Ambulatory care–sensitive conditions can be systematically assessed in a large electronic medical database to describe admission rates by year, catchment area, and hospital affiliation.
Objectives: This study assessed rates of ambulatory care—sensitive condition (ACSC) admissions within a healthcare system to identify areas for intervention.
Study Design: This was a multiyear cross-sectional study using the data warehouse of Clalit Health Services (Clalit), the largest payer/provider healthcare system in Israel, with complete clinical records for more than 4 million members. All admissions from 2009 to 2014 were included in the study. Discharge diagnoses were identified using International Classification of Diseases, Ninth Revision codes.
Methods: We provide adjusted rates (per 100,000 Clalit population adjusted by age and sex to the 2005 Organisation for Economic Co-operation and Development population) for all admissions, by discharge diagnoses, for each year. We identify the highest adjusted rates (relative and absolute) by both catchment area and hospital affiliation (Clalit or non-Clalit).
Results: ACSC-related admissions made up 16.2% of all admissions for the 5 years studied, and the overall rate increased by 26.8% from 2009 to 2014. The conditions with the highest admission rates in all years and all catchment areas were pneumonia and congestive heart failure. There was extreme variation among catchment areas for hypertension-related admissions. Within the Clalit hospitals, ACSCs accounted for 20.5% of admissions; within non-Clalit hospitals, ACSCs accounted for 13.6% of admissions.
Conclusions: In evaluating the rates of ACSC-related admissions, this study demonstrates the contribution of a single, longitudinal benchmark. This study also suggests that hypertension, congestive heart failure, and pneumonia may be areas for future intervention in Clalit.
Am J Manag Care. 2020;26(5):e155-e161. https://doi.org/10.37765/ajmc.2020.43158
Admissions for ambulatory care—sensitive conditions (ACSCs) may be preventable, and therefore it has been suggested that the rates of ACSC-related admissions in the inpatient setting reflect on the quality of care in the outpatient setting. This concept was described in 1976 by Rutstein et al and underscored by the contribution of outpatient care in reducing the risk of admission among certain disease conditions.1-6 Several studies have aimed to identify which elements of primary care might be associated with an increased risk of ACSC-related admissions, such as number of primary care physicians per capita, access to care, and level of health education.7-9
Additional studies have suggested that access, as indicated by sociodemographic variables, such as socioeconomic status and ethnicity, can also significantly contribute to rates of ACSC-related admissions, even independent of the quality of available primary care.10-12 Additional risk factors have been suggested, such as mental health,13 race/ethnicity,14 and coexisting morbidities.15,16 Study findings also show that the economic impact and burden of ostensibly preventable admissions is significant, and it has been suggested that an investment in access to high-quality primary care might therefore ultimately be cost-saving.17,18
However, although many agree that ACSC-related admissions might be preventable, the benchmarking of rates of ACSC-related admissions has not been standardized across health systems.19 This is mainly due to the fact that the majority of available electronic databases do not have individual-level data linking outpatient and inpatient care and depend instead on population-level claims data.4,20 Furthermore, rates of ACSC-related admissions have been reported using multiple measures and definitions, including chronic and acute groupings,6,16,20,21 relative risks,22 percentage of admissions,23 person-years, by age bracket,24 among the elderly,25-27 and only among Medicare populations.15,28 This variation in study design and populations prevents any single system from comparing performance across health systems.
Several coding methods in various countries have been proposed to create standardized rates,7,11,29-31 including in England,32 Ireland,33 and Italy,34 as well as by the Organisation for Economic Co-operation and Development (OECD)31 and the Agency for Healthcare Research and Quality.30 The most comprehensive taxonomy study outlines the most commonly defined 19 conditions, divided into acute and chronic, and lists the specific International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes associated with each condition.29
In this study, we report on ACSC-related admissions within a healthcare system that integrates inpatient and outpatient services so that we can identify areas that can potentially benefit from further investigation of the primary care services. Specifically, we describe and compare the rates of ACSC-related admissions using individual-level comprehensive data in several divisions (years, catchment, affiliation) as a suggestion of how such benchmarks using admission data of a single large healthcare provider can inform healthcare managers and policy makers.
This was a retrospective cross-sectional study using the data warehouse of Clalit Health Services (Clalit), a payer/provider health fund covering more than 53% of Israeli residents. There is minimal attrition (~1%) among the 4 major health funds in Israel, resulting in low loss to follow-up within the electronic health record system.35,36 As such, Clalit’s data warehouse contains highly comprehensive clinical and administrative records for its more than 4.5 million members. Clalit is the only healthcare fund in Israel that is nationally distributed37,38 and owns and runs its own hospitals. Thus, the records maintained by the system include those of both inpatient and outpatient services, as well as of ancillary services such as pharmacies, laboratories, and imaging. In Israel, health coverage is mandatory and universal by law, such that all residents have equal access to the same basic “basket” of services. In the case of inpatient admissions, all residents can receive care from any one of the hospitals, Clalit or non-Clalit (which are either private or publicly run). This study was approved by the Clalit internal review board.
All Clalit members as of January 1 of each year of the study, from 2009 to 2014, were eligible to be included in the study. Members 1 year and older were required to have at least 1 year of continuous membership. Any Clalit member who was born after January 1 of a given year or younger than 1 year as of January 1 of any given year was also included in the population during that year, without any requirement for continuity of membership. All urgent inpatient admissions of any Clalit member to any hospital that lasted for at least 24 hours between 2009 and 2014 were included in the study. Individual-level data for adjusting were extracted as of January 1 for each given year, or by date of birth among those born during the course of a given year, and included age, sex, residence based on clinic catchment area, and hospital type (Clalit network [hospitals owned and operated by Clalit] or Clalit contracted [hospitals owned and operated outside of the Clalit system]).
ACSC-Related Admission Definition
Each admission was defined as either ACSC-related or non—ACSC-related based on the primary diagnosis associated with the admission. An ACSC-related admission was defined if there was at least 1 diagnosis from ICD-10-CM codes specific to at least 1 of the 19 disease conditions as identified by Purdy et al (see eAppendix Table 1 [available at ajmc.com]).29 ICD-10-CM codes were manually converted to ICD-9-CM, which is the primary system used in Clalit, and independently reviewed by 2 clinicians. The ACSC admissions were further subcategorized by disease condition. If more than 1 ACSC code was present upon discharge, the first occurrence of a condition during the admission was considered the defining ACSC condition.
Rates of ACSC-related admissions are provided for each of the 19 ACSC disease conditions and all non—ACSC-related admissions per 100,000 Clalit members, adjusted by age and sex relative to the 2005 OECD population distribution. They are also stratified by year and catchment area (accordingly numbered 1-9). Finally, the percentages of admissions to Clalit and non-Clalit hospitals are also presented.
All analyses were performed using SAS software version 9.0 for Windows (SAS Institute Inc; Cary, North Carolina).
The highest rate of ACSC-related admissions among acute conditions was for pneumonia and increased by 14.9% (from 290.0 to 333.1) from 2009 to 2014, as seen in Table 1. The highest rate of ACSC-related admissions among chronic conditions was for congestive heart failure and increased by 44.6% (from 199.0 to 287.7) from 2009 to 2014. Overall, ACSC-related admissions increased by 26.8% between 2009 and 2014, and the majority of types of ACSC-related admissions, acute and chronic, increased during that time. Cellulitis and hypertension were among the top 5 contributors to ACSC-related admission, with increases of 47.5% and 92.4%, respectively. These yearly graphs are seen in the Figure (A and B).
The variation across catchment areas in ACSC-related admissions during 2014 is presented in Table 2 (full table presented in eAppendix Table 2). The disease conditions with age-/sex-adjusted rates that had the top 5 highest SDs were hypertension; congestive heart failure; pneumonia; ear, nose, and throat conditions; and cellulitis. Together with angina, dehydration, and epilepsy, these 8 disease conditions made up the top 5 contributors in all catchment areas. The top 5 conditions contributed to between 63% and 74% of all ACSC-related admissions by catchment area.
In some catchment areas, the percentage increase compared with the total for an ACSC-related admission was highest for a disease condition that was not otherwise in the top 5 conditions in the total population. For example, in catchment area 2, the rate of admissions for chronic obstructive pulmonary disease (COPD) increased to 117.0 from 63.0 (85.8%). Additionally, in catchment area 5, the rate of admission for anemia was 55.6 versus 35.4 (57.5% higher).
Of note, in catchment area 1, the difference of ACSC-related admission rates was roughly an order of magnitude higher than any other catchment area (more than 500%) for hypertension, which also showed the greatest change (more than 90% increase over the time period).
Of the 371,912 ACSC-related admissions of Clalit members, 48.0% were to a Clalit hospital, as seen in Table 3. However, among all admissions at non-Clalit hospitals, 13.6% were ACSC-related compared with 20.5% of all admissions to Clalit hospitals. This difference varied by ACSC; 61.2% of patients admitted for diabetes presented to a Clalit hospital, whereas 37.5% of patients with vaccine-preventable admissions presented to a non-Clalit hospital.
The rates of nearly all the measured ACSC-related admissions appear to be increasing over time. The conditions that contributed the most to the total ACSC-related rates also had the greatest variation by catchment area (hypertension; congestive heart failure; pneumonia; ear, nose, and throat conditions; and cellulitis). For several conditions (anemia and COPD), rates were relatively higher in some of the catchment areas compared with other areas. Additionally, the 2 most prominent ACSC-related admissions (pneumonia and congestive heart failure) were also the ones with the greatest apparent increase over time.
Of note, we found that non—ACSC-related admission rates decreased, as did the total admission rates. We note that this is consistent with findings from recent studies of inpatient admissions in the United States.39,40 We also believe that the increase in ACSC-related rates has several explanations in addition to the possibility that insufficient care is being delivered in the outpatient setting. For example, the introduction of the National Quality Indicators immediately prior to this study41 is likely to be responsible for a significant increase in the comprehensiveness of documentation and thus potentially the accuracy in diagnostic documentation in the inpatient setting as well in Clalit hospitals, where the clinicians have access to outpatient records.
Although non-Clalit hospitals have relatively fewer total ACSC-related admissions than Clalit hospitals (48.0% vs 52.0%), they admit a larger percentage of all admissions (20.5% vs 13.6%, which matches the bed ratios of the hospitals). In the Israeli system, patients can present to any emergency department, network or contracted. One explanation for our findings may be that members preferentially seek Clalit care for ACSC-related admissions (as one might hope, for continuity) and may instead seek care out of network for what have become urgent or emergency conditions. The ability to compare patient-level clinical data and ACSC-related admission rates among in-network and out-of-network hospitals in this study allows for an understanding of how administrative or technical documentation artifacts (such as those found in claims-based data) can be identified and can influence these rates. Specifically, hypertension-related admission rates may be reflective of administrative or technical issues that should be further examined to improve documentation that would allow for the accurate assessment of ACSC-related admission rates across hospital providers.
Our study is the first to translate a well-established concept into a formulaic methodology that can be replicated worldwide by benchmarking using standardized age-/sex- and OECD-adjusted rates. However, the results are supported by some of what has previously been described in the literature. For example, we calculated an ACSC admission rate for asthma of 60.4 in 2014 compared with the OECD average of 48.0 in 201231 and England’s rate of 124.9 in 2011.32 Also, we calculated an ACSC admission rate for diabetes of 63.0 in 2014 compared with the OECD average of 127.7 in 201231 and England’s rate of 87.4 in 2011.32 These findings suggest that although Israel has unique regional variables, the overall benchmark of care delivered to members for ACSCs is within the range of other countries in the OECD.
Limitations and Strengths
This study has a number of limitations. We do not compare baseline status (namely socioeconomic status and underlying comorbidities) because it limits our ability to present a single, standardized rate. It may be that the increasing rates of ACSC-related admissions may actually represent the burden on the healthcare system of caring for the aging population with multimorbid conditions. However, this explanation only furthers our conclusion: By identifying the rising rates of ACSC-related admissions, one can readily observe that the primary care setting may not be able to meet that increasing burden.
Additionally, we compare both chronic and acute conditions, which should, to a large extent, mitigate some of the influence of demographic or clinical factors of the patient (such as in perforated appendicitis). Furthermore, in this study, we do not compare length of stay, which may ultimately be a critical factor for policy makers determining which conditions have the greatest cost impact on the healthcare system. Finally, we include only a single healthcare system, which, despite being the largest and nationally distributed, is not representative of the entire population. Due to Clalit’s larger size and larger geographic distribution than other healthcare systems in Israel, it is possible that the reported ACSC hospitalization rates, particularly of chronic conditions, may be higher than the national average.
Strengths of our study include the ability to systematically create a single benchmarking model for evaluating multiple disease conditions by year, catchment area, and hospital type of a large, comprehensive healthcare provider. Additionally, this data set is one of the few that directly links outpatient-driven demographic data with inpatient admission data to allow for a fully integrated approach to benchmarking ACSC-driven admissions. Follow-up studies of this data set (or similar ones) can benefit from the linkage and determine whether consistency and continuity between the outpatient and inpatient setting are associated with reduced ACSC-driven admissions. Additional research questions could also consider the impact of clinical indices (eg, laboratory values, imaging results, medical use and adherence) and support services (eg, rehabilitation, physical therapy, case managers) on the rates of ACSC-driven admissions. Policy makers can use such a system to identify areas and conditions that are most likely to benefit from review of best practices, strengthening of health education, or direct approach through primary care delivery.
This study demonstrates the contribution of comparing ACSC-related admissions using a large electronic health record system to highlight yearly performance and variation by disease, catchment area, and hospital type. This measurement system can support healthcare managers and policy makers in understanding which areas of care may be underutilizing the primary care system. Healthcare managers should consider adopting a standardized reporting of ACSC-related admissions by year, area, and hospital type to better highlight areas of performance.Author Affiliations: Clalit Research Institute (ML-R, CC-S, MH, IG, RB), Tel Aviv, Israel; Icahn School of Medicine at Mount Sinai (ML-R), New York, NY; Clalit Health Services (MS, RB), Tel Aviv, Israel; Department of Epidemiology, Faculty of Health Sciences, Ben Gurion University (MS, RB), Be’er Sheva, Israel.
Source of Funding: None.
Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (ML-R, CC-S, MH, MS, RB); acquisition of data (ML-R, MH, IG, MS, RB); analysis and interpretation of data (ML-R, CC-S, MH, IG, MS); drafting of the manuscript (ML-R, CC-S, MS); critical revision of the manuscript for important intellectual content (ML-R, CC-S, MS, RB); statistical analysis (ML-R, MH, MS); provision of patients or study materials (IG); administrative, technical, or logistic support (IG); and supervision (RB).
Address Correspondence to: Maya Leventer-Roberts, MD, MPH, Clalit Research Institute, Arlozoroff 101, Tel Aviv, Israel 6209804. Email: firstname.lastname@example.org.REFERENCES
1. Laditka JN, Laditka SB, Probst JC. More may be better: evidence of a negative relationship between physician supply and hospitalization for ambulatory care sensitive conditions. Health Serv Res. 2005;40(4):1148-1166. doi: 10.1111/j.1475-6773.2005.00403.x.
2. Rutstein DD, Berenberg W, Chalmers TC, Child CG III, Fishman AP, Perrin EB. Measuring the quality of medical care—a clinical method. N Engl J Med. 1976;294(11):582-588. doi: 10.1056/NEJM197603112941104.
3. Rosano A, Loha CA, Falvo R, et al. The relationship between avoidable hospitalization and accessibility to primary care: a systematic review. Eur J Public Health. 2013;23(3):356-360. doi: 10.1093/eurpub/cks053.
4. Kringos D, Boerma W, Bourgueil Y, et al. The strength of primary care in Europe: an international comparative study. Br J Gen Pract. 2013;63(616):e742-e750. doi: 10.3399/bjgp13X674422.
5. Casalino LP, Pesko MF, Ryan AM, et al. Small primary care physician practices have low rates of preventable hospital admissions. Health Aff (Millwood). 2014;33(9):1680-1688. doi: 10.1377/hlthaff.2014.0434.
6. Caminal J, Starfield B, Sánchez E, Casanova C, Morales M. The role of primary care in preventing ambulatory care sensitive conditions. Eur J Public Health. 2004;14(3):246-251. doi: 10.1093/eurpub/14.3.246.
7. Freund T, Campbell SM, Geissler S, et al. Strategies for reducing potentially avoidable hospitalizations for ambulatory care—sensitive conditions. Ann Fam Med. 2013;11(4):363-370. doi: 10.1370/afm.1498.
8. Gibson OR, Segal L, McDermott RA. A systematic review of evidence on the association between hospitalisation for chronic disease related ambulatory care sensitive conditions and primary health care resourcing. BMC Health Serv Res. 2013;13:336. doi: 10.1186/1472-6963-13-336.
9. Orueta JF, García-Alvarez A, Grandes G, Nuño-Solinís R. Variability in potentially preventable hospitalisations: an observational study of clinical practice patterns of general practitioners and care outcomes in the Basque Country (Spain). BMJ Open. 2015;5(5):e007360. doi: 10.1136/bmjopen-2014-007360.
10. Billings J, Zeitel L, Lukomnik J, Carey TS, Blank AE, Newman L. Impact of socioeconomic status on hospital use in New York City. Health Aff (Millwood). 1993;12(1):162-173. doi: 10.1377/hlthaff.12.1.162.
11. Weiner JP, Starfield BH, Steinwachs DM, Mumford LM. Development and application of a population-oriented measure of ambulatory care case-mix. Med Care. 1991;29(5):452-472. doi: 10.1097/00005650-199105000-00006.
12. Roos LL, Walld R, Uhanova J, Bond R. Physician visits, hospitalizations, and socioeconomic status: ambulatory care sensitive conditions in a Canadian setting. Health Serv Res. 2005;40(4):1167-1185. doi: 10.1111/j.1475-6773.2005.00407.x.
13. Yoon J, Yano EM, Altman L, et al. Reducing costs of acute care for ambulatory care—sensitive medical conditions: the central roles of comorbid mental illness. Med Care. 2012;50(8):705-713. doi: 10.1097/MLR.0b013e31824e3379.
14. O’Neil SS, Lake T, Merrill A, Wilson A, Mann DA, Bartnyska LM. Racial disparities in hospitalizations for ambulatory care—sensitive conditions. Am J Prev Med. 2010;38(4):381-388. doi: 10.1016/j.amepre.2009.12.026.
15. Saver BG, Wang CY, Dobie SA, Green PK, Baldwin LM. The central role of comorbidity in predicting ambulatory care sensitive hospitalizations. Eur J Public Health. 2014;24(1):66-72. doi: 10.1093/eurpub/ckt019.
16. Gao J, Moran E, Li YF, Almenoff PL. Predicting potentially avoidable hospitalizations. Med Care. 2014;52(2):164-171. doi: 10.1097/MLR.0000000000000041.
17. Feachem RG, Sekhri NK, White KL. Getting more for their dollar: a comparison of the NHS with California’s Kaiser Permanente. BMJ. 2002;324(7330):135-141. doi: 10.1136/bmj.324.7330.135.
18. Axon RN, Gebregziabher M, Craig J, Zhang J, Mauldin P, Moran WP. Frequency and costs of hospital transfers for ambulatory care-sensitive conditions. Am J Manag Care. 2015;21(1):51-59.
19. Thygesen LC, Christiansen T, Garcia-Armesto S, Angulo-Pueyo E, Martínez-Lizaga N, Bernal-Delgado E; ECHO Consortium. Potentially avoidable hospitalizations in five European countries in 2009 and time trends from 2002 to 2009 based on administrative data. Eur J Public Health. 2015;25(suppl 1):35-43. doi: 10.1093/eurpub/cku227.
20. Weeks WB, Ventelou B, Paraponaris A. Rates of admission for ambulatory care sensitive conditions in France in 2009-2010: trends, geographic variation, costs, and an international comparison. Eur J Health Econ. 2016;17(4):453-470. doi: 10.1007/s10198-015-0692-y.
21. Strauss KJ, Goske MJ, Kaste SC, et al. Image gently: ten steps you can take to optimize image quality and lower CT dose for pediatric patients. AJR Am J Roentgenol. 2010;194(4):868-873. doi: 10.2214/AJR.09.4091.
22. Eggli Y, Desquins B, Seker E, Halfon P. Comparing potentially avoidable hospitalization rates related to ambulatory care sensitive conditions in Switzerland: the need to refine the definition of health conditions and to adjust for population health status. BMC Health Serv Res. 2014;14:25. doi: 10.1186/1472-6963-14-25.
23. Rizza P, Bianco A, Pavia M, Angelillo IF. Preventable hospitalization and access to primary health care in an area of Southern Italy. BMC Health Serv Res. 2007;7:134. doi: 10.1186/1472-6963-7-134.
24. Ansari Z, Haider SI, Ansari H, de Gooyer T, Sindall C. Patient characteristics associated with hospitalisations for ambulatory care sensitive conditions in Victoria, Australia. BMC Health Serv Res. 2012;12:475. doi: 10.1186/1472-6963-12-475.
25. Trivedi AN, Moloo H, Mor V. Increased ambulatory care copayments and hospitalizations among the elderly. N Engl J Med. 2010;362(4):320-328. doi: 10.1056/NEJMsa0904533.
26. Magan P, Otero A, Alberquilla A, Ribera JM. Geographic variations in avoidable hospitalizations in the elderly, in a health system with universal coverage. BMC Health Serv Res. 2008;8:42. doi: 10.1186/1472-6963-8-42.
27. Schiøtz M, Price M, Frølich A, et al. Something is amiss in Denmark: a comparison of preventable hospitalisations and readmissions for chronic medical conditions in the Danish healthcare system and Kaiser Permanente. BMC Health Serv Res. 2011;11:347. doi: 10.1186/1472-6963-11-347.
28. Moy E, Chang E, Barrett M; CDC. Potentially preventable hospitalizations — United States, 2001-2009. MMWR Suppl. 2013;62(3):139-143.
29. Purdy S, Griffin T, Salisbury C, Sharp D. Ambulatory care sensitive conditions: terminology and disease coding need to be more specific to aid policy makers and clinicians [erratum in Public Health. 2010;124(12):720. doi: 10.1016/j.puhe.2010.07.009]. Public Health. 2009;123(2):169-173. doi: 10.1016/j.puhe.2008.11.001.
30. Quality indicators. Agency for Healthcare Research and Quality website. qualityindicators.ahrq.gov. Accessed October 9, 2016.
31. Avoidable hospital admissions. In: OECD. Health at a Glance 2013: OECD Indicators. Paris, France: OECD Publishing; 2013:108-109. doi: 10.1787/health_glance-2013-43-en.
32. Bardsley M, Blunt I, Davies S, Dixon J. Is secondary preventive care improving? observational study of 10-year trends in emergency admissions for conditions amenable to ambulatory care. BMJ Open. 2013;3(1). pii: e002007. doi: 10.1136/bmjopen-2012-002007.
33. Sheridan A, Howell F, Bedford D. Hospitalisations and costs relating to ambulatory care sensitive conditions in Ireland. Ir J Med Sci. 2012;181(4):527-533. doi: 10.1007/s11845-012-0810-0.
34. Agabiti N, Pirani M, Schifano P, et al; Italian Study Group on Inequalities in Health Care. Income level and chronic ambulatory care sensitive conditions in adults: a multicity population-based study in Italy. BMC Public Health. 2009;9:457. doi: 10.1186/1471-2458-9-457.
35. Shmueli A. Switching sickness funds in Israel: adverse selection or risk selection? some insights from the analysis of the relative costs of switchers. Health Policy. 2011;102(2-3):247-254. doi: 10.1016/j.healthpol.2011.07.008.
36. Shmueli A, Bendelac J, Achdut L. Who switches sickness funds in Israel? Health Econ Policy Law. 2007;2(3):251-265. doi: 10.1017/S1744133107004100.
37. Shoval G, Balicer RD, Feldman B, et al. Adherence to antidepressant medications is associated with reduced premature mortality in patients with cancer: a nationwide cohort study. Depress Anxiety. 2019;36(10):921-929. doi: 10.1002/da.22938.
38. Krivoy A, Stubbs B, Balicer RD, et al. Low adherence to antidepressants is associated with increased mortality following stroke: a large nationally representative cohort study. Eur Neuropsychopharmacol. 2017;27(10):970-976. doi: 10.1016/j.euroneuro.2017.08.428.
39. Cunningham P, Sabik LM, Bonakdar Tehrani A. Trends in hospital inpatient admissions following early Medicaid expansion in California. Med Care Res Rev. 2017;74(6):705-722. doi: 10.1177/1077558716669433.
40. Barakat MT, Mithal A, Huang RJ, et al. Affordable Care Act and healthcare delivery: a comparison of California and Florida hospitals and emergency departments. PLoS One. 2017;12(8):e0182346. doi: 10.1371/journal.pone.0182346.
41. Bramesfeld A, Wensing M, Bartels P, et al. Mandatory national quality improvement systems using indicators: an initial assessment in Europe and Israel. Health Policy. 2016;120(11):1256-1269. doi: 10.1016/j.healthpol.2016.09.019.