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The American Journal of Managed Care January 2019
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Eli G. Phillips Jr, PharmD, JD; Chadi Nabhan, MD, MBA; and Bruce A. Feinberg, DO
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Mind the Gap: The Potential of Alternative Health Information Exchange
Jordan Everson, PhD; and Dori A. Cross, PhD
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Sung J. Choi, PhD; and M. Eric Johnson, PhD
Organizational Influences on Healthcare System Adoption and Use of Advanced Health Information Technology Capabilities
Paul T. Norton, MPH, MBA; Hector P. Rodriguez, PhD, MPH; Stephen M. Shortell, PhD, MPH, MBA; and Valerie A. Lewis, PhD, MA
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Sunny C. Lin, MS; John M. Hollingsworth, MD, MS; and Julia Adler-Milstein, PhD
Drivers of Health Information Exchange Use During Postacute Care Transitions
Dori A. Cross, PhD; Jeffrey S. McCullough, PhD; and Julia Adler-Milstein, PhD

Mind the Gap: The Potential of Alternative Health Information Exchange

Jordan Everson, PhD; and Dori A. Cross, PhD
Proprietary health information exchanges (HIEs) offer significant but uneven opportunities to advance provider connectivity. Open forms of HIE remain critical for comprehensive coverage of patient transitions.
Dependent Variables: Potential HIE Connectivity

We calculated the number of transitions between physicians using a 2-step process. First, we collapsed the data so that each observation represents the total volume of patients shared between a pair of physicians (ie, is undirected), then attributed half of the total volume to each of the 2 physicians in that dyad. Second, we summed the attributed number of transitions for each physician across all pairs in the data set in which that physician is listed to calculate the total transitions per physician.

We then created 4 continuous dependent variables that capture the proportion of patients shared by each physician who can be covered by any of 4 potential connectivity options. The first 3, which are not mutually exclusive, include (1) patients shared with physicians who belonged to the same horizontally (ie, multiphysician group practice) or vertically (ie, hospital–physician integrated system) integrated provider system (potential “enterprise HIE”), (2) patients shared with physicians who used the same EHR vendor (potential “EHR vendor–mediated HIE”), and/or (3) patients shared with physicians who used an EHR vendor that participated in an alliance (potential “vendor alliance HIE”). We identified EHR vendors participating in vendor alliances by visiting each alliance’s website, where participating organizations are listed.18,19 We then created a fourth continuous outcome, “open HIE needed,” which included patients for whom none of the other forms of HIE could cover their transition. We also coded a fifth scenario in which HIE was not possible because 1 or both physicians had no EHR.

Independent Variables

Physician characteristics. We measured 4 categorical variables from the SK&A data that were likely associated with patient referral patterns and, ultimately, the value of enterprise HIE or other vendor-based proprietary solutions. These variables included (1) physician specialty (primary care, ancillary [eg, radiologists and pathologists], specialist, surgeon, and other); (2) physician practice size (solo, 2-5, 6-9, 10-19, 20-49, ≥50); (3) size of the multipractice physician group or hospital–physician integrated system to which a provider belongs, which we defined by first identifying independent physicians and then dividing systems into 5 equally sized categorical quantiles by physician membership (very small system [1-20 physicians], small [21-140], medium [141-427], large [433-973], and very large [987-8743]); and (4) EHR vendor (designation of specific vendor if the vendor was 1 of the 7 with the highest market share, as well as binary indicators for any other vendor indicated or no vendor indicated).

Transition characteristics. Using the patient transition data, we created 3 categorical measures to capture the structure of the physician’s network, which may relate to the value of each HIE approach. We first measured the number of other physicians with whom they transitioned patients (divided into categories by tertile: low [1-29 physicians], medium [30-107], and high [108-11,793]), as well as the percentage of their transitions that occurred with their 10 most frequent transition partners (also divided into categories by tertile: low [2.5%-28.3167% of transitions], medium [28.3169%-58.1839%], and high [58.1843%-100%]). Finally, we created a continuous variable representing the proportion of patient transitions each physician had with physicians of different specialty types (primary care, specialist, surgeon, and ancillary).

Analytic Plan

We initially calculated descriptive statistics of included physicians. Then, to address our first research question regarding opportunity for network coverage by proprietary forms of HIE, we created an Euler diagram of the percentage of all transitions potentially covered by each form of HIE. Euler diagrams allow for the visual representation of the proportion of patient transitions that could uniquely be covered by each HIE method and capture the extent of overlap (eg, redundancy in coverage of patient transitions by HIE methods). We also examined the differential potential coverage and overlap from these HIE approaches based on key organizational characteristics. We generated separate Euler diagrams for 4 subgroups of physicians: (1) those in an integrated care system with 1 of the 7 most common EHRs (by market share), (2) those in an integrated system with an EHR not in the top 7, (3) those in an independent practice with an EHR in the top 7, and (4) those in an independent practice with an EHR not in the top 7. We selected these 2 distinguishing characteristics because they most directly relate to the availability of proprietary HIE.

To address the second research question about differential coverage by specialty type, we conducted bivariate analyses to determine whether each HIE approach varies in its potential to meet the needs of different types of providers. To do this, we first calculated the number of transitions involving each type of provider (primary care, specialist, surgeon, ancillary provider) as a percentage of total transitions in the data set. For each provider type, we then compared whether the percentage of transitions containing that provider type differed across the 5 possible scenarios and tested the statistical significance of these differences using χ2 tests.

We used a multivariate ordinary least squares regression model with standard errors clustered by practice to address our final research question regarding which physicians have the highest need for open HIE. To do so, we estimated the proportion of each physician’s patient transitions for which open HIE would still be needed even if the proprietary forms of HIE potentially available to that provider (based on organizational and EHR characteristics) were in use. Covariates included physician specialty, the proportion of patient transitions each physician had with different physician specialties, size of integrated organization, practice size, vendor, number of exchange partners, and concentration of exchange partners. All statistical analyses were performed in Stata MP 15 (Stata Corp; College Station, Texas).


 
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