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The American Journal of Managed Care January 2018
Measuring Overuse With Electronic Health Records Data
Thomas Isaac, MD, MBA, MPH; Meredith B. Rosenthal, PhD; Carrie H. Colla, PhD; Nancy E. Morden, MD, MPH; Alexander J. Mainor, JD, MPH; Zhonghe Li, MS; Kevin H. Nguyen, MS; Elizabeth A. Kinsella, BA; and Thomas D. Sequist, MD, MPH
The Health Information Technology Special Issue: Has IT Become a Mandatory Part of Health and Healthcare?
Jacob Reider, MD
Bridging the Digital Divide: Mobile Access to Personal Health Records Among Patients With Diabetes
Ilana Graetz, PhD; Jie Huang, PhD; Richard J. Brand, PhD; John Hsu, MD, MBA, MSCE; Cyrus K. Yamin, MD; and Mary E. Reed, DrPH
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Electronic Health Record "Super-Users" and "Under-Users" in Ambulatory Care Practices
Juliet Rumball-Smith, MBChB, PhD; Paul Shekelle, MD, PhD; and Cheryl L. Damberg, PhD
Hospital Participation in Meaningful Use and Racial Disparities in Readmissions
Mark Aaron Unruh, PhD; Hye-Young Jung, PhD; Rainu Kaushal, MD, MPH; and Joshua R. Vest, PhD, MPH
A Cost-Effectiveness Analysis of Cardiology eConsults for Medicaid Patients
Daren Anderson, MD; Victor Villagra, MD; Emil N. Coman, PhD; Ianita Zlateva, MPH; Alex Hutchinson, MBA; Jose Villagra, BS; and J. Nwando Olayiwola, MD, MPH
Electronic Health Record Problem Lists: Accurate Enough for Risk Adjustment?
Timothy J. Daskivich, MD, MSHPM; Garen Abedi, MD, MS; Sherrie H. Kaplan, PhD, MPH; Douglas Skarecky, BS; Thomas Ahlering, MD; Brennan Spiegel, MD, MSHS; Mark S. Litwin, MD, MPH; and Sheldon Greenfield, MD
Racial/Ethnic Variation in Devices Used to Access Patient Portals
Eva Chang, PhD, MPH; Katherine Blondon, MD, PhD; Courtney R. Lyles, PhD; Luesa Jordan, BA; and James D. Ralston, MD, MPH
Hospitalized Patients' and Family Members' Preferences for Real-Time, Transparent Access to Their Hospital Records
Michael J. Waxman, MD, MPH; Kurt Lozier, MBA; Lana Vasiljevic, MS; Kira Novakofski, PhD; James Desemone, MD; John O'Kane, RRT-NPS, MBA; Elizabeth M. Dufort, MD; David Wood, MBA; Ashar Ata, MBBS, PhD; Louis Filhour, PhD, RN; & Richard J. Blinkhorn Jr, MD

Electronic Health Record "Super-Users" and "Under-Users" in Ambulatory Care Practices

Juliet Rumball-Smith, MBChB, PhD; Paul Shekelle, MD, PhD; and Cheryl L. Damberg, PhD
Nearly 40% of US ambulatory care practices are “under-users” of health information technology functionalities, which impacts the ability of the health system as a whole to provide coordinated, efficient care.

This study explored variation in the extent of use of electronic health record (EHR)-based health information technology (IT) functionalities across US ambulatory care practices. Use of health IT functionalities in ambulatory care is important for delivering high-quality care, including that provided in coordination with multiple practitioners.

Study Design: We used data from the 2014 Healthcare Information and Management Systems Society Analytics survey. The responses of 30,123 ambulatory practices with an operational EHR were analyzed to examine the extent of use of EHR-based health IT functionalities for each practice.

Methods: We created a novel framework for classifying ambulatory care practices employing 7 domains of health IT functionality. Drawing from the survey responses, we created a composite “use” variable indicating the extent of health IT functionality use across these domains. “Super-user” practices were defined as having near-full employment of the 7 domains of health IT functionalities and “under-users” as those with minimal or no use of health IT functionalities. We used multivariable logistic regression to investigate how the odds of super-use and under-use varied by practice size, type, urban or rural location, and geographic region.

Results: Seventy-three percent of practices were not using EHR technologies to their full capability, and nearly 40% were classified as under-users. Under-user practices were more likely to be of smaller size, situated in the West, and located outside a metropolitan area.

Conclusions: To achieve the broader benefits of the EHR and health IT, health systems and policy makers need to identify and address barriers to full use of health IT functionalities.

Am J Manag Care. 2018;24(1):26-31
Takeaway Points
As of 2014, 73% of ambulatory practices were not using electronic health record (EHR)-based functionalities to their full capability, and nearly 40% were classified as health information technology (IT) “under-users.”
  • Under-use of health IT in ambulatory care has implications for the ability of the health system as a whole to provide coordinated and efficient care. 
  • Facilitating the full use of a range of health IT tools in the ambulatory setting may help the broader health system gain the full benefit of investments in EHR-based technologies. 
  • Efforts to increase the use of health IT functionalities should focus on practices that are small, are located in nonmetropolitan areas, and provide specialty care.
Healthcare organizations across the United States have invested substantially in electronic health record (EHR) systems, incentivized by federal investment and legislation.1 Ambulatory care practices have steadily improved their EHR adoption over the last decade; 2014 estimates indicated that approximately 78% of ambulatory care practices had a certified EHR platform.2,3 There is substantial heterogeneity within this group, however. The EHR acts as a backbone for a range of health information technology (IT) functionalities with multiple potential applications to care delivery; practices vary in their adoption of these functionalities and in the extent of their use of these tools in routine practice.

Empirical data show benefit to processes of care from an array of health IT functionalities, including data repository,4 computerized order entry,5,6 electronic messaging and health information exchange,7 patient-facing tools,8,9 and clinical decision support.5,10 In addition, quality improvements from the EHR and associated functionalities likely transcend the individual provider organization, with some tools (such as health information exchange) designed to work in synergy for coordination of care among multiple practitioners.11 Practices restricting themselves to the more basic features of this technology may limit the potential impact of the EHR on their own performance4,12,13; it is also possible that slow or elementary adopters may have a negative impact on the quality of the health system as a whole.

In this study, we explored variation in the extent of use of EHR-based health IT functionalities in the ambulatory care setting. We used data from the Healthcare Information and Management Systems Society (HIMSS) Analytics ambulatory practice surveys to create a new framework of EHR use across 7 domains of health IT functionality, and we identified practices that were high users of a range of functionalities (“super-users”) and those that used these EHR tools only minimally (“under-users”). Noting that studies on hospital EHR adoption suggest that small and rural hospitals may experience greater barriers in implementing this technology,14 we investigated how the rates of super-use and under-use vary according to practice size, type, urban or rural location, and geographic region.


HIMSS conducts annual surveys of US health systems and organizations, with a particular focus on structural characteristics of their EHR and health IT functionalities in use, generating a comprehensive database that has been frequently used in empirical research.15-18 To date, published studies that have employed these data utilized only the data regarding hospitals.19 However, HIMSS also obtains data on ambulatory care practices, defined as facilities providing “preventative, diagnostic, therapeutic, surgical, and/or rehabilitative outpatient care where the duration of treatment is less than 24 hours—and is generally referred to as outpatient care.” We used data from the 2014 ambulatory practice survey, which contains information on more than 75% of US health system–associated ambulatory care practices. HIMSS defines a health system as an organization composed of at least 1 hospital and its associated nonacute facilities, and “associated” as a governance relationship (ie, they are owned, leased, or managed by a health system). Eligible practices for our study were those that indicated they had a “live and operational” EHR and had completed at least 1 health IT functionality survey question. We linked the practice site zip code with a publicly available dataset providing a geographic taxonomy to develop a measure of rurality.20

Existing EHR classifications applicable to the ambulatory care setting have limitations; many are defined by only short lists of Meaningful Use criteria,21 and categorizations of “basic” or “comprehensive” systems are largely hospital-focused. We created a novel framework for classifying ambulatory care practices using 7 domains of health IT functionality, referencing the structure of the HIMSS survey and historical taxonomies (such as that by Des Roches et al22). The 7 domains were data repository, clinical decision support, order entry management, electronic messaging, results management, health information exchange, and patient use. The HIMSS survey asks respondents to indicate if they use any of more than 50 EHR-based health IT functionalities and, in some cases, assesses the intensity of this use (eg, “What proportion of orders are completed using the EHR?”). We matched all of these items to 1 of the 7 domains of functionality (details are given in the eAppendix [available at]).

We used a 3-step process to define a practice as a super-user or under-user of health IT functionalities. First, we classified practices into 3 categories based on the number of functionalities employed within each domain. Practices in the lower quartile for their sum total of functionality within a domain were categorized as “low” (score of 0), those in the upper quartile were defined as “high” (score of 2), and practices in the interquartile range were categorized as “moderate” (score of 1). Second, we created a composite “use” variable by summing the domain scores for each practice (composite scores ranged from a minimum of 0 to a maximum of 14). Third, we ranked practices according to this composite variable. We explored the natural distribution of the data in order to identify practices that were low and high outliers on the composite score. We defined practices as super-users if they had a composite score of 12 to 14 and under-users if their composite score was 0 to 2. We performed sensitivity analyses to explore the impact of alternative criteria; our findings were robust to alternate specification of the cut points.

We examined characteristics of practices according to their classification as a super-user or under-user, using Pearson’s χ2 test for the categorical variables and a 2-sided t test for the continuous variable. Variables of interest included the size of the practice (defined as number of affiliated physicians, in 4 categories), location (metropolitan, midsize, small town, or rural), geographical region (Northeast, Midwest, South, or West), and type of practice (primary/family care; single-specialty, multispecialty, and allied health; or urgent care and specialist services). Allied health practices included those practicing podiatry, occupational health, weight management, and holistic medicine, among others. Practices providing “specialist services” were those giving specialty-circumscribed care to a defined population (eg, patients undergoing dialysis or cardiac rehabilitation). Using multivariable logistic regression models, we estimated odds ratios associated with super-user and under-user status, according to practice characteristics. Analyses were performed using Stata version 14.2 (StataCorp LLC; College Station, Texas). We used Quantum Geographic Information Software to create maps showing the distribution of use categories across the United States.


There were 38,638 health system–affiliated practices in the HIMSS data; 32,236 (83.4%) indicated they had a live and operational EHR, and of these, 30,123 (93.5%) provided survey responses. The majority (77.4%) of responding practices in the sample had fewer than 7 associated physicians; however, the distribution of this variable was skewed by some practices with large numbers of physicians (maximum, 2300) such that the median number of physicians per practice was 2 and the mean was 5.6. The dominant practice type was single or multiple specialty and allied health practitioners (62.5%), whereas 30.8% were primary/family medicine. Nearly 75% of practices were located in metropolitan areas; only 4.7% were rurally located.

Table 1 shows the proportions of practices with low, moderate, and high use by domain of health IT functionality. The eAppendix provides the full table of functionalities and frequency of responses and the descriptive characteristics of the total sample and super-user and under-user practices. Among practices indicating any use of computerized physician order entry, only 35.6% used this capacity for more than 75% of orders. Additionally, although the majority of practices were adept at using their EHR for more elementary functions, such as data storage (100% of practices stored transcribed reports electronically and 61.1% used the EHR for nursing documentation), some of the more advanced functionalities (such as the ability to find and modify orders for all patients on a specific medication) were used at much lower rates (29.3%).

Table 2 gives the findings of the multivariable analyses, in which 8003 practices were classified as health IT super-users (26.6%). The odds of super-user status were lower for single-specialty, multispecialty, and allied health practices than for primary/family care clinics, and lower still for practices providing specialist services or acute care. The likelihood of super-use increased as the number of affiliated physicians increased, and super-users were more than twice as likely to be located in metropolitan areas than rural. Overall, the odds of being a super-user were highest for practices in the Midwest.

In contrast, 11,706 practices (38.9%) were classified as health IT under-users. Under-user practices were more likely to be situated in the West, have fewer affiliated physicians, and be located outside of metropolitan centers. Compared with primary/family care practices, single-specialty, multispecialty, and allied health practices were more likely to be under-users, as were those that provided specialist or acute care services. Figure 1, Figure 2, and Figure 3 give the geographical location of super- and under-users and the proportion of these practices by county.


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