Healthcare. White Paper. Importance of Data, Interoperability, and Population Health Management in Medicare ACO

Transcription

1 Healthcare White Paper Importace of Data, Iteroperability, ad Populatio Health Maagemet i Medicare ACO

2 About the Author Jayathi Subramaia Jayathi Subramaia, a licesed ad registered cliicia, is a Subject Matter Expert ad healthcare cosultat for Tata Cosultacy Services (TCS). She has bee actively egaged i the field of healthcare i the US for more tha 20 years. Subramaia is also egaged i health iformatio techology (HIT) iitiatives, such as Health Isurace Exchage (HIX), Health Iformatio Exchage (HIE), Accoutable Care Orgaizatios (ACOs) ad Meaigful Use (MU) of US HealthCare Reform, both at a orgaizatioal level ad at various workgroup levels. She is also a member of the ehealth Iitiative ad is a advisory editorial review board member for the publicatio Topics i Geriatric Rehabilitatio. Subramaia has a Doctorate i Busiess Admiistratio from the Califoria Itercotietal Uiversity with a specializatio i Healthcare Leadership ad Maagemet.

3 Abstract The Affordable Care Act is trasformig the way healthcare is coceived ad delivered i the US, creatig ew busiess practices ad models, ad itroducig sigificat idustry shifts that will affect all players i the healthcare ecosystem. While the legislatio relies o Accoutable Care Orgaizatios (ACOs) as the ew vehicle to drive improvemets i healthcare quality ad delivery, prelimiary results show that ACOs have ecoutered umerous cliical, operatioal, ad fiacial challeges, each of which has relevace to the success or failure of the ACO model. This paper examies the three mai obstacles that ACOs face aroud data, iteroperability, ad Populatio Health Maagemet ad suggests a solutio that utilizes techology to simultaeously address the aforemetioed challeges. Techology compaies should work had i had with ACOs to uderstad their curret challeges ad their future goals, ad develop systems that will address their eeds. A sigificat itegratio of health iformatio techology ito the ACO model will esure the latter's cotiued success, ad with it, the health of the populatio as well.

4 Cotets Itroductio 5 Curret Treds ad Challeges 7 Data 7 Iteroperability 9 Populatio Health Maagemet (PHM), Commuity Egagemet ad Aalytics 11 ACO iitiatives i the area of PHM 13 The Solutio lies at the itersectio of Data, Iteroperability ad PHM 14 Coclusio 15

5 Itroductio Accoutable Care Orgaizatios are icreasigly beig viewed as the vehicle through which physicias ad hospitals are held accoutable for the overall cost ad quality of healthcare. Accordig to the Istitute of Health Improvemet (IHI), the Triple Aim, or the three dimesios of optimizig health system performace are: improvig patiet experiece of care through care coordiatio ad outcomes, improvig health of populatios through proactive care maagemet ad educatio, ad reducig per capita cost of care through paymet models that pay for value rather tha volume of care (as i the traditioal fee-for-service structure). The mai objective i the establishmet of ACOs is to drive healthcare trasformatio, distict from the way healthcare has bee traditioally delivered. I 2012, the first Medicare ACOs, icludig the Pioeer ACO model ad Medicare Shared Savig Program (MSSP) model, were formed. I the past few years, the umber of ACOs has grow to over 600, accordig to the Health Affairs report (Figure 1). Figure 1: Number of ACOs by Types ¹ The yearly icrease i the umber of ACOs formed, ad the ogoig ivestmets by the Ceters for Medicare ad Medicaid Services (CMS) to refie ad advace the ACO model, suggest that the ACO model will further grow ad stregthe to become the future model of healthcare. However, prelimiary results, both o the cliical ad fiacial dimesios of the early Medicare ACOs, have bee mixed, ad demad a deeper uderstadig of some of the challeges beig faced today, i order to explore appropriate solutios. For the purposes of this white paper, discussios o ACOs will be limited to Medicare ACOs (both Pioeer ad MSSP) oly. This is maily because Medicare ACOs have a regulatory-drive structure ad fuctioal scope that iclude reportig of 33 specified quality metrics. These metrics spa four quality domais: patiet experiece, care coordiatio, patiet safety ad prevetive health, ad at-risk populatio, ad ultimately determie ACO sharig of defied oe sided savigs oly or two sided savigs or losses with CMSs. I this paper, the words Medicare beeficiaries ad patiets are used iterchageably. ACO Fiacial Results Aalysis I Jauary 2014, CMS released Performace Year 1 iterim fiacial recociliatio results for Medicare ACOs that started i April ad July The data reveals that 54 of the iitial 114 ACOs icluded i the aalysis had lower- [1] ACO Results: What We Kow So Far, by Matthew Peterse & David Muhlestei, Leavitt Parters, published 30 May 2014, accessed September 2014, 5

6 tha-projected expeditures i their first year. I additio, oly 29 of those ACOs saved eough to geerate bous paymets. It is importat to ote that 21 of the 29 ACOs that geerated bous paymets were physicia-led versus hospital-led ACOs. Figures 2 ad 3 preset the fiacial results for the Pioeer ad MSSP models of ACOs. Figure 2: Pioeer Fiacial Breakdow ² Figure 3: MSSP Fiacial Breakdow³ [2] ACO Results: What We Kow So Far, by Matthew Peterse & David Muhlestei, Leavitt Parters, published 30 May 2014, accessed September 2014, [3] ACO Results: What We Kow So Far, by Matthew Peterse & David Muhlestei, Leavitt Parters, published 30 May 2014, accessed September 2014, 6

7 Curret Treds ad Challeges Prelimiary results show that Pioeer, ad particularly MSSP ACOs, have ecoutered umerous cliical, operatioal, ad fiacial challeges, each of which has relevace to the success or failure of the ACO model. Oe of the major barriers to success is the heavy upfrot ivestmet required by ACOs i Health Iformatio Techology (HIT) elemets such as Electroic Health Records (EHR), cetral data repositories, likages to Health Iformatio Exchages (HIE), cliical decisio support systems (CDSS) ad aalytical tools. I additio, the curret ACO paymet models ad icetives push ACOs to use HIT maily to comply with regulatory requiremets, ad do ot ecourage them to use HIT as a more productive ad iovative patiet-cetered tool. This has resulted i 100% compliace i regulatory reportig of the required metrics but has made little differece i positively affectig health outcomes or beeficiary experiece. Likig the paymet to actual beeficiary experiece ad outcomes will perhaps persuade ACOs to view HIT iitiatives more positively, ad use these iovative digital techologies to miimize their curret challeges. Most of the challeges that impact a beeficiary s prevetive health, outcomes, ad experiece (ad subsequetly the health of the populatio) revolve aroud the followig three areas: Data Iteroperability Populatio Health Maagemet Data Data is icreasigly importat for the very survival of a ACO, as it is eeded to drive care coordiatio ad outcomes, ad maage costs, - the primary objectives of a ACO. Data ca be broadly classified as structured, semistructured, ad ustructured⁴. Accordig to Healthcare Iformatio ad Maagemet Systems Society (HIMSS)⁵, about 60% of the data preset i cliical documets is i a ustructured format, examples of which are foud i practice maagemet systems, cliical otes, radiology reports ad images ad others. Structured data such as laboratory, claims, ad billig iformatio is curretly leveraged by disparate systems withi the orgaizatio to aalyze, produce reports, ad maage cliical ad fiacial operatios. Data that exists i disjoited systems creates challeges i leveragig the iformatio to obtai a logitudial view of the patiet, required to aalyze costs, outcomes, ad quality. The existece of largely discoected legacy data systems, ad poor data defiitios ad data goverace lead to poor data quality. Some examples of icosistet data quality iclude variatios i the stadardized abbreviatios i cliical documetatio: CHD ca refer to cogeital heart disease or cogestive heart disease or coroary heart disease. This ca be problematic, especially for a ACO that has beeficiaries i all of the said risk groups. Other examples of iaccurate data iclude eterig data i wrog fields i the EHR (leadig to challeges with aalysis ad actioable use), eterig icorrect values or trasposig umbers for vital sigs (weight ad height or age umbers trasposed), or eterig structured data elemets i the free text areas withi the EHR. Although the [4] Ustructured Data i Electroic Health Record (EHR) Systems: Challeges ad Solutios by Datamark Ic. accessed September 2014, [5] HIMMS Health Story Project by HIMMS, accessed September 2014, 7

8 push towards Meaigful Use of EHRs (EHR-MU) has improved data quality to some extet, there cotiue to be challeges i the completio of data etry, which i tur pose a sigificat hidrace to better care ad cost reductio. It is ot just iteral data that is importat. To esure high caliber aalytical capabilities that provide actioable isights about the health of the populatio, a ACO requires a combiatio of claims data, cliical ad EHR data, as well as data from public registries. The structured claims data cotais iformatio about disease classificatio, procedure codes, duratio ad frequecy of illess, ad other demographics. The EHR provides rich ustructured data that cotributes sigificat value to the logitudial cliical maagemet of the beeficiary. Combiig the EHR ad claims data with data from public registries that provide treds by various parameters ca help cater to the beeficiary s curret ad past disease state. It ca also trasform the future disease state of the beeficiary, thus eablig the shift from disease maagemet to populatio health maagemet. To esure that the data i the electroic etworked cliical maagemet eviromet of a ACO is accurate, cosistet, ad timely, it is imperative that data maagemet is ot etrusted to just a few HIT or Health Iformatio Maagemet (HIM) persoel withi a orgaizatio. Istead, it should be the resposibility of each ad every idividual professioal who has access to cliical, operatioal, admiistrative, ad fiacial record keepig withi the orgaizatio. Ayoe who eters data should be traied to follow data policies i place correctly. Accordig to the America Health Iformatio Maagemet Associatio (AHIMA), the followig best practices should be followed for superior data quality i the healthcare eviromet:⁶ Providig role-based access to data, which meas pre-assigig authority for persoel access to create, read, update, ad delete data, based o role, locatio of idividual providers, ad as madated by HIPAA criteria. Such access rules must be defied, eforced, ad built ito system security fuctioalities. Clear policies o what iformatio access is eeded by a specific role or relatioship to patiet types must be developed. For example, access to psychiatric patiet data should be provided oly to those care providers who work i the psychiatric cliic ad ot to all care providers i the orgaizatio. Creatig a data dictioary for each iformatio system, with stadard data field defiitios for each data elemet. These defiitios should be clearly commuicated to all staff accessig the record especially those resposible for reportig EHR data. I additio, there should be periodic validatio of access. The data dictioary should also be built ito system fuctioalities to esure adherece across all levels. For example, there should be a stadardized abbreviatio data dictioary that is uiform across the multiple EHRs withi a ACO. For all other systems that feed ito the EHR, clear policies, stadards, procedures, ad fuctioalities should be established to defie who ows ad has resposibility for maitaiig ad creatig the data dictioary for each system ad module. Developig a stadardized format for cosistet data quality, such as the use of the Systematized Nomeclature of Medicie - Cliical Terms (SNOMED CT) to record curret, active, ad past diagoses. Additioally, usig stadardized templates ad olie forms that miimize free text ad copy-paste fuctios ca add to the accuracy ad cosistecy of data quality. [6] AHIMA Practice Brief Assessig ad Improvig EHR Data Quality (Updated) published March 2013, accessed September 2014, Copyright 2014 by the America Health Iformatio Maagemet Associatio. All rights reserved. No part of this publicatio may be reproduced, stored i a retrieval system, or trasmitted i ay form or by ay meas, electroic, photocopyig, recordig or otherwise without prior permissio from the publisher. 8

9 Usig structured data to eable the sharig ad exchage of health iformatio withi a ACO, with other providers, with HIEs ad with other orgaizatios. For example, eterig vital sigs as discrete data ito correctly formatted fields versus allowig free text etry of the vital sigs ito the system ca esure cosistecy i vital sigs data fields, thus eablig data gatherig ad aalysis of Basal Metabolic Rate (BMI). Such structured etry will miimize loss of iformatio due to formattig errors ad also miimize risk of misiterpretatio. State ad federal laws ad regulatios, accreditatio stadards, medical staff bylaws, rules, ad regulatios, ad orgaizatioal policies ad procedures should mirror stadardizatio decisios ad be followed by the desigated staff. The Joit Commissio s Iformatio Maagemet ad Record of Care stadards, Health Isurace Portability ad Accoutability Act (HIPAA) stadards, CMS Coditios of Participatio, ad Federal Rules of Civil Procedure related to electroic discovery are just a few of the stadards that should be kept i mid whe developig data stadards ad goverace processes. Followig data itegrity policies ad procedures withi a ACO, icludig (but ot limited to) registratio processes, stadards for hadlig duplicate records, ad processes for addressig overlays. There should be idividuals dedicated to the cotiuous auditig ad EHR correctio processes, who moitor the system proactively ad correct errors as they are idetified. These idividuals ca play a importat role i fie-tuig processes ad esurig the overall quality of the data. Iteroperability Greater access to perso-level health iformatio is ecessary to improve the quality, efficiecy, ad safety of health care delivery, ad this ca be doe through quick ad efficiet sharig of accurate data. Such data sharig is depedet o the iteroperability of the various systems both withi ad outside the ACO. To be successful, a ACO must esure cliical iteroperability first withi the ACO ad secodly, with exteral orgaizatios icludig laboratories, radiology ceters, o-aco cliics ad hospitals, pharmacies ad HIEs. I additio, beyod cliical iteroperability, it is ecessary to develop admiistrative iteroperability that covers paymet processes, clearig house ad other fiacial systems. However, curretly either providers or their HIT vedors have a busiess imperative or a HIE-targeted fiacial icetive to electroically share extesive perso-level health iformatio across providers. For example, i 2011, 40% of hospitals set laboratory ad radiology data electroically to providers outside their orgaizatio but oly 25% of these hospitals could exchage medicatio lists ad cliical summaries with outside providers. Also, Log Term ad Post Acute Care (LTPAC) ad Behavioral Health (BH) uits are ot eligible for Meaigful Use (MU) icetive paymets uder Medicare ad Medicaid EHR Icetive Programs, ad hece are ot obliged to exchage health iformatio. This reduces the motivatio amog the LTPAC commuity to adopt ay level of EHR (oly 6% of LTPAC, 4% of rehabilitatio hospitals, ad 2% of BH uits have basic EHR). This is a Beeficiary Attributio CMS characterize[s] the process of beeficiary attributio as a aligmet of beeficiaries with a ACO, based o a beeficiary s utilizatio of primary care services. Medicare examies all the claims for services of ay patiet by the ACO, ad determies from which physicia the patiet got most of his/her primary care services. It the attributes the patiet to that physicia. A key poit to remember is that eve though a Medicare beeficiary is attributed to a specific primary care physicia, the patiet ca see ay physicia he/she wats. 9

10 sigificat gap ad a esuig challege for Medicare ACOs, as 40% of Medicare beeficiaries are discharged to LTPAC ad rehabilitatio hospitals. The curret beeficiary attributio followed i a Medicare ACO ad the limited iteroperability betwee providers, vedors, suppliers, ad health isurers - both withi ad outside a ACO - prevets the beeficiary from kowig whether he/she belogs to a ACO. This leaves the beeficiary with little reaso to be loyal to the same provider(s) withi a ACO etwork ad does ot ecourage the patiet to actively egage i his/her ow care. I fact, beeficiaries ofte seek care outside the ACO, causig gaps i the beeficiary s logitudial care iformatio due to lack of iteroperability as well as leadig to populatio chur withi a ACO. This egatively impacts the coordiatio, cost, quality, ad outcome of care. Full iteroperability is hampered due to the followig reasos: Haphazard adoptio of EHR: To reap the beefits of iteroperability, all etities i the ACO ecosystem must adopt EHR across the board. To promote EHR adoptio, the Health Iformatio Techology for Ecoomic ad Cliical Health (HITECH) Act of 2009 authorized icetive paymets to cliicias ad hospitals through Medicare ad Medicaid for usig EHRs to achieve specified improvemets i patiet care. However, accordig to the Natioal Health Statistics Report (NHSR) released i May 2014, oly 71.8% of office-based physicias were usig some form of basic or fully fuctioal EHR by 2012 (see Figure 4). Of these, 39.6% were usig oly basic EHR with very limited fuctioality. Figure 4: Office Based Physicias with a EHR ( )⁷ [7] Natioal Health Statistics reports: Treds i Electroic Health Record System Use Amog Office- Based Physicias: Uited States , by Chu-Ju Hsiao, Esther Hig & Jill Ashma, published 20 May 2014, accessed July 2014, 10

11 I additio, a ACO has to ivest cosiderably i fiacial ad huma capital to be somewhat iteroperable, operatioal, ad successful. It is estimated that the upfrot ifrastructure ad techology ivestmet i healthcare is, o average, $2 millio per ACO. This icludes EHR implemetatio, compliace of reportig tools for ACOs, creatig data iteroperability, HIE subscriptios ad coectivity, ad other itegratio activities. There is a additioal huma capital ivestmet to address the care coordiatio factors i terms of larger umber of urses, care coordiators, hospitalists, techology experts ad cosultats, aalytics, EHR, ad other product ad services vedors. These cotribute ot oly to the fiacial pressure but also icreased axiety amog the ACO participats ad providers of the busiess ad udermie the viability of the fiacial model. Lack of a sigle stadard Curretly, the existece of variable stadards hiders the ACOs ability to exchage ad use data with exteral agecies. There is also a sigificat cost to developig iterfaces with each exteral laboratory. For istace, there is o regulatory requiremet for laboratories to sed results usig Logical Observatio Idetifiers Names ad Codes (LOINC). This has allowed ACOs to use preferred laboratories, which i tur limits the exchage ad use of lab iformatio whe beeficiaries use o-preferred laboratories. Oe of the core requiremets of MU is the patiet s ability to electroically view, dowload, ad trasmit (VDT) their health iformatio. Hece iteroperability is imperative for HIE-eablemet. Thus, we expect to see some improvemets i iteroperability ad HIE capability for ACO etities i the future. The Veteras Admiistratio s (VA) Blue Butto Plus iitiative icreases VA patiet egagemet by eablig them to access registries for immuizatio ad other activities. Likewise, the Medicare ACO regulatory divisio should require Medicare ACOs to develop a method similar to the Blue Butto Plus to facilitate egagemet of beeficiaries i their care. Populatio Health Maagemet, Commuity Egagemet ad Aalytics Every four years, the Health ad Huma Services (HHS) updates its strategic pla. Figure 5 below depicts the CMS strategy that aligs with the HHS pla for A key objective of the CMS Strategy is to improve the health of the populatio through proactive care maagemet ad educatio both at a prevetive level ad for the chroically ill, reducig the eed for emergecy departmet visits, hospitalizatios, expesive itervetios such as surgery, imagig tests, ad procedures. The success of a populatio health maagemet (PHM) program is depedet o may factors excellet care coordiatio, HIT that goes beyod EHR implemetatio, ad a proactive medical care model. I fact, rather tha limit itself to retrospective disease maagemet, a care model should prospectively look at disease treds, costs, ad also icorporate data such as social eviromet (icome, educatio, employmet ad social support), physical eviromet (urba desig, clea air ad water), geetics, ad idividual behavior from commuity resources icludig public disease registries. To implemet PHM, ACOs must also develop orgaized care delivery processes, ad use established multidiscipliary teams ad care coordiatio protocols. I additio, they should utilize multi-modal patiet egagemet models (for istace, s, text, face to face) ad other remote access models such as telehealth. Importatly, for successful PHM executio, ACO leadership should demostrate cotiued commitmet to chage, workflow ad process redesig, ad the HIT implemetatio. 11

12 Figure 5: CMS Strategy Road Forward ⁸ I the area of PHM, oe major cocer for Medicare ACOs is the maagemet of chroically ill Medicare beeficiaries. Much of the Medicare populatio suffers from multiple chroic coditios, such as hypertesio, high cholesterol, arthritis, ischemic heart disease or diabetes, with aroud 14% havig more tha 6 chroic coditios as oted i figure 6 below: Figure 6: Prevalece of Multiple Chroic Coditios for Medicare Beeficiaries⁹ The chroically ill, especially those with multiple chroic coditios, put a lot of stress o the Medicare costs, as show i figure 7 below: [8] CMS Strategy: The Road Forward , published 2013, accessed July 2014, Strategy/Dowloads/CMS-Strategy.pdf [9] Chroic Coditios Chart 2012, published 2012, accessed July 2014, Reports/Chroic-Coditios/Dowloads/2012Chartbook.pdf 12

13 Figure 7: Percet of Beeficiaries with Multiple Coditios ad Medicare Spedig10 All of the above show that dyamic access to iformatio that is better tha curretly available EHR data ad claims reports is ecessary to maage the high cost of servig the chroically ill Medicare beeficiary populatio. A EHR shows alerts ad iformatio about a beeficiary oly whe it is opeed for that particular patiet. But if other alert ad otificatios tools are plugged ito the EHR, it ca automatically alert the beeficiary about a upcomig physicia visit, or provide otificatios so that urses ad care coordiators ca call the beeficiary about his/her care before his/her health takes a tur for the worse. Providig alerts ad otificatios requires the implemetatio of HIT capabilities to track ad moitor the health of the idividual beeficiaries, which i tur requires the ability to stratify populatios ito various segmets accordig to risk, diagoses, demographics, health risk assessmet, or other factors such as overdue tests. ACO iitiatives i the area of PHM May ACOs are explorig optios to assess, develop, ad purchase stratificatio tools for effective PHM implemetatio. Stratificatio eables better care coordiatio, provisio of proactive itervetios, ad eablemet of the educatio ad egagemet of patiets uder care before the disease worses or the beeficiary is hospitalized or readmitted. This also reduces costs ad improves outcomes. ACOs should develop predictive algorithms that ca help forecast the likelihood ad umber of beeficiaries who may icur the most cost per give period i the future for example, most likely to be sick i the ext six moths. Combiig risk stratificatio tools, predictive models, ad reportig ad aalytics should be part of the digital techology arseal of a ACO. The Agecy for Healthcare Research ad Quality (AHRQ) recommeds categories of health IT tools for the stratificatio ad moitorig of populatios that will: Target patiets i greatest eed of services by arrowig sub-populatios, Geerate alerts for patiets to seek appoitmets with their providers, ad Eable providers to leverage the alerts (actioable data) to address patiet care eeds. Itegratig data ad iformatio from public registry data poits such as the Ceter for Disease Cotrol, commuity data poits like [10] Chroic Coditios Chart 2012, published 2012, accessed July 2014, Reports/Chroic-Coditios/Dowloads/2012Chartbook.pdf 13

14 educatio, trasportatio, social support, ad other iformatio from social security admiistratio, ca erich the risk stratificatio ad predictive models for Medicare ACOs. This will i tur create effective itervetio plas ad outcomes for Medicare beeficiaries. The Solutio: Itersectio of Data, Iteroperability, ad PHM As previously discussed, there are several sources of patiet-cetered data withi a ACO that require iteroperability to itegrate ad aggregate for actioable isights ad treatmet plaig. I additio, curret EHR systems have limited capability to itegrate with the multiple cliical data sources or with other systems such as billig, schedulig, ad admiistratio to provide a sigle view of the patiet s logitudial care record. Coectivity to commuity iformatio like public registries is ecessary to kow about care durig the lifespa of the beeficiary. Data itegrity ad goverace, data likig from various sources, ad iteroperability are critical factors for maagig populatio health i a ACO. All of the above factors suggest that the cotiuig success of the ACO model will deped o techology solutios that ca simultaeously address the challeges of data, iteroperability, ad PHM faced by ACOs today. Realizig this, the Istitute of Health Improvemet (IHI) reviewed 87 leadig health techologies to that ca help ACOs meet the Triple Aim objectives. Of the 87 techologies, 55% are focused o PHM, 29% of techologies are focused o cost reductio, ad oly 14% are focused o improvig patiet experiece. Four remote moitorig techologies were icluded i the PHM cout. Oly 2% out of the 87 health techologies met all 3 Triple Aim Compoets as show i the figure 8 below: Figure 8: Number of Techologies by Triple Aim Compoets 11 I additio, most of the ivestmet was i hospital ad/or cliic admiistratio techologies ad very little ivestmet was i techologies for PHM, aalytics, or iteroperability as show i the figure 9 below. [11] A Framework for Selectig Digital Health Techology, by Ostrovsky A, Dee N, Simo A, Mate K, Istitute of Health Improvemet, published Jue 2014, accessed July 2014, file:///c:/users/534447/dowloads/ihiiovatioreport_digitalhealthselectioframework_exteralju14.pdf 14

15 Clearly, there is immese opportuity o the techology frot i the areas of data goverace, data warehousig, iteroperability, PHM risk stratificatio, ad predictive models ad aalytics reportig. What is eeded is for techology compaies to work had i had with ACOs to uderstad their curret challeges ad future goals, ad to develop systems that will address their eeds. Coclusio Figure 9: Number of Techologies by Type 12 Data, iteroperability ad populatio health maagemet are emergig as extremely importat areas of exploratio, growth ad developmet i the ACO ecosystem. The may challeges with data ad iteroperability, high HIT costs, ad limited kowledge of the various HIT tools ad services required are both a area of educatio ad opportuity for HIT ad HIM professioals. It is also very importat for Medicare ACOs to establish their short ad log term busiess objectives, regulatory requiremets, ad curret techology ladscape both withi ad outside the ACO (HIE eeds, EHR itegratio ad other eeds), prior to peig their future techology ladscape. We believe that techology is the aswer to may of the challeges described i this paper. ACOs should tie up with a strog techological parter that ca help them address these challeges as oly the sigificat itegratio of health iformatio techology ito the ACO model will esure the latter s cotiued success, ad with it, the health of the populatio as well. [12] A Framework for Selectig Digital Health Techology, by Ostrovsky A, Dee N, Simo A, Mate K, Istitute of Health Improvemet, published Jue 2014, accessed July 2014, file:///c:/users/534447/dowloads/ihiiovatioreport_digitalhealthselectioframework_exteralju14.pdf 15

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