Robert S. Rudin, PhD

 The adoption and use of health information technology (IT) by health systems in ambulatory care can be an important driver of care quality. We examine recent trends in health IT adoption by health system—affiliated ambulatory clinics in the context of the federal government’s Meaningful Use and Promoting Interoperability programs.

 We analyzed a national sample of 17,861 ambulatory clinics affiliated with 1711 health systems, using longitudinal data (2014-2016) from the HIMSS Analytics annual surveys.

 We used descriptive analyses and linear probability models to examine the adoption of electronic health records (EHRs), as well as 16 specific functionalities, at the clinic level and the system level. We compared the differential trends of adoption by various characteristics of health systems.

 We find that the adoption of an EHR certified by the Office of the National Coordinator for Health IT (ONC) increased from 73% to 91%. However, in 2016, only 38% of clinics reported having all 16 health IT functionalities included in this study. Small health systems lag behind large systems in ambulatory health IT adoption. Patient-facing functionalities were less likely to be adopted than those oriented toward physicians. Health information exchange capabilities are still low among ambulatory clinics, pointing to the importance of the ONC’s recent Promoting Interoperability initiative.

 The relatively low uptake of health IT functionalities important to care improvement suggests substantial opportunities for further improving adoption of ambulatory health IT even among the current EHR users.

The adoption of an electronic health record (EHR) certified by the Office of the National Coordinator for Health Information Technology (ONC) among ambulatory clinics increased from 73% to 91% between 2014 and 2016.

However, in 2016, only 38% of clinics reported having all 16 health information technology (IT) functionalities included in this study.

Small health systems significantly lag behind large systems in adoption.

Patient-facing functionalities were less likely to be adopted than functionalities oriented toward physicians.

Health information exchange capabilities are still low, pointing to the importance of the ONC’s continuing focus on interoperability.

Our findings suggest substantial opportunities for further improving adoption of ambulatory health IT even among the current EHR users.

Advancing the adoption and use of health information technology (IT) has been an important focus of the federal government’s efforts to improve quality of care during the past decade. Two major pieces of legislation promoted health IT adoption: the Health Information Technology for Economic and Clinical Health (HITECH) Act,

 enacted as part of the American Recovery and Reinvestment Act of 2009, and the Patient Protection and Affordable Care Act

 in 2010. The multistage incentive programs under HITECH, commonly known as Meaningful Use, provided more than $30 billion to about a half-million healthcare providers and were essential to the initial adoption of and sustained use of health IT.

 Nevertheless, the penetration of health IT has proceeded at different paces for hospitals and providers in ambulatory settings. For example, by 2013, more than 59% of hospitals had adopted at least a basic electronic health record (EHR), whereas the proportion of clinics using an EHR was estimated to be about 48%.

 As the adoption and use of health IT grow, evidence supporting the positive impacts of health IT has also been accumulating, although not all health IT implementations have produced effective results.

 Furthermore, there is evidence of unintended consequences, such as physician burnout.

 Although most empirical studies linking IT to quality of care using large national samples were conducted in hospital settings,

 a recent study found that higher rates of ambulatory EHR adoption at the county level were associated with reductions in hospitalization between 2003 and 2012.

Along with policies to promote health IT adoption, significant and rapid changes have been occurring in the organizational landscape of healthcare, with health systems assuming an increasingly important role in delivering care.

 A typical health system, often formed through consolidation with the expectation of better clinical integration and higher-value care, can include multiple hospitals, numerous ambulatory clinics, and even an insurance plan. Some studies have found positive associations between care quality and increased integration of care delivery (ie, various healthcare services being delivered with coordination among different providers) within health systems,

 although whether the formation of health systems actually leads to quality improvement is still an open question.

However, little is known about how the adoption and use of health IT varies across health systems. Different health systems may vary in their capabilities of pooling resources to overcome cost, implementation, or usage barriers to health IT, and they may also employ different strategies to adopt and implement IT.

 In this study, we examine the recent trends in the adoption of health IT by health system—affiliated ambulatory clinics and make 2 important contributions to this topic. First, despite the large existing literature on health IT adoption in hospitals, relatively few studies have examined adoption in the ambulatory setting at the national level. Most previous research on ambulatory health IT either examined only basic EHR adoption without measuring adoption of specific functionalities

 or studied selected functionalities using cross-sectional data.

 One recent study examined use of specific functionalities among a cohort of 566 small primary care clinics.

 Our study extends this work by analyzing the adoption of 16 health IT functionalities with a longitudinal sample of ambulatory care clinics between 2014 and 2016. Second, our study focuses on clinics that are affiliated with (ie, owned, leased, or managed by) health systems to provide an understanding of ambulatory health IT adoption from a delivery system perspective. Specifically, we identify patterns of adoptions at both the ambulatory clinic level and the health system level and examine how adoption varies by clinic and health system characteristics. Our analysis provides an up-to-date understanding of the advancement of adoption of health IT in the ambulatory setting and the extent to which a core set of functionalities are being adopted to support patient care.

We used 2014-2016 data from the HIMSS annual surveys of ambulatory clinics, provided by HIMSS Analytics LOGIC Market Intelligence Platform. The HIMSS surveys collected detailed information on the adoption and use of health IT for more than 75% of ambulatory care clinics affiliated with health systems.

 HIMSS defines a health system as an organization that owns at least 1 hospital.

 Although data from hospital surveys conducted by HIMSS have been widely used in previous health IT research,

 the HIMSS ambulatory survey data have been used in only a relatively small number of studies and government reports.

 Despite its limited use to date, the HIMSS ambulatory health IT database is an important source of information because of the longitudinal survey design and the large sampling frame.

For our analysis, we included only ambulatory clinics that responded to the survey in all 3 years. They accounted for 92% of all clinics surveyed in 2014. Because we intended our analysis to be focused on ambulatory clinics in which providers directly interact with patients in delivering primary and/or specialty care, as these care sites were likely to benefit from the commonly adopted health IT functionalities,

 we excluded certain types of facilities (eg, urgent care, podiatry, and wellness centers). Finally, we excluded 6% of the clinics because they did not report the adoption statuses of all the functionalities examined in this study. Our final study sample contained 17,861 clinics and 1711 health systems (

The National Academy of Medicine identified 8 core areas of health IT that are instrumental to the improvement of quality, safety, and efficiency in care delivery: health information and data, results management, order entry and management, decision support, electronic communication and connectivity, patient support, administrative processes, and reporting and population health.

 In our study, we measured the adoption of an ambulatory EHR system certified by the Office of the National Coordinator for Health IT (ONC), as well as the adoption of 16 specific health IT functionalities, which represent 7 of the 8 core areas defined by the National Academy of Medicine (

) and were included by HIMSS in its annual survey of ambulatory care clinics. These specific functionalities are also at the center of the 3 stages of Meaningful Use programs (eg, clinical decision support) as well as their recent extension, Promoting Interoperability (eg, capabilities of exchanging health information).

 Functionalities in the area of reporting and population health were not covered in the HIMSS ambulatory surveys during our study period.

Characteristics of Ambulatory Care Clinics and Health Systems

We included ambulatory care clinic and health system characteristics in our analyses. For ambulatory clinics, we examined type (primary care vs specialty), location (urban vs rural), number of physicians, and ownership (owned vs leased or managed) by the parent health system. At the health system level, we included characteristics that capture both the inpatient and the outpatient aspects of care provided by each system: being a single-hospital system, number of licensed hospital beds, number of ambulatory clinics within each system, and profit status. We included number of licensed beds in our analysis because hospital(s) may influence a health system’s overall IT strategy and thus have implications on how ambulatory care clinics within the system adopt health IT. The size of a health system can potentially be quantified by various measures, such as the total number of physicians, the total annual revenue, and the total number of outpatient visits. We used the number of ambulatory clinics within a system as a proxy to measure system size for 2 reasons. First, the number of clinics is highly correlated with other measures of size available in our data. Second, the clinics are often the units for health IT implementation, and hence the number of clinics might be directly impactful in terms of cost and efficiency in adopting IT.

We first descriptively examined the ambulatory clinics’ trends of adopting EHR and specific IT functionalities and tested whether the changes in adoption rates were statistically significant. To analyze the adoption trends at the health system level, we calculated adoption rates of EHR and health IT functionalities by aggregating the adoption statuses of all ambulatory clinics within a system, weighted by the number of physicians in each clinic. We then stratified health systems into 5 groups based on the number of ambulatory clinics within each system (1, 2, 3-10, 11-30, or >30) and computed the average adoption rates by stratum.

Linear probability (regression) models were used to examine whether ambulatory health IT adoption was associated with specific clinic characteristics (location, clinic type, ownership status, number of physicians in the clinic) and health system characteristics (being a single-hospital system, profit status, number of ambulatory clinics in the system, number of total licensed hospital beds in the system). The outcomes were the adoption statuses of individual IT functionalities as well as an aggregate indicator for whether a clinic adopted all 16 functionalities. Dummy variables for each period were included in all models, and standard errors were clustered at the health system level to adjust for potential intrasystem correlations.

Table 1 summarizes the characteristics of the study sample by year. The majority of the ambulatory care clinics in our sample were primary care providers (64%), were located in urban areas (82%), were owned by health systems (98%), and had 5 or fewer physicians (74%). The health systems included in the study mostly were single-hospital systems (74%) and nonprofit (95%), had 10 or fewer ambulatory care clinics (66%), and had 150 or fewer licensed hospital beds (52%). Although all sample characteristics were stable during the study period, the total number of health systems decreased from 1711 to 1601, likely because of consolidations.

The trends in the adoption of health IT functionalities are presented in Table 2. From 2014 to 2016, the adoption of all 16 functionalities increased significantly at the clinic level, by similar magnitudes. In 2016, 91% of the clinics in our sample had a certified EHR, an increase of 18 percentage points from 2014. Among the examined functionalities, clinical data repository had consistently the highest average adoption rate (61% in 2014 and 68% in 2016), whereas patient health record was the least adopted (38% in 2014 and 48% in 2016). In 2014, only 28% of clinics had adopted all 16 functionalities examined in this study, increasing to 38% in 2016. Across individual functionalities, the proportion of nonadopters was still large in 2016, between 32% and 52%. Patient-facing functionalities (eg, patient health record, patient portal) were less adopted than physician-oriented functionalities (eg, clinician charting, computerized provider order entry [CPOE]), and the differences persisted over time.

At the health system level, the adoption patterns varied greatly depending on the size of the system and the specific functionality, as shown in the 

]). Although there was an increasing trend in the adoption of all functionalities across all subgroups, the large systems (with >30 ambulatory clinics) showed not only a much higher level of adoption but also significantly faster increases over time compared with small systems (with ≤10 clinics). In 2016, the average adoption of CPOE among the large systems (69%) was more than double the rate among the small systems (28%) and also had a greater increase from 2014 (9 vs 5 percentage points). Similar patterns were seen in the adoption of other functionalities such as clinical decision support and e-prescribing. Only a modest proportion of small systems (13%-22% in 2016) had the capability to exchange clinical health information between clinics and hospitals, whereas 57% of the large systems were able to do so. Also, most patient-facing functionalities were present in only a fraction of small systems (8%-18% in 2016), and their adoption rates in large systems were around 50%, although these rates were still significantly lower than those of other functionalities. Finally, only about 5% of the small systems had adopted all 16 functionalities by 2016, whereas the proportion was much higher among the large systems (38%).

, several clinic and system characteristics were significant correlates of health IT adoption (

 ≤.05). At the clinic level, being a primary care clinic and having a larger number of physicians were associated with an increased likelihood of adoption. At the system level, being nonprofit and having more ambulatory clinics in the system were associated with increased adoption levels of ambulatory health IT. In particular, nonprofit systems were, on average, between 20 and 35 percentage points more likely to adopt various functionalities. Systems with more than 30 ambulatory clinics were between 35 and 45 percentage points more likely to adopt compared with systems with only 1 clinic. Across functionalities, clinics with more than 20 physicians were associated with an increase of 20 percentage points in probability of adoption relative to solo physician clinics. Multispecialty care clinics were, on average, about 5 percentage points less likely to adopt the functionalities than primary care clinics. Being in urban areas, being a single-hospital system, and having more licensed hospital beds were not significantly associated with adoption. The significant and positive coefficients of the time indicators were consistent with descriptive trends observed earlier.

The principal findings from this study show that most ambulatory clinics had an ONC-certified EHR in 2016. However, specific functionalities designed to improve care quality vary in their rate of adoption, and considerable gaps remain in the full adoption of any specific functionality as well as in the adoption of all functionalities. Although the proportion of ambulatory clinics having all 16 functionalities increased by 10 percentage points over the 3-year period, almost two-thirds of clinics still did not have the complete set. This is consistent with results of other recent studies

 and may partly explain the mixed findings on the impact of the EHR adoption found in previous research.

 Our study findings suggest substantial opportunities for further improving ambulatory health IT among current EHR users.

In particular, patient-facing functionalities such as patient portals and patient health records were about 10 to 20 percentage points less likely to be adopted than physician-based functions such as clinical data repositories and clinician charting. Patient health records and patient portals are considered core elements of health IT that can potentially improve patient-centered care through better engagement and patient experience.

 Because ambulatory providers are often the principal source of care for most patients, the use of these functionalities in such settings is especially important, and hence their lower adoption rate is of concern. Another area of concern is health information exchange (HIE), which is viewed as a crucial tool for improving coordination of care and efficiency.

 Consistent with findings from other studies,

 our results show that by 2016, close to half of the clinics were still not able to exchange clinical information with hospitals or other clinics.

Equally important, we find substantial disparities in the trends of adoption across health systems of different sizes. Compared with large systems, smaller health systems had a 2-, 3-, or even 4-fold lower rate of adopting important functionalities such as CPOE, clinical decision support, and HIE. Moreover, the gap in adoption rates is increasing, with large systems adopting new functionalities faster than small systems. For example, whereas the majority of the large systems had HIE capabilities in 2016, about 80% of systems with 10 or fewer clinics were still without HIE. As health IT is de facto becoming an indispensable component of care delivery, the gaps in adoption may translate into differences in quality performance.

 The observed disparity in adoption may be due to economy of scale as well as differential resource barriers

 (eg, up-front cost, staff training, prior IT experiences) faced by systems of different sizes.

The results from the regression analysis indicated that a number of clinic and system characteristics were important predictors of ambulatory health IT adoption, especially the number of clinics within a system and the number of physicians within a clinic, consistent with the descriptive patterns. We also found that, possibly because of their inclination to reduce short-term investment cost, for-profit health systems were much less likely (17-20 percentage points less) to adopt ambulatory IT.

Our study has several limitations. First, we used self-reported survey data, and previous research has demonstrated that health IT surveys in general may be subject to measurement errors.

 However, in terms of major functionalities, results of a recent study showed reasonable agreement between HIMSS ambulatory data and other sources of information.

 Second, the information in the data did not fully capture all potential attributes of health systems that may be related to adoption of health IT. For example, our analysis did not include organizational factors such as governance structures and managerial processes, which can be critical to the implementation of technology.

 Analyzing such factors may provide important insights for better understanding the barriers to the adoption and use of health IT. Third, the responses in the HIMSS data on health IT functionalities are mostly binary (yes/no) and do not contain more granular information about capability and use. Nevertheless, the HIMSS survey does include a broad set of functionalities with some specificity (eg, clinical decision support used for basic medication screening). Finally, because our data did not include independent ambulatory clinics or physician groups unaffiliated with health systems, our findings are not representative of the entire universe of ambulatory providers. Based on the much lower adoption rates among small health systems, we expect independent providers outside health systems to fare even worse.

Small health systems lagged in their adoption of ambulatory health IT. Patient-facing functionalities were less adopted than physician-oriented functionalities. Such gaps in adoption may lead to differences in performances across health systems. The fact that health IT capabilities are low among ambulatory clinics supports the ONC’s continuing focus on interoperability. As the healthcare market continues to consolidate, the number of small systems may decrease, but they probably will still play important roles in care delivery, highlighting the ongoing need to develop strategies to support health IT adoption by smaller providers of care. Additionally, efforts to promote the adoption of HIE and patient-facing functions such as patient portals are needed.

The authors would like to thank Donald Miller and Tracy Johnson at The Pennsylvania State University for their research assistance.

 The Pennsylvania State University (YS, AA-R, DS), University Park, PA; RAND Corporation, Boston, MA (RSR, SHF), and Santa Monica, CA (CLD); Southern California Evidence-based Practice Center, RAND Corporation (PS), Santa Monica, CA.

 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.

 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.

 Concept and design (YS, RSR, SHF, PS); acquisition of data (YS, DS, CLD); analysis and interpretation of data (YS, AA-R, RSR, SHF, CLD); drafting of the manuscript (YS, AA-R, CLD); critical revision of the manuscript for important intellectual content (YS, AA-R, RSR, SHF, PS, DS, CLD); statistical analysis (YS); obtaining funding (RSR, DS, CLD); administrative, technical, or logistic support (CLD); and supervision (YS, PS, CLD).

 Yunfeng Shi, PhD, The Pennsylvania State University, 504E Ford Bldg, University Park, PA 16802. Email: yus16@psu.edu.

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Articles

Health systems are important in driving electronic health record adoption in ambulatory clinics, although the uptake of key functionalities varies across systems.

Health Information Technology for Ambulatory Care in Health Systems

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.

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.

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.

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.

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):4-10

Following 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.

 A 2015 survey found that 77.9% of office-based physicians reported using a certified EHR.

 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.

 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.

 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.

 Although hospital use of CDS has been extensively studied,

 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.

 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. 

 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.

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)&mdash;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.

 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) (

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) (

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 (

). 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 (

). 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).

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.

 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.

 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.

 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.

 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.

RAND Corporation, Boston, MA (RSR, SHF), and Santa Monica, CA (PS, MSR, CLD); The Pennsylvania State University (YS, AA-R, DPS), University Park, PA.

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.

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).

Robert S. Rudin, PhD, RAND Corporation, 20 Park Plaza, Ste 920, Boston, MA 02116. Email: rrudin@rand.org.

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. 

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.

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. 

. 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.

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.

Trends in the Use of Clinical Decision Support by Health System–Affiliated Ambulatory Clinics in the United States, 2014-2016

 Complex patients incur a majority of healthcare costs in the United States, in part, because their care is poorly coordinated. We sought to determine the leading edge of health information technology (IT) tools for care coordination of complex patients.

 Qualitative discussions and technical expert panels.

 We conducted 35 discussions with clinical leaders, technology and startup executives, government officials, academic researchers, and 2 technical expert panels.

 Although health IT has the potential to improve care coordination, the types of IT tools available to clinicians and patients are currently limited. We found substantial barriers to developing technical capabilities for improving care coordination, including lack of knowledge of users’ needs; lack of standardized roles, responsibilities, and protocols; required changes in providers’ work activities to achieve coordination; and an unclear value proposition.

 We found several innovative tools, but existing efforts suffer from important limitations, including minimal engagement by physicians, lack of standardized definitions of what the tools do, and challenges integrating with clinical workflows. For health IT to facilitate coordination of care for complex patients, user needs and workflows must be better understood and used to guide the development of technology and policy.

	Coordination of complex patients’ care has great potential for producing savings. Although health information technology (IT) has the potential to help achieve this goal, the types of IT tools available to coordinate care for complex patients are currently limited. Specific study findings include: 

		Substantial barriers to developing technical capabilities exist and include a lack of knowledge of users’ needs and a lack of standardized roles and responsibilities. 

		Current tools engage physicians minimally and have challenges integrating with clinical workflows. 

		For health IT to facilitate coordination of care for complex patients, user needs and workflows must be better understood and used to guide the development of technology and policy.

Arguably, the greatest opportunity to save on healthcare spending in the United States is through more effective care of complex patients—defined as those with multiple comorbidities, high risk for poor outcomes, and high cost.

 Given that such patients typically visit multiple providers, improved care coordination is one important means of improving the effectiveness of their care.

 Despite this need, the care of complex patients is generally poorly coordinated.

 Few interventions have been shown to be effective in addressing the care coordination needs of these patients, and thus, reducing costs.

Information technology (IT) has been widely adopted outside of healthcare to support coordination among individuals. For example, project management technology coordinates workplace tasks, and configuration management programs help coordinate the work of software developers. However, recent studies have found a relative lack of such technical capabilities in healthcare, despite the existence of many of the components required for such capabilities (eg, cloud computing platforms, widespread use of smartphones, internet connectivity).

Although spending on health IT has increased, little effort has been devoted to developing care coordination technologies for complex patients. In addition, little is known about the latest generation of technologies.

 We sought to understand the key barriers to progress and the technical capabilities (ie, functionalities) at the leading edge of innovation. Building on our findings, we provide suggestions for how to better harness health IT to coordinate care for complex patients.

We conducted a series of discussions with experts from a variety of stakeholder perspectives, identifying them through a targeted literature review, recommendations from other experts, and snowball sampling. We selected 35 experts to represent a wide range of stakeholder perspectives from the private sector, academic institutions, and federal agencies. We conducted 2 phases of in-depth discussions, ranging from 45 to 90 minutes, using a guide that identified key topics to be covered, including IT capabilities for care coordination. After the first phase, we convened a technical expert panel to discuss the preliminary results and refine a second phase of expert discussions. The second phase focused on IT capabilities designed to facilitate care coordination through communication among providers caring for the same patient.

From the expert discussions, we identified key themes, including barriers to progress and types of technical capabilities that overcome these barriers. We then identified potential opportunities for further advancing care coordination IT tools to better meet the needs of complex patients and their care teams.

This study was approved by the RAND Institutional Review Board and all discussion subjects gave informed consent.

Experts identified 4 key barriers to the development of new IT tools for care coordination.

 The types of coordination needs, and activities that address them, are poorly understood, which makes it difficult for IT designers to create solutions. Even members of our technical expert panel disagreed about what activities should be called coordination. Some experts drew a sharp distinction between activities related to coordination versus treatment; in their view, physicians were not a necessary part of coordination. Others believed that such a distinction was unhelpful because many coordination activities are also related to treatment and involve physicians (eg, developing care plans, communicating with other physicians). From their viewpoint, it would be a mistake to not fully engage physicians as users of a functionality that facilitates care team communication.

 The users’ roles, responsibilities, and protocols are not standardized, except in special circumstances, like postsurgical care within an institution.

 The lack of standardized shared definitions for care team membership and associated workflows makes it difficult to operationally establish current care team roles. Even creating names for roles, such as “care coordinator,” is challenging, because they lack universal meaning and the roles change over the course of a patient’s care. Experts described challenges defining user roles even for common tasks: the management of a patient’s problem list—a list of the most important health problems facing a patient, including current and active diagnoses as well as relevant past diagnoses&mdash;does not have agreed-upon protocols defining which member of a care team is responsible for updates or corrections. Providers tend to be reluctant to modify problems added by other providers.

 Additionally, product design is complicated if users must be able to handle significant diversity, which can include variation among providers’ practice patterns and workflows, and patients’ needs and preferences.

 The use of IT tools for care coordination forces changes in the typical provider workflow. Joint use of a shared platform outside of a patient visit may require a work culture change for providers accustomed to visit-centric care. Including the patient in such communication is also new and unfamiliar.

 Incorporating this kind of work into routine clinical practice may require substantial training and the development of new work processes, such as establishing dedicated time for using the tools, and protocols for identifying and prioritizing patients’ coordination needs.

 Changes in incentives—such as accountable care&mdash;may make a better business case for providers to purchase and use care coordination IT capabilities, but the shift to newer payment arrangements has been slow. Even under such incentives, there is no evidence that use of IT tools for coordination will result in enough savings to justify purchasing and using the tools. Part of the challenge is the need to provide a compelling value proposition that takes into account the many types of users (eg, patients with varying conditions, patients’ families, physicians of all specialties, nurses, social workers, medical assistants) that vary both across and within organizations.

Technical Capabilities for Care Coordination

Despite the barriers described above, we identified 5 types of IT tools that can facilitate coordination: care plans, dashboards, patient relationship managers, event alerts, and referral tracking

). Some products combine and integrate these capabilities and focus on certain types of care coordination activities. We found no efforts that were comprehensive care coordination solutions. For some of these capabilities, basic forms are in more widespread use (eg, dashboards of quality indicators, text-based care plans). More advanced functionalities (eg, a goal-oriented care plan that supports communication among all members of a care team, alerts for a range of event types that can be “subscribed” to) are largely still under development or being tested in pilots. Care plans were the care coordination functionality described by experts as being in use most frequently, as well as being the most complex and varied. Other functionalities included dashboard, patient relationship managers, event alerts, and referral tracking. We describe care plans below and provide additional details on this functionality and others in the 

Experts described several care plan products developed by companies, including several startups, and those as part of research projects. Other experts described the desired functionality based on their experience as leaders in large provider organizations. Below, we summarize the experts’ input on the following aspects of care plans related to health IT: the definition of care plan and its varying content, types of users involved, definitions of user roles and responsibilities, and methods for tracking members of a care team. The eAppendix contains a summary of how care plans supported task tracking and communication among the care team.

 We found that the term “care plan” was poorly defined and meant different things to different experts. Experts differed in their understanding of the content and purpose of the care plans within a health IT system: a) static text that described brief action steps: annual ophthalmology visit and foot exam; reach a target weight; take BP every day; b) ability to track gaps in care, such as overdue screening tests, and issue reminders to patient, clinicians, or care coordinator; c) care plan structured around a list of problems, each with an associated goal and its assigned intervention; and d) “dynamic” care plans that are accessible between visits and can be updated (experts did not provide any more detailed specifications).

 Care plan functionalities mentioned by several experts were accessible to members of a care team and facilitated interactions among them. Users included clinicians, the patient, patient surrogates (eg, parents or children), care coordinators, and social services. Typically, the care plan functionality was designed primarily for use by care coordinators who would work with other supporting services, such as social workers, diabetic coaches, or fitness coaches. Although physicians usually had access to the functionality, they were rarely actively involved in using it, and when they did, the degree of use varied. For example, one startup company found that some physicians wanted to be fully engaged, overseeing the whole process surrounding a surgical episode of care. Others wanted to create the postsurgical care plan, hand it off to the rest of the care team, and not be involved further.

Definitions of user roles and responsibilities.

 Patients used the care plan functionalities to varying degrees. In some cases, patients were not actively using, or not offered access, to their care plan, partly because experts stated that older, sicker patients often were not interested in using the software to actively manage their care. In other cases, the patient was allowed control of many aspects of the care plan, such as the ability to selectively share specific types of data only with specific team members (eg, patients may not want to share psychological issues with fitness coaches). Some care plans allowed for structured interactions with patients, such as the use of patient assessment surveys that ask about patients’ available resources, goals, and symptoms. These assessments are used in some products to automatically produce or update tasks within the care plan (eg, triggering a care coordinator to give patients educational materials). Experts also described how the care plan functionality defined control and ownership. In one project that used a care plan functionality, only the care coordinator was allowed to edit the care plan; other team members could make suggestions, but could not make official changes. Other care plans are more flexible and allow multiple users to change the care plan.

Methods for tracking members of a care team.

 Implementing care plan functionality requires identifying and tracking the current members of the care team. One expert said that having clinical logic that would suggest or automatically add or remove providers to the team for a specific patient would facilitate the use of care plans. Several experts described products that allowed the patient to specify members of their care team. Other products were more provider-focused and required the provider or care coordinator to “invite” providers who might practice in the same organization or a different one. We found no widely agreed-upon definition of a care team member, and given that care team membership changes over time, this complicates the efforts of those developing these functionalities

Although the 5 IT capabilities above are clear signs of innovation, we found 3 important limitations. First, few care coordination products are designed for or used by physicians—this is particularly true for care plans. Although there are certainly care coordination activities that may not need to involve physicians, complex patients commonly receive care from multiple physicians,

 and some coordination-related issues that involve physicians are well-documented (eg, missing or unclear information at the point of care, unclear accountability for next steps, “dropping the baton” on referrals).

 Yet, few efforts described by discussants engaged physicians to address these issues.

Second, the types of activities that the IT tools support vary widely and lack standardized definitions, which reflects the barriers cited above; ultimately, user roles and responsibilities are poorly understood. For example, “care plans” could mean anything from a simple text box to a structured list of problems with associated goals and assigned interventions. Other ways in which care plans varied included the types of users for whom the capabilities were designed, the ability granted to patients to perform tasks, tracking members of a care team (patient invite vs provider invite), support for tracking tasks as part of the care plan, and lastly, the ability to communicate through messages embedded within the care plan. This variation and lack of standard definitions for describing the technical capability was a source of frustration for executives from provider organizations and made it challenging for them to understand the capabilities or compare products.

Third, care coordination products are often not easily integrated into existing physician workflows. Some experts believed that electronic health record (EHR) integration was required for physicians to agree to use the capabilities. Others said that care coordination capabilities could serve as a kind of bridge between different EHRs, because they would contain data from multiple systems. However, in order for such capabilities to bridge this EHR gap, care coordinators were often required to manually translate information (eg, physician orders) from the EHRs into separate care plan software. EHR integration challenges were less of a problem when the care plan capability was offered as part of an EHR vendor’s integrated solution, as certain data elements were populated automatically from the patients’ medical record, facilitating the generation of the care plan. Some experts expressed optimism in a “post-EHR” world in which innovative care coordination tools could be more easily integrated into the products used by providers.

Although the potential for IT to improve the coordination of complex processes has been demonstrated in many industries, the use of IT to improve coordination of care for complex patients is less clear. Substantial barriers impede development and the use of these tools in healthcare. Recent efforts are focusing on developing innovative solutions, but are nevertheless struggling with the barriers. Below, we discuss 3 key challenges, as well as policy opportunities to address them.

Challenge 1: The information and communication needs of users are diverse and poorly understood. 

Understanding user needs is a prerequisite for building effective technology. To improve care coordination of complex patients, the needs of every type of user (eg, physicians of every specialty, nurses, social workers, patients, family members) and the types of coordination activities in which they participate must be understood. This knowledge is needed to allow IT tool developers to accommodate different types and levels of engagement by various types of users as they perform a range of coordination activities. Amassing this knowledge is complicated by the wide variation in workflows across the healthcare system, as well as diversity of users’ preferences. For example, one recent study assessed preferences of elderly patients for information sharing with their caregivers and found substantial complexity and diversity.

 Furthermore, information needs will undoubtedly change; as with other technologies, the roles, responsibilities, and workflows of users will need to be redesigned to make full use of new capabilities.

Two activities could help address this problem. First, conducting an extensive assessment of user needs. This work will require characterizing the workflows and preference of each type of user, establishing standard definitions for user needs, and describing prototypical roles and responsibilities. Second, experimenting with the redesign of both workflows and IT capabilities to address a subset of critical user needs. It will be important to perform rapid cycles of testing, evaluation, and refinement of workflows and IT capabilities, so that care coordination interventions can be improved efficiently.

Challenge 2: Care coordination capabilities interrupt provider workflows.

 To fully engage physicians and other providers in care coordination, the burden of accessing care coordination IT capabilities must be minimized. If EHR vendors developed the capabilities to be well-integrated into existing products, workflow interruptions could easily be reduced for some patients. However, patients are likely to continue to receive care from providers in different organizations given existing patterns of fragmentation.

 Thus, there will be an ongoing need for care coordination IT capabilities to integrate with multiple EHR vendors to avoid workflow interruptions for patients who receive care from provider who use different EHRs.

Improvement in integration could be achieved through novel care coordination capabilities and/or improved interoperability of health IT more generally. Given that EHR vendors have many competing priorities, organizational capacity to focus specifically on developing novel care coordination functionalities may be limited. Therefore, we believe that the most promising path to innovation is through improved interoperability. Ongoing health IT interoperability efforts are attempting to develop standards that would make it easier for healthcare applications to integrate with each other.

 Such standards could facilitate integration of care coordination capabilities and ultimately reduce workflow interruptions.

The challenge to this approach, however, is to develop technical standards for capabilities that have not yet been specified. To better specify care coordination capabilities, particularly care plans, policymakers may consider convening experts to decide on key use cases (specifications of how and by whom the technical capabilities will be used) to guide standards efforts. Such efforts may be informed by the user needs assessments described above.

Challenge 3: Existing incentives are not sufficient.

 The lack of reimbursement for the care coordination supported by IT capabilities means that only risk-sharing or other emerging forms of accountable care provide incentives for adopting these technologies. However, there may be a “chicken and egg” problem: providers may resist taking on risk without proven tools and methods to manage the care of their highest-cost patients. Yet, without a market of providers to drive innovation, such tools may not be developed. Policy makers may overcome this problem by sponsoring research the helps to develop the tools as described above, demonstrate their efficacy, and identify best practices.

This work has several limitations. First, we limited our expert discussions to the United States because the international situation was beyond the scope of this project. Second, this was not a formal qualitative research study and we did not transcribe the discussions. We did conduct the discussions rigorously, requiring at least 2 project team members at each discussion and following up with e-mail for clarifications as needed. Finally, new innovations could have emerged since our discussions given the high level of interest in cost-effective coordination strategies related to payment reform.

Although health IT capabilities are being developed to facilitate coordination of care of complex patients, much is still unknown. Developing successful health IT capabilities should begin with a deep understanding of user needs and will require iterative development in which changes are made to both the technology and workflows. There may be a role for policy to address these critical gaps in knowledge and kick-start the development of better solutions.

 RAND Corporation (RR, ZP, CG), Boston, MA; The Commonwealth Fund (ES), New York, NY.

 This research was funded under contract #HHSP23320095649WC from the Assistant Secretary for Planning and Evaluation (ASPE), US Department of Health and Human Services (HHS). The findings and conclusions of this report are those of the authors and do not necessarily represent the views of ASPE or HHS.

 Concept and design (CG, RR, ES); acquisition of data (ZP, RR); analysis and interpretation of data (ZP, RR); drafting of the manuscript (CG, ZP, RR); critical revision of the manuscript for important intellectual content (CG, ZP, RR, ES); obtaining funding (CG, RR); administrative, technical, or logistic support (ZP); and supervision (RR, ES).

 Robert S. Rudin, PhD, RAND Corporation, 20 Park Plaza, Ste 920, Boston, MA 02116. E-mail: rrudin@rand.org.

1. Medicaid: a small share of enrollees consistently accounted for a large share of expenditures. Government Accountability Office website. http://www.gao.gov/assets/680/670112.pdf. Published May 2015. Accessed February 22, 2016.

This study examines the leading edge of health information technology used to coordinate the care of complex patients.

Robert S. Rudin, PhD

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