POLICY Electroic Health Record Super-Users ad Uder-Users i Ambulatory Care Practices Juliet Rumball-Smith, MBChB, PhD; Paul Shekelle, MD, PhD; ad Cheryl L. Damberg, PhD Healthcare orgaizatios across the Uited States have ivested substatially i electroic health record (EHR) systems, icetivized by federal ivestmet ad legislatio. 1 Ambulatory care practices have steadily improved their EHR adoptio over the last decade; 2014 estimates idicated that approximately 78% of ambulatory care practices had a certified EHR platform. 2,3 There is substatial heterogeeity withi this group, however. The EHR acts as a backboe for a rage of health iformatio techology (IT) fuctioalities with multiple potetial applicatios to care delivery; practices vary i their adoptio of these fuctioalities ad i the extet of their use of these tools i routie practice. Empirical data show beefit to processes of care from a array of health IT fuctioalities, icludig data repository, 4 computerized order etry, 5,6 electroic messagig ad health iformatio exchage, 7 patiet-facig tools, 8,9 ad cliical decisio support. 5,10 I additio, quality improvemets from the EHR ad associated fuctioalities likely trasced the idividual provider orgaizatio, with some tools (such as health iformatio exchage) desiged to work i syergy for coordiatio of care amog multiple practitioers. 11 Practices restrictig themselves to the more basic features of this techology may limit the potetial impact of the EHR o their ow performace 4,12,13 ; it is also possible that slow or elemetary adopters may have a egative impact o the quality of the health system as a whole. I this study, we explored variatio i the extet of use of EHRbased health IT fuctioalities i the ambulatory care settig. We used data from the Healthcare Iformatio ad Maagemet Systems Society (HIMSS) Aalytics ambulatory practice surveys to create a ew framework of EHR use across 7 domais of health IT fuctioality, ad we idetified practices that were high users of a rage of fuctioalities ( super-users ) ad those that used these EHR tools oly miimally ( uder-users ). Notig that studies o hospital EHR adoptio suggest that small ad rural hospitals may experiece greater barriers i implemetig this techology, 14 we ivestigated how the rates of super-use ad uder-use vary ABSTRACT OBJECTIVES: This study explored variatio i the extet of use of electroic health record (EHR)-based health iformatio techology (IT) fuctioalities across US ambulatory care practices. Use of health IT fuctioalities i ambulatory care is importat for deliverig high-quality care, icludig that provided i coordiatio with multiple practitioers. STUDY DESIGN: We used data from the 2014 Healthcare Iformatio ad Maagemet Systems Society Aalytics survey. The resposes of 30,123 ambulatory practices with a operatioal EHR were aalyzed to examie the extet of use of EHR-based health IT fuctioalities for each practice. METHODS: We created a ovel framework for classifyig ambulatory care practices employig 7 domais of health IT fuctioality. Drawig from the survey resposes, we created a composite use variable idicatig the extet of health IT fuctioality use across these domais. Super-user practices were defied as havig ear-full employmet of the 7 domais of health IT fuctioalities ad uder-users as those with miimal or o use of health IT fuctioalities. We used multivariable logistic regressio to ivestigate how the odds of super-use ad uder-use varied by practice size, type, urba or rural locatio, ad geographic regio. RESULTS: Sevety-three percet of practices were ot usig EHR techologies to their full capability, ad early 40% were classified as uder-users. Uder-user practices were more likely to be of smaller size, situated i the West, ad located outside a metropolita area. CONCLUSIONS: To achieve the broader beefits of the EHR ad health IT, health systems ad policy makers eed to idetify ad address barriers to full use of health IT fuctioalities. Am J Maag Care. 2018;24(1):26-31 26 JANUARY 2018 www.ajmc.com
Ambulatory Care Practices: Super- ad Uder-Users of EHRs accordig to practice size, type, urba or rural locatio, ad geographic regio. TAKEAWAY POINTS METHODS HIMSS coducts aual surveys of US health systems ad orgaizatios, with a particular focus o structural characteristics of their EHR ad health IT fuctioalities i use, geeratig a comprehesive database that has bee frequetly used i empirical research. 15-18 To date, published studies that have employed these data utilized oly the data regardig hospitals. 19 However, HIMSS also obtais data o ambulatory care practices, defied as facilities providig prevetative, diagostic, therapeutic, surgical, ad/or rehabilitative outpatiet care where the duratio of treatmet is less tha 24 hours ad is geerally referred to as outpatiet care. We used data from the 2014 ambulatory practice survey, which cotais iformatio o more tha 75% of US health system associated ambulatory care practices. HIMSS defies a health system as a orgaizatio composed of at least 1 hospital ad its associated oacute facilities, ad associated as a goverace relatioship (ie, they are owed, leased, or maaged by a health system). Eligible practices for our study were those that idicated they had a live ad operatioal EHR ad had completed at least 1 health IT fuctioality survey questio. We liked the practice site zip code with a publicly available dataset providig a geographic taxoomy to develop a measure of rurality. 20 Existig EHR classificatios applicable to the ambulatory care settig have limitatios; may are defied by oly short lists of Meaigful Use criteria, 21 ad categorizatios of basic or comprehesive systems are largely hospital-focused. We created a ovel framework for classifyig ambulatory care practices usig 7 domais of health IT fuctioality, referecig the structure of the HIMSS survey ad historical taxoomies (such as that by Des Roches et al 22 ). The 7 domais were data repository, cliical decisio support, order etry maagemet, electroic messagig, results maagemet, health iformatio exchage, ad patiet use. The HIMSS survey asks respodets to idicate if they use ay of more tha 50 EHR-based health IT fuctioalities ad, i some cases, assesses the itesity of this use (eg, What proportio of orders are completed usig the EHR? ). We matched all of these items to 1 of the 7 domais of fuctioality (details are give i the eappedix [available at ajmc.com]). We used a 3-step process to defie a practice as a super-user or uder-user of health IT fuctioalities. First, we classified practices ito 3 categories based o the umber of fuctioalities employed withi each domai. Practices i the lower quartile for their sum total of fuctioality withi a domai were categorized as low (score of 0), those i the upper quartile were defied as high (score As of 2014, 73% of ambulatory practices were ot usig electroic health record (EHR)-based fuctioalities to their full capability, ad early 40% were classified as health iformatio techology (IT) uder-users. Uder-use of health IT i ambulatory care has implicatios for the ability of the health system as a whole to provide coordiated ad efficiet care. Facilitatig the full use of a rage of health IT tools i the ambulatory settig may help the broader health system gai the full beefit of ivestmets i EHR-based techologies. Efforts to icrease the use of health IT fuctioalities should focus o practices that are small, are located i ometropolita areas, ad provide specialty care. of 2), ad practices i the iterquartile rage were categorized as moderate (score of 1). Secod, we created a composite use variable by summig the domai scores for each practice (composite scores raged from a miimum of 0 to a maximum of 14). Third, we raked practices accordig to this composite variable. We explored the atural distributio of the data i order to idetify practices that were low ad high outliers o the composite score. We defied practices as super-users if they had a composite score of 12 to 14 ad uder-users if their composite score was 0 to 2. We performed sesitivity aalyses to explore the impact of alterative criteria; our fidigs were robust to alterate specificatio of the cut poits. We examied characteristics of practices accordig to their classificatio as a super-user or uder-user, usig Pearso s χ 2 test for the categorical variables ad a 2-sided t test for the cotiuous variable. Variables of iterest icluded the size of the practice (defied as umber of affiliated physicias, i 4 categories), locatio (metropolita, midsize, small tow, or rural), geographical regio (Northeast, Midwest, South, or West), ad type of practice (primary/family care; sigle-specialty, multispecialty, ad allied health; or urget care ad specialist services). Allied health practices icluded those practicig podiatry, occupatioal health, weight maagemet, ad holistic medicie, amog others. Practices providig specialist services were those givig specialtycircumscribed care to a defied populatio (eg, patiets udergoig dialysis or cardiac rehabilitatio). Usig multivariable logistic regressio models, we estimated odds ratios associated with superuser ad uder-user status, accordig to practice characteristics. Aalyses were performed usig Stata versio 14.2 (StataCorp LLC; College Statio, Texas). We used Quatum Geographic Iformatio Software to create maps showig the distributio of use categories across the Uited States. RESULTS There were 38,638 health system affiliated practices i the HIMSS data; 32,236 (83.4%) idicated they had a live ad operatioal EHR, ad of these, 30,123 (93.5%) provided survey resposes. The majority (77.4%) of respodig practices i the sample had fewer tha 7 associated physicias; however, the distributio of this variable THE AMERICAN JOURNAL OF MANAGED CARE VOL. 24, NO. 1 27
POLICY TABLE 1. Number ad Percetage of Practices Reportig Use of Health IT Fuctioalities by EHR Domai (N = 30,123) Low Use Moderate Use High Use Domai % % % Data repository 9850 32.7 9707 32.2 10,566 35.1 Cliical decisio support 12,553 41.7 9095 30.2 8475 28.1 Order etry maagemet 12,533 41.6 5722 19.0 11,868 39.4 Electroic messagig 14,299 47.5 8032 26.7 7792 25.9 Results maagemet 16,897 56.1 1931 6.4 11,295 37.5 Health iformatio exchage 12,821 42.6 8839 29.3 8463 28.1 Patiet use of EHR tools 14,649 48.6 7043 23.4 8431 28.0 EHR idicates electroic health record; IT, iformatio techology. practice was 2 ad the mea was 5.6. The domiat practice type was sigle or multiple specialty ad allied health practitioers (62.5%), whereas 30.8% were primary/family medicie. Nearly 75% of practices were located i metropolita areas; oly 4.7% were rurally located. Table 1 shows the proportios of practices with low, moderate, ad high use by domai of health IT fuctioality. The eappedix provides the full table of fuctioalities ad frequecy of resposes ad the descriptive characteristics of the total sample ad super-user ad uder-user practices. Amog practices idicatig ay use of computerized physicia order etry, oly 35.6% used this capacity for more tha 75% of orders. Additioally, although the majority of practices were adept at usig their EHR for more elemetary fuctios, such as data storage (100% of practices stored trascribed reports electroically ad 61.1% used TABLE 2. Odds of Super- or Uder-Use by Practice Characteristics a the EHR for ursig documetatio), some of Super-User Uder-User the more advaced fuctioalities (such as the OR 95% CI OR 95% CI ability to fid ad modify orders for all patiets Practice Size (umber of o a specific medicatio) were used at much associated physicias) lower rates (29.3%). <7 Referece Table 2 gives the fidigs of the multivariable 7-19 1.61 (1.49-1.75) 0.77 (0.71-0.83) aalyses, i which 8003 practices were 20-99 2.06 (1.82-2.32) 0.66 (0.58-0.75) classified as health IT super-users (26.6%). The 100 3.24 (2.95-3.55) 0.78 (0.71-0.85) odds of super-user status were lower for siglespecialty, multispecialty, ad allied health Practice Type practices tha for primary/family care cliics, Primary/family Referece ad lower still for practices providig specialist services or acute care. The likelihood of Sigle or multiple specialty, 0.89 (0.83-0.94) 1.08 (1.03-1.14) allied health super-use icreased as the umber of affiliated physicias icreased, ad super-users Specialist services 0.64 (0.57-0.72) 1.40 (1.27-1.55) ad urget care Locatio were more tha twice as likely to be located Rural Referece i metropolita areas tha rural. Overall, the odds of beig a super-user were highest for Small tow 1.02 (0.84-1.22) 0.99 (0.86-1.13) practices i the Midwest. Midsize 0.99 (0.83-1.18) 0.95 (0.84-1.08) I cotrast, 11,706 practices (38.9%) were Metropolita 2.45 (2.10-2.85) 0.61 (0.54-0.68) classified as health IT uder-users. Uderuser Regio practices were more likely to be situated Northeast Referece i the West, have fewer affiliated physicias, Midwest 2.05 (1.90-2.22) 0.59 (0.56-0.64) ad be located outside of metropolita ceters. South 1.32 (1.22-1.42) 0.98 (0.92-1.04) Compared with primary/family care West 0.93 (0.85-1.03) 1.16 (1.07-1.26) practices, sigle-specialty, multispecialty, OR idicates odds ratio. ad allied health practices were more likely a Aalysis excludes 115 practices without accurate zip code iformatio. to be uder-users, as were those that provided specialist or acute care services. Figures 1, 2, was skewed by some practices with large umbers of physicias (maximum, 2300) such that the media umber of physicias per ad 3 give the geographical locatio of super- ad uder-users ad the proportio of these practices by couty. DISCUSSION We examied variatio i the extet of use of EHR-based health IT fuctioalities i a atioal sample of US ambulatory care 28 JANUARY 2018 www.ajmc.com
Ambulatory Care Practices: Super- ad Uder-Users of EHRs FIGURE 1. Locatio of Super-User (Gree) ad Uder-User (Blue) Practices practices. Amog 30,123 practices that were affiliated with a health system ad had a operatioal EHR, oly 27% were super-users, meaig they were maximally usig EHR fuctioalities desiged to improve patiet care ad facilitate high-quality performace across the broader health system. Of cocer was that early 40% of ambulatory practices were categorized as uder-users, idicatig miimal use of the EHR ad associated health IT fuctioalities. Uder-use was more likely i smaller practices, those located outside of metropolita ceters, o primary care practices, ad those situated i the West. There are likely multiple factors ivolved i EHR ad health IT uder-use by ambulatory care practices. Cost has bee cited as the primary barrier to adoptig a EHR system 23 ; similarly, upgradig a basic EHR to oe more comprehesive may ot be fiacially possible for practices with limited resources. Cost may also be a factor for these practices because of the health IT support resources required to trai users ad maitai the systems. Smaller ad rural practices were more likely to be uder-users, a patter also see i the adoptio of EHR ad health IT i hospitals. 24 These types of practices may face fiacial, huma resource, or structural barriers that impede their ability to use their EHR to full capacity. We foud that super-use was least prevalet i practices providig specialist-level care ad services; it is possible that these practices are less well served by existig health IT fuctioalities ad require specific tools developed for more specialized cliical scearios. Policy makers should cosider how to address the barriers of the small, ometropolita, ad specialist practices to usig their health IT fuctioalities more fully, as the relative uder-use of these tools has far-reachig implicatios. First, suboptimal use of critical health IT fuctioalities may have direct relevace for the quality of care provided by a idividual practice as part of routie patiet care. Secod, uder-use of these techologies (such as health iformatio exchage) may have cosequeces for the quality of care provided across the ambulatory care sector. Curret policies, such as shared risk programs, ecourage commuity-based strategies to avoid costly hospitalizatios; similarly, value-based purchasig holds providers accoutable for care delivered by multiple practitioers. It is also otable that the diversity of ambulatory care providers has expaded i recet years 25 ; our sample icluded more tha 50 types of ambulatory specialty services. This tred ad the aforemetioed policies suggest that commuicatio ad care coordiatio amog ambulatory care providers is more crucial tha ever. Third, the iteroperability of the broader digital health system is essetial for the etwork beefits of health IT ad EHR systems, yet differeces i EHR capacity betwee the US hospital sector ad ambulatory care are substatial. For example, trasitios i care are a crucial task for both primary ad tertiary health providers. The Office of the Natioal Coordiator for Health Iformatio Techology reported that approximately 49% of hospitals could geerate a care summary documet i 2014 (a low estimate that shows room for improvemet) 26 ; however, oly 39% of ambulatory practices i our sample were able to create ad trasmit a equivalet report. Especially tellig is the 2013 estimate that 77% of hospitals had the capacity to sed laboratory results to ambulatory THE AMERICAN JOURNAL OF MANAGED CARE VOL. 24, NO. 1 29
POLICY FIGURE 2. Proportio of Super-User Practices, by Couty LEGEND Proportio of SuperUsers by Couty 0%-24.9% 25%-49.9% 50%-74.9% 75%-100% FIGURE 3. Proportio of Uder-User Practices, by Couty LEGEND Proportio of UderUsers by Couty 0%-24.9% 25%-49.9% 50%-74.9% 75%-100% providers27 compared with the 49% of ambulatory practices that either super- or uder-users. Secod, this is the first publicatio were able to commuicate with hospitals for cliical iformatio. usig HIMSS ambulatory care data, ad their validity has ot bee Hospitals with advaced EHR systems are fudametally limited examied by the research commuity. However, may published if there are fuctioal restrictios o their ability to iteract with studies have used the HIMSS hospital dataset,15-17 which utilizes caregivers ad orgaizatios i the commuity settig. Limitatios This study has some limitatios. First, we used 2014 reported data, the same samplig ad survey methodology as the ambulatory practice survey that provided the data i our study. Oe such study describes this source as the idustry stadard for iformatio o EMR [electroic medical record] adoptio. 18 Accordigly, a ad practices may have sice expaded their health IT fuctioality. stregth of our study is its presetatio of the first-ever aalysis However, chages sice this time are likely to be icremetal oly; of the correspodig data from HIMSS about ambulatory care give that we have focused o the outliers of EHR use, it is ulikely health IT use. The HIMSS survey represets oe of the most com- that there would be substatial alteratios i the proportios of prehesive assessmets of use of health IT that curretly exists; our 30 JANUARY 2018 www.ajmc.com
Ambulatory Care Practices: Super- ad Uder-Users of EHRs study exteded the curret taxoomy of EHR systems well beyod that of basic ad comprehesive. Fially, the survey icludes oly ambulatory practices that are affiliated with a health system. Give a presumed desire for system iteroperability, we might expect greater use of some health IT fuctioalities (such as health iformatio exchage) by the ambulatory practices i our sample compared with idepedet practices. The use of this subset, i cojuctio with our focus o those practices with a preexistig operatioal EHR, suggests that our results may overestimate the true proportio of super-users i the broader ambulatory care settig ad that the atioal rate is eve lower; the reverse is also likely true for the estimates of uder-users. CONCLUSIONS Although it is critical for ambulatory care practices to have the buildig block of the EHR, a substatial proportio of these practices use this techology oly miimally, idicatig there is capacity for sigificat improvemet. It is importat that policy makers ad healthcare providers uderstad the limits of health IT fuctioality i ambulatory care practices, as strategies aimed at improvig the coordiatio of care or those relyig o the EHR as a vehicle for itervetio may be hidered by the techological capacity of ambulatory care parters. We also suggest that policy makers idetify the barriers limitig the use of these tools i ambulatory care (i particular those related to small, rural, ad specialty practices) ad cosider how best to facilitate the full use of a rage of EHR-based health IT fuctioalities. Ivestmet i EHR-based health IT capacity of idividual ambulatory practices will likely have beefits to providers across the ambulatory settig ad to the performace of the broader health system. Ackowledgmets The authors wish to thak Julie Lai ad Julie Newell of RAND Health for their assistace i preparig the data ad performig the GIS mappig. Author Affiliatios: RAND Health (JRS, PS, CLD), Sata Moica, CA; Northlad District Health Board (JRS), Whagarei, New Zealad; Geeral Iteral Medicie, Greater Los Ageles VA Healthcare System (PS), Los Ageles, CA. Source of Fudig: Support for this research was made possible by a Harkess Fellowship i Healthcare Policy ad Practice (Dr Rumball-Smith), awarded by The Commowealth Fud. This work was also supported, i whole or i part, through a cooperative agreemet (1U19HS024067-01) betwee the RAND Corporatio ad the Agecy for Healthcare Research ad Quality. The cotet ad opiios expressed i this publicatio are solely the resposibility of the authors ad do ot reflect the official positio of the Agecy, HHS, or the Commowealth Fud or its directors, officers, or staff. Author Disclosures: The authors report o relatioship or fiacial iterest with ay etity that would pose a coflict of iterest with the subject matter of this article. Authorship Iformatio: Cocept ad desig (JRS); acquisitio of data (CLD); aalysis ad iterpretatio of data (JRS, PS, CLD); draftig of the mauscript (JRS, PS, CLD); critical revisio of the mauscript for importat itellectual cotet (JRS, PS, CLD); statistical aalysis (JRS); obtaiig fudig (JRS, PS, CLD); ad supervisio (PS, CLD). Address Correspodece to: Juliet Rumball-Smith, MBChB, PhD, Tohora House, Northlad District Health Board, Whagarei 0110, New Zealad. Email: jrs@health.rumballsmith.z. REFERENCES 1. America Recovery ad Reivestmet Act of 2009, S 1, 111th Cog (2009). 2. Jamoom EW, Yag N, Hig E. 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eappedix Table 1. Distributio of Resposes by EHR Domai ad HIT Fuctioality Frequecy (%) Media (mea) Rage Iterquartile rage Domai: Use of data repository capacity 4 (6.1) 1 12 1-11 Cliical research data aalysis 9542 (31.7) Curret ecouter procedures 13302 (44.2) Curret ecouter vital sigs icludig height, weight, blood pressure, temperature, etc 12467 (41.4) Nursig documetatio 18391 (61.1) Physicia documetatio 18206 (60.4) Problem lists 13251 (44.0) Structured documet templates (e.g. diabetic workup, aual physical, etc.) creatig discrete data 15269 (50.7) Trascribed reports are stored electroically 30123 (100.0) Ability to create growth charts from the capture of structured data (vital sigs, immuizatios, BMI, etc.) 12637 (42.0) Ability to icorporate curret ecouter procedures ito stadardized format (e.g. CCD, CCR) 12546 (41.6) Medicatio lists o-lie for all patiets 14011 (46.5) Medicatio recociliatio 13225 (43.9) Domai: Cliical decisio support 2 (3.1) 0-10 0-6 Basic medicatio screeig (drug/drug, drug/allergy) 16182 (53.7) Cliical guidelies or protocols 13551 (45.0) Data from the commuity based EHR is icorporated ito the EMR's rules egie ad triggers alerts 5653 (18.8) Geomics profilig is icorporated ito the EMR ad could result i a suggested order or order chage 1829 (6.1) Prevetive medicie (e.g. immuizatios, follow-up testig) 14805 (49.1) Receipt of diagostics results trigger relevat cliical alerts ad cliical 9018 (29.9)
guidace/recommeded care Remote device moitorig process alerts cliicia whe cliically sigificat chages i data are detected 4319 (14.3) Capable of comparig patiet follow-up recommedatios to care redered by all providers with access to the commuity-based EMR ad variace ad 5664 (18.8) compliace alerts are geerated EMR suggests recommeded follow-up based o date, patiet problem list ad procedures redered by curret provider ad others. 9894 (32.8) Follow-up otices set to the patiets are iitiated by flags set by provider 11245 (37.3) Frequecy (%) Media (mea) Rage Iterquartile rage Domai: Order etry maagemet 2 (1.8) 0-4 0-3 Ability to fid ad modify orders for all patiets o a specific medicatio 8824 (29.3) e-prescribig for ew medicatios 17281 (57.4) e-prescribig for refill medicatio requests 16906 (56.1) 75% or more orders completed i this way 10720 (35.6) Domai: Electroic messagig 1 (2.0) 0-5 0-5 Cosult commuicatios 12494 (41.5) Disease maagemet commuicatios 8634 (28.7) Iteral cliic commuicatios 15545 (51.6) Patiet commuicatios 11120 (36.9) Referral commuicatios 12620 (41.9) Domai: Results maagemet 0 (1.03) 0-3 0-2 All lab reports are electroically imported ad stored i discrete structured form OR Textual/data results may be retured via HL 7 trasactios ad stored directly ito patiet records Textual/data results retured electroically i formats such as PDF, CCR, ad CCD, ad the attached to patiet record Output from diagostic ad itelliget medical devices are icorporated directly ito patiet's EMR whe appropriate. 12716 (42.2) 11183 (37.1) 7627 (25.3)
Frequecy (%) Media (mea) Rage Iterquartile rage Domai: Health iformatio exchage 4 (5.3) 1-13 1-10 Capable of exchagig data across multiple vedor platforms for the 10254 (34.0) purpose of health iformatio exchage Web browser o physicia/urse desktops for access to olie referece material, eligibility iformatio, lab results, etc. 30123 (100.0) With exteral registries for reportig of patiet data (e.g. immuizatio, disease or device) 10087 (33.5) With govermetal agecies (e.g. local, couty, state) 11475 (38.1) With hospitals for cliical iformatio OR web-oly access 14663 (48.7) With hospitals for demographic ad isurace iformatio 13516 (44.9) With iteral disease registries for case maagemet 5631 (18.7) With other cliics for cliical iformatio 13585 (45.1) With pharmacies or pharmacy clearighouses (e.g. SureScripts) 13636 (45.3) With referece laboratories 12149 (40.3) With the Ceters for Disease Cotrol 5286 (17.5) Ability to trasmit stadardized format (e.g. CCD, CCR) or other stadardized idividual compoets of patiet's electroic record 11680 (38.8) Ability to update the patiet's EHR where there is a commuity-based HIE 7983 (26.5) Domai: Patiet use 1 (1.6) 0-4 0-4 A patiet portal allowig the patiet to see persoal health iformatio, pay 11663 (38.7) bills, request a schedule, request a appoitmet, etc. Email commuicatios with physicias or urses 12506 (41.5) Patiet Health Record 10816 (35.9) Patiet specific medical educatio cotet 12183 (40.4) All fuctioalities are questios take verbatim from the Healthcare Iformatio ad Maagemet Systems Society survey o ambulatory care practices. BMI = Body Mass Idex, CCD = Cotiuity of Care Documet; CCR = Cotiuity of Care Record; PDF = Portable Documet Format; HL 7= Health Level 7 format; EHR = Electroic health Record; EMR = Electroic Medical Record; HIE = Health iformatio Exchage.
Tables A2 ad A3 show the descriptive characteristics of the practices, accordig to their superuser ad uder-user status of EHR-based HIT fuctioalities, ad that of the total sample. Note that the super- ad uder- categories are ot complemetary; the majority of practices fall ito the middle uclassified category of beig either a super or uder-user. Also ote that the sigificace of the p-values likely reflects the large sample size; differeces betwee the groups may ot be practically meaigful. eappedix Table 2. Descriptive Characteristics of Ambulatory Care Practices Defied as Superusers of EHR-Based HIT Fuctioalities Super-user (%) p Total (%) 8003 (26.6) 30123 Media, mea 3,9.2 < 0.0001 2,5.6 Size < 7 5303 (66.3) 23324 (77.4) 7-19 1146 (14.3) 3415 (11.3) < 0.0001 20-99 487 (6.1) 1222 (4.1) >100 1067 (13.3) 2162 (7.2) (umber of associated physicias) Primary 2458 (30.7) 9289 (30.8) Practice Type Sigle or multiple specialty, allied health 5116 (63.9) <0.0001 18823 (62.5) Specialist services ad urget care 429 (5.4) 2011 (6.7) Locatio Regio Rural 222 (2.8) 1402 (4.7) Small tow 355 (4.5) 2234 (7.4) <0.0001 Mid-size 590 (7.4) 3933 (13.1) Metropolita 6833 (85.4) 22518 (74.8) Northeast 1310 (16.4) 6141 (20.4) Midwest 3264 (40.8) 9756 (32.4) <0.0001 South 2496 (31.2) 9767 (32.4) West 933 (11.7) 4459 (14.8) P values calculated with Pearso s c 2 for categorical variables, they estimate the statistical sigificace of differeces i proportios betwee categories of practice variables i superuse practices compared to the total sample. Two-sided t test performed to test sigificace of differece i mea umber of associated physicias. The aalysis for the locatio excludes 36 practices which did ot have a accurate zip code-rurality crosswalk.
eappedix Table 3. Descriptive Characteristics of Ambulatory Care Practices Defied as Uder-users of EHR-Based HIT Fuctioalities Uder user (%) p Total (%) Size (Number of associated physicias) 11706 (38.9) 30123 Media, mea 2, 4.3 < 0.0001 2, 5.6 < 7 9465 (80.9) 23324 (77.4) 7-19 1142 (9.6) 3415 (11.3) < 0.0001 20-99 365 (3.1) 1222 (4.1) >100 734 (6.3) 2162 (7.2) Practice Type Locatio Sigle or multiple specialty, allied health Primary 3534 (30.2) 9289 (30.8) 7267 (62.1) < 0.0001 18823 (62.5) Specialist services ad urget care 905 (7.7) 2011 (6.7) Small tow Rural 640 (5.5) 1402 (4.7) Mid-size 1041 1844 (8.9) (15.8) <0.0001 2234 3933 (7.4) (13.1) Metropolita 8179 (69.9) 22518 (74.8) Regio Northeast 2556 (21.8) 6141 (20.4) Midwest 3042 (26.0) <0.0001 9756 (32.4) South 4089 (34.9) 9767 (32.4) West 2019 (17.3) 4459 (14.8) P values calculated with Pearso s c 2 for categorical variables, they estimate the statistical sigificace of differeces i proportios betwee categories of practice variables i uderuse practices compared to the total sample. Two-sided t test was performed to test sigificace of differece i mea umber of associated physicias. The aalysis for the locatio excludes 36 practices which did ot have a accurate zip code-rurality crosswalk.