Use of clinical decision support (CDS) in ambulatory clinics is increasing but remains modest. The CDS function with the greatest use is basic medication screening, which increased from 52% of clinics nationally in 2014 to 61% in 2016.
Objectives: Clinical decision support (CDS) is important for delivering high-quality care. We examined the use of 7 functions of CDS and changes over time in US health system—affiliated ambulatory clinics.
Study Design: We analyzed longitudinal data for 19,209 ambulatory clinics that participated in 3 years (2014-2016) of the Healthcare Information and Management Systems Society Analytics survey to assess use of 7 CDS functions and the characteristics of clinics associated with use of CDS.
Methods: We used descriptive statistics and linear probability models to assess the association of clinic characteristics (practice type, health system type, practice size) and 2 outcomes: CDS use during the study period and CDS use beginning in 2015 or 2016.
Results: Use rates increased between 2014 and 2016 for all 7 CDS functions, with increases ranging from 4 percentage points (genetic testing) to 13 percentage points (diagnostic result alerts). In 2016, the rate of use was highest for basic medication screening (61%), clinical guidelines and protocols (54%), and preventive medicine (57%). Lower use rates were observed for diagnostic result alerts (42%), remote device alerts (19%), incorporation of community-based electronic health record data into rules engines (26%), and genomics profiling in orders (9%). More than half of health systems, which included many smaller health systems, reported that none of their affiliated clinics used any CDS function. Affiliation with a multihospital health system and clinic size, but not clinic type (eg, primary care), were associated with a greater likelihood of use for most CDS functions.
Conclusions: Despite federal investment to promote health information technology adoption, substantial gaps remain in the use of CDS among ambulatory clinics, particularly among smaller health systems.
The American Journal of Accountable Care. 2019;7(4):4-10Following federal incentives from Meaningful Use (MU) legislation to encourage the adoption of health information technology (IT), physicians in the United States have adopted electronic health records (EHRs) in growing numbers.1-3 A 2015 survey found that 77.9% of office-based physicians reported using a certified EHR.4 EHRs include a range of health IT functionalities that can be adopted separately. One of the most well-studied EHR functionalities is clinical decision support (CDS). A systematic review of peer-reviewed studies published between 2007 and 2013 found 142 studies of the impact of CDS on patient care, with the majority of studies reporting positive results on quality, safety, or efficiency outcomes.5 MU incentives (now called Promoting Interoperability) defined CDS as a health IT functionality “that builds upon the foundation of an EHR to provide persons involved in care processes with general and person-specific information, intelligently filtered and organized, at appropriate times, to enhance health and health care.” To earn stage 2 MU incentives, for example, eligible providers were required to adopt drug-drug and drug-allergy interaction checks, as well as 5 CDS interventions related to their scope of practice or patient population.6 As the complexity of medical practice continues to increase, CDS tools will provide important support to providers to achieve high performance and succeed under payment arrangements that hold them accountable for cost and quality.
CDS has many functions, such as alerts for potential adverse drug events, clinical guidelines for use by clinicians at the point of care, and notifications to flag abnormal diagnostic results.7,8 Although hospital use of CDS has been extensively studied,9-11 less is known about use of CDS in ambulatory settings. To understand use of CDS in ambulatory care, we examined the use of 7 CDS functions and change in use between 2014 and 2016 among health system—affiliated ambulatory clinics, which represent a larger and growing proportion of clinics in the United States.
We used longitudinal data for 19,209 ambulatory clinics that participated in 3 years (2014, 2015, and 2016) of the Healthcare Information and Management Systems Society (HIMSS) Analytics survey from the HIMSS Analytics LOGIC Market Intelligence Platform.12 The survey covered health system—affiliated clinics (approximately 42,000) in 50 states, the District of Columbia, and Puerto Rico, providing information on more than 75% of US health system–associated ambulatory care clinics. HIMSS defined clinics for this survey as ambulatory clinics providing preventive, diagnostic, therapeutic, surgical, and/or rehabilitative outpatient care for which the duration of treatment was less than 24 hours. HIMSS defined a health system as an organization composed of at least 1 hospital and its associated nonacute facilities. Associated was defined as a governance relationship (ie, owned, leased, or managed by a health system).
We included only clinics that completed the survey every year between 2014 and 2016. We excluded clinics (n = 980; 4.85%) that reported use of a CDS functionality in one year followed by nonuse in the subsequent year; it is unlikely that clinics would discontinue using a CDS function, so we assumed that the response was likely due to an error. The panel included 19,209 clinics affiliated with 1704 health systems as of 2016.
Clinic and Health System Characteristics
We used the HIMSS survey descriptive data to classify ambulatory clinics according to size (based on number of physicians, dichotomized into ≤3 and ≥4), clinic type (primary vs specialty), and health system type (single hospital or multihospital).
We examined use of EHR and 7 functions of CDS that were defined in the HIMSS survey: (1) basic medication screening (drug-drug, drug-allergy), (2) clinical guidelines or protocols, (3) preventive medicine (eg, immunizations, follow-up testing), (4) alerts based on external EHR data (data from the community-based EHR is incorporated into the EHR’s rules engine and triggers alerts), (5) genomics profiling used in orders (incorporated into the EHR and could result in a suggested order or order change), (6) diagnostics result alerts (trigger relevant clinical alerts and clinical guidance/recommended care), and (7) remote device alerts (alert clinician when clinically significant changes in data are detected).
Responses were coded as a binary yes/no to indicate use during a given survey year. We calculated a composite binary variable that indicated whether the clinic used 1 or more of the CDS functions. The HIMSS survey did not capture data on extent of use of health IT functionalities.
Descriptive statistics are presented here for each CDS function between 2014 and 2016 by clinics and by affiliated health systems. To understand changes in CDS use by health systems for each CDS function, the percentage of health systems was calculated for which none of the system’s clinics had reported using it, for which all the system’s clinics reported using it, and for which some but not all the system’s clinics reported using it during the study period. Also calculated was the number of health systems that reported not using any CDS in any affiliated clinic.
To examine the association between clinic characteristics—clinic type (primary care vs specialty care), health system type (single- vs multihospital), and clinic size (≤3 vs ≥4 providers)—and use of each CDS function at any point during the 3-year analysis period, we used a linear probability model, using 2014 as the reference year and dummy variables for 2015 and 2016. In addition, to determine whether the magnitude of those same associations changed over time, for each CDS function, a linear probability model was used on the pool of clinics that did not report using the CDS function in 2014, with the dependent variable defined as reported CDS use in 2015 or 2016. Standard errors were clustered at the health system level.
Table 1 displays the characteristics of clinics and health systems in the analytic sample. Health systems had a mean of approximately 12 clinics per health system and 7 physicians per clinic in 2016. The number of reporting health systems declined by 113 (7%) between 2014 and 2016, likely mainly due to mergers and acquisitions. Nearly all ambulatory clinics (96%) had adopted EHRs by 2016. The majority of clinics provided primary care (64% in 2016) and were associated with a multihospital system (72% in 2016).
For every function of CDS, ambulatory clinic use increased between 2014 and 2016, ranging from an increase of 4 percentage points in genetics profiling used in orders to an increase of 13 percentage points for diagnostic result alerts. The portion of ambulatory clinics using any form of CDS increased from 53% in 2014 to 62% in 2016. Use of CDS was greatest for basic medication screening (61% in 2016), clinical guidelines and protocols (54% in 2016), and preventive medicine (57% in 2016), and CDS use was least for genomics profiling used in orders (9% in 2016) (Figure 1).
When assessed at the health system level, the majority of health systems reported that none of their ambulatory clinics were using CDS. This percentage decreased from 61% of health systems reporting no CDS use in 2014 to 55% in 2016. Of the 7 CDS functions, basic medication screening had the largest share of health systems reporting use by all their clinics (32% in 2016) and reporting use by some or all their clinics (44% in 2016) (Figure 2).
For every CDS function, clinics in multihospital systems (as opposed to single-hospital systems) had an increased likelihood of CDS use, ranging from genomics profiles having a 5.1% increased likelihood to be used in orders in multihospital systems to diagnostic result alerts having a 21.7% increased likelihood to be used in multihospital systems (Table 2). Clinic size (clinics with ≥4 physicians) was associated with a small to moderate increased likelihood of use for all CDS functions, except remote device alerts, ranging from 2.1% (genomics profiles used in orders) to 10.2% (clinical guidelines or protocols). Type of clinic (primary vs specialty care) was not associated with use of CDS except in use of preventive medicine.
We found similar associations among clinics that were not using CDS in 2014 compared with subsequent use in 2015 or 2016, with higher use among clinics affiliated with multihospital systems and clinics of large size. However, almost all coefficients were lower compared with the complete panel results (Table 3). These findings indicate that the magnitudes of associations between CDS use and these 2 factors (multihospital system affiliation and size of clinic) have decreased over time.
This study of a national panel of health system—affiliated clinics provides a national view of the state of CDS use in the ambulatory care setting during a recent 3-year period. The principal findings are that the use of CDS tools is increasing, particularly in larger clinics and those affiliated with multihospital health systems, but there is wide variation in use by CDS function and considerable room to increase the penetration of all CDS functions. Of health system–affiliated ambulatory clinics, 62% were using 1 or more of the 7 examined CDS functions. The portion of health systems with at least some clinics using 1 or more CDS function was 45%. Considering the public investments in promoting CDS, these levels of use are likely substantially lower than expected.
Multiple factors may explain the variation by CDS function. MU likely promoted some functions of CDS (eg, basic medication screening, preventive medicine) more than others. Feasibility of implementation, availability of best practices, disruption in workflow, and perceived clinical and/or financial benefit by clinicians likely also affect CDS use. The CDS functions with the strongest evidence base (eg, medication screening, clinical guidelines, preventive medicine) seem to have greater use, and there seems to be less use of CDS functions that are not as well studied (eg, genomics profiles used in orders, incorporating community EHR data, remote device alerts).5,13
The substantial association of CDS use with an ambulatory practice affiliation with a multihospital system and clinic size likely reflects the greater number of staff and more resources available for rolling out clinical IT systems in larger health systems and clinics.14,15 The weakening over time of the associations of CDS with affiliation with a multihospital system and clinic size may be due in part to MU incentives that promoted CDS more evenly compared with prior years or to a relative increase among single-hospital systems and small clinics in their perceived need to adopt health IT. Even though these associations have weakened over time, they are significant and suggest that single-hospital systems and small clinics may need additional assistance in implementing and using CDS. Further study is needed to better understand the barriers facing single-hospital systems and small clinics and develop solutions to address
Most nationwide studies of health IT have focused on hospitals.11,16-18 To address policy goals of higher quality and lower costs, more attention should be given to ambulatory clinics to ensure that they have the necessary tools to provide optimal care and prevent ambulatory care—sensitive hospital admissions. Direct incentives may not be effective to achieve universal adoption: Results of one analysis suggest that existing federal incentives have had minimal to no effect on EHR adoption in ambulatory settings.19 However, there are other policy options, such as increasing the proportion of value-based payment arrangements through payer contracting to provide incentives for the cost and quality outcomes facilitated by CDS; supporting the development of more usable and implementable CDS capabilities through research or EHR certification requirements; generating additional evidence of the effect of less-used CDS functions on quality, safety, health outcomes, and costs; and promoting and disseminating best practices for implementation and ongoing use.
Vendors and health systems should also play important roles in increasing CDS use. Vendors can prioritize developing CDS functionalities and making them more usable and easier to implement. Health systems can influence their affiliated clinics to adopt CDS tools because they often control the IT systems centrally and can learn and apply best practices for efficiently and effectively implementing these tools in their many clinics.
This study had a number of limitations. First, HIMSS data (and therefore the panel of ambulatory clinics) excluded clinics unaffiliated with a health system, and the study is therefore not a complete assessment of the state of CDS use in all ambulatory settings in the United States. Clinics that are not affiliated with health systems likely have different (and probably lower) CDS use patterns because of more limited resources; therefore, our reports are likely an overestimate of the national rate. Second, the data were from a self-reported survey and have not been validated. However, our previous work found high concordance with HIMSS and another survey on some variables.20 Third, CDS use variables were binary and did not include extent or effectiveness of use. Fourth, the analyses assessed associations, not causality with regard to clinic characteristics that may affect CDS use. Fifth, clinics without responses were excluded, so there may be some nonresponse bias.
Despite substantial federal incentives related to health IT adoption, major gaps remain in the use of many CDS functions among ambulatory clinics, especially among single-hospital systems and smaller clinics. Greater incentives to use CDS to improve care or lower costs, improving availability and usability of CDS capabilities within EHRs, and spreading best practices may help accelerate increased use of CDS in health systems.
The authors thank John Daly of RAND for assistance with the data.Author Affiliations: RAND Corporation, Boston, MA (RSR, SHF), and Santa Monica, CA (PS, MSR, CLD); The Pennsylvania State University (YS, AA-R, DPS), University Park, PA.
Source of Funding: This work was supported through the RAND Center of Excellence on Health System Performance, which is funded through a cooperative agreement (1U19HS024067-01) between the RAND Corporation and the Agency for Healthcare Research and Quality (AHRQ). The content and opinions expressed in this publication are solely the responsibility of the authors and do not reflect the official position of AHRQ or HHS.
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 (RSR, SHF, YS, PS, DPS); acquisition of data (YS, DPS, CLD); analysis and interpretation of data (RSR, SHF, YS, PS, AA-R); drafting of the manuscript (RSR); critical revision of the manuscript for important intellectual content (RSR, SHF, YS, PS, AA-R, MSR, DPS, CLD); statistical analysis (RSR, SHF); obtaining funding (RSR, MSR, DPS); administrative, technical, or logistic support (MSR); and supervision (MSR).
Send Correspondence to: Robert S. Rudin, PhD, RAND Corporation, 20 Park Plaza, Ste 920, Boston, MA 02116. Email: firstname.lastname@example.org.REFERENCES
1. American Recovery and Reinvestment Act of 2009, Pub L No. 111-5, 123 Stat 115.
2. Jamoom EW, Yang N, Hing E. Adoption of certified electronic health record systems and electronic information sharing in physician offices: United States, 2013 and 2014. NCHS Data Brief. 2016;(236):1-8.
3. Blumenthal D. Launching HITECH. N Engl J Med. 2010;362(5):382-385. doi: 10.1056/NEJMp0912825.
4. Office-based physician electronic health record adoption. Office of the National Coordinator for Health Information Technology website. dashboard.healthit.gov/quickstats/pages/physician-ehr-adoption-trends.php. Accessed December 3, 2019.
5. Jones SS, Rudin RS, Perry T, Shekelle PG. Health information technology: an updated systematic review with a focus on meaningful use. Ann Intern Med. 2014;160(1):48-54. doi: 10.7326/M13-1531.
6. Medicare & Medicaid EHR Incentive Program: Meaningful Use stage 1 requirements overview. cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/downloads/MU_Stage1_ReqOverview.pdf. Published 2010. Accessed December 3, 2019.
7. Wright A, Goldberg H, Hongsermeier T, Middleton B. A description and functional taxonomy of rule-based decision support content at a large integrated delivery network. J Am Med Inform Assoc. 2007;14(4):489-496. doi: 10.1197/jamia.M2364.
8. Berner ES. Clinical decision support systems: state of the art. Agency for Healthcare Research and Quality website. healthit.ahrq.gov/sites/default/files/docs/page/09-0069-EF_1.pdf. Published June 2009. Accessed December 3, 2019.
9. Jha AK, DesRoches CM, Campbell EG, et al. Use of electronic health records in U.S. hospitals. N Engl J Med. 2009;360(16):1628-1638. doi: 10.1056/NEJMsa0900592.
10. Jha AK, Burke MF, DesRoches C, et al. Progress toward meaningful use: hospitals’ adoption of electronic health records. Am J Manag Care. 2011;17(12 spec no.):SP117-SP124.
11. Adler-Milstein J, Holmgren AJ, Kralovec P, Worzala C, Searcy T, Patel V. Electronic health record adoption in US hospitals: the emergence of a digital “advanced use” divide. J Am Med Inform Assoc. 2017;24(6):1142-1148. doi: 10.1093/jamia/ocx080.
12. Secondary HIMSS Analytics Logic. HIMSS Analytics website. himssanalytics.org. Accessed December 3, 2019.
13. Bright TJ, Wong A, Dhurjati R, et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med. 2012;157(1):29-43. doi: 10.7326/0003-4819-157-1-201207030-00450.
14. Casalino LP, Chen MA, Staub CT, et al. Large independent primary care medical groups [erratum in Ann Fam Med. 2016;14(2):99. doi: 10.1370/afm.1914]. Ann Fam Med. 2016;14(1):16-25. doi: 10.1370/afm.1890.
15. Ash JS, Sittig DF, Wright A, et al. Clinical decision support in small community practice settings: a case study. J Am Med Inform Assoc. 2011;18(6):879-882. doi: 10.1136/amiajnl-2010-000013.
16. Lin SC, Jha AK, Adler-Milstein J. Electronic health records associated with lower hospital mortality after systems have time to mature. Health Aff (Millwood). 2018;37(7):1128-1135. doi: 10.1377/hlthaff.2017.1658.
17. Holmgren AJ, Adler-Milstein J, McCullough J. Are all certified EHRs created equal? assessing the relationship between EHR vendor and hospital meaningful use performance. J Am Med Inform Assoc. 2018;25(6):654-660. doi: 10.1093/jamia/ocx135.
18. Adler-Milstein J, Jha AK. HITECH Act drove large gains in hospital electronic health record adoption. Health Aff (Millwood). 2017;36(8):1416-1422. doi: 10.1377/hlthaff.2016.1651.
19. Mennemeyer ST, Menachemi N, Rahurkar S, Ford EQ. Impact of the HITECH Act on physicians’ adoption of electronic health records. J Am Med Inform Assoc. 2016;23(2):375-379. doi: 10.1093/jamia/ocv103.
20. Fischer SH, Shi Y, Shekelle P, Amill-Rosario A, Shaw B, Rudin RS. Asking provider organizations about their health IT adoption and use: a mixed-methods analysis of an ambulatory survey. Paper presented at: AMIA 2018 Annual Symposium; November 3-7, 2018; San Francisco, CA.