to look at this more closely in the stroke population.[19] As our database gets larger, we would like to reexamine our findings after correcting for more specific characteristics of each hospital. In the future, if additional states designate centers by level of stroke care, it will be important to compare how such designations compare to nonprofit organization certifications in terms of impacting performance on a larger scale.

CONCLUSION

This study shows better compliance of New Jersey state‐designated CSCs with the JC PSC core stroke measures and better mean door‐to‐thrombolytic drug times. Because these measures are evidence based, these results may translate into better stroke care and outcomes for patients treated at state‐designated CSCs.