Richard Brand, PhD

 Continuity of patient information across settings can improve transitions after hospital discharge, but outpatient clinicians often have limited access to complete information from recent hospitalizations. We examined whether providers’ timely access to clinical information through shared inpatient-outpatient electronic health records (EHRs) was associated with follow-up visits, return emergency department (ED) visits, or readmissions after hospital discharge in patients with diabetes.

 As an integrated delivery system staggered implementation of a shared inpatient-outpatient EHR, we studied 241,510 hospital discharges in patients with diabetes (2005-2011), examining rates of outpatient follow-up office visits, telemedicine (phone visits and asynchronous secure messages), laboratory tests, and return ED visits or readmissions (as adverse events). We used multivariate logistic regression adjusting for time trends, patient characteristics, and medical center and accounting for patient clustering to calculate adjusted follow-up rates.

 For patients with diabetes, provider use of a shared inpatient-outpatient EHR was associated with a statistically significant shift toward follow-up delivered through a combination of telemedicine and outpatient laboratory tests, without a traditional in-person visit (from 22.9% with an outpatient-only EHR to 27.0% with a shared inpatient-outpatient EHR; 

 < .05). We found no statistically significant differences in 30-day return ED visits (odds ratio, 1.02; 95% CI, 0.96-1.09) or readmissions (odds ratio, 0.98; 95% CI, 0.91-1.06) with the shared EHR compared with the outpatient-only EHR.

 Real-time clinical information availability during transitions between health care settings, along with robust telemedicine access, may shift the method of care delivery without adversely affecting patient health outcomes. Efforts to expand interoperability and information exchange may support follow-up care efficiency.

Continuity in access to patient information across providers in different settings can improve patient transitions. We examined whether timely access to complete clinical information through a shared inpatient-outpatient electronic health record (EHR) was associated with changes in rates of in-person follow-up visits after hospital discharge in patients with diabetes.

Using the shared inpatient-outpatient EHR was associated with a shift toward 7-day follow-up delivered through a combination of telemedicine (telephone and patient-physician secure messages) and outpatient laboratory tests, without a traditional office visit.

Patient outcomes, as measured by 30-day return emergency department visits or hospital readmission, were comparable when using the shared EHR.

Timely availability of clinical information during health care transitions, in a setting with robust telemedicine access, can shift the method of care delivery without adversely affecting patient health outcomes.

For the growing number of patients with chronic conditions, care transitions, such as those after hospital discharge, require coordination among multiple clinicians practicing in different settings.

 Health information exchange (HIE) that ensures that patient health information follows each patient as they move between care settings is important to providing well-coordinated care.

 Still, developing widespread electronic health record (EHR) interoperability and HIE between settings remains challenging.

 Emerging health care payment models, coordination, and consolidation efforts are likely increasing information sharing among providers at follow-up visits.

 Still, there is limited evidence on the impact of sharing electronic patient information across settings on patient care transitions and outcomes, particularly on follow-up care and adverse events after a hospitalization.

With limited electronic data sharing, deficiencies in information availability and communication among providers after hospital discharge are regularly reported by both clinicians and patients—including inconsistent awareness of the hospitalization itself and limited communication of medications, orders, and laboratory results, leading to medical errors and increases in readmission.

 In the absence of broad clinical guidelines for optimal postdischarge follow-up interval or encounter type, differences in follow-up visit rates have been associated with mixed outcomes.

 Shared seamless access to EHRs can offer clinicians direct access to fully view and document detailed patient clinical information across health care settings without requiring shifts across systems, modalities (like paper), or capabilities (like limited snapshots or fragmented documentation).

 The timely exchange of patient information and communication is defined for this paper as data access to support clinical care at any time it is clinically needed, ideally in real time. Timely and seamless EHR access across providers can support patient transitions from hospital discharge to outpatient follow-up care, such as by facilitating coordination, risk stratification, and flexibility for telemedicine encounter types, with potential implications for clinical care quality and efficiency.

 Early evidence from other settings and patient populations about impacts of HIE is mixed; however, findings of some studies have suggested that electronic health information availability and exchange are associated with reductions in repeat tests or imaging, supporting the feasibility of telemedicine follow-up.

Building on previous findings of improved diabetes care and health outcomes after implementation of an outpatient-only EHR, we studied the subsequent implementation of a shared inpatient EHR that seamlessly integrates patient health information between hospital and outpatient providers.

 In patients with diabetes, this study examined the association between provider use of the shared inpatient-outpatient EHR and rates and types of outpatient follow-up, including office visits, telemedicine encounters (including asynchronous secure patient-physician messages and real-time telephone visits), and laboratory tests. We also examined the impact on adverse clinical events after hospital discharge, measured by emergency department (ED) visits and hospital readmissions.

This quasi-experimental study examined a historical natural experiment in which an integrated delivery system staggered implementation of an inpatient EHR, integrating it with an existing outpatient EHR, across 17 hospitalist-staffed hospitals (2006-2010). This created a stepped-wedge study design to study the impact of shared inpatient-outpatient EHR use (

). The implementation order was not associated with medical centers’ health care quality or readmission rates.

Prior to the study period, the outpatient EHR used by clinicians had already integrated all outpatient records, including primary and specialty care, laboratories, and pharmacy, but did not include complete clinical information from hospitalizations or ED visits. At baseline, providers also already had the capability to conduct scheduled real-time telephone telemedicine visits with patients and had access to a patient portal website that offered patient-provider asynchronous secure messaging tools. As such, our study examines a shift in provider-to-provider access to patient data across settings after implementation of the shared inpatient-outpatient EHR.

The commercially available complete hospital EHR replaced the inpatient paper medical record and a patchwork of optional health information technology tools, creating a shared inpatient-outpatient EHR. Before implementing the inpatient EHR, HIE between inpatient and outpatient providers was not consistent and was provider dependent. After the inpatient EHR was implemented, both inpatient and outpatient EHRs were seamlessly integrated. The hospital EHR automatically integrates patient history and clinical information (eg, clinical notes, procedures, imaging, laboratory tests, medications) with the outpatient EHR used by primary care providers and specialists, including order-entry capability for outpatient follow-up, medication reconciliation before discharge, secure messaging between providers and with patients, and notifications to outpatient providers that their patient had been hospitalized.

Between 2005 and 2011, we identified all hospitalizations among patients with diabetes (using the clinical diabetes registry) in medical centers where the outpatient EHR was already in use. We examined patients with diabetes as an example of the impact of shared EHR use in a common chronic condition that has associated complications. We excluded from our study analytic data set patients who were not health plan members in the year prior to the hospitalization and for 30 days after discharge in order to ensure complete capture of comorbidities and follow-up outcomes. We also excluded hospitalizations of patients who were not discharged home in order to ensure eligibility for outpatient follow-up.

Our data captured all outpatient office visits, telephone visits, secure electronic messages, and laboratory tests after hospital discharge. In the absence of broad evidence-based guidelines for any optimal postdischarge follow-up timing, we examined the encounters within 7 and 30 days after hospital discharge.

 We also examined any 30-day clinical events as a measure of adverse health events or outcomes, including ED visits or readmissions, based on the 30-day Medicare readmission measures.

Among hospitalizations with follow-up, we examined the rates of follow-up through a combination of only telemedicine and laboratory tests.

We used multivariate logistic regression models for each type of follow-up encounter outcome with EHR status as the main predictor and adjustment for patient characteristics (age, gender, neighborhood socioeconomic status [SES], race/ethnicity, co-payment, comorbidity score), seasonality (calendar month, categorical variable), time trend (year 1 for 2005, year 2 for 2006, and so on, as a continuous variable), quadratic term of the time trend, comorbidity score (centered), interaction term of comorbidity score and time trend, medical center, and medical center–specific time trend (interaction term of medical center with time trend and quadratic term of time trend), with standard errors corrected for patient clustering (using Stata SE 10.1 [StataCorp LP]). We defined a patient as having a low SES neighborhood using 2000 US census block groups with 20% of residents having household incomes below the federal poverty level or 25% of residents 25 years or older having less than a high school education.

 To adjust for comorbidity, we used the prospective diagnostic cost group score, which has been adopted by CMS for Medicare risk adjustment.

We reported adjusted odds ratios from these multivariate models. For easier interpretation, we also calculated the adjusted rates of each outcome by applying the coefficients from the logistic regression to the entire cohort as if everyone were in pre–inpatient EHR and post–inpatient EHR periods, respectively, so that patients in pre–inpatient EHR and post–inpatient EHR periods shared the exact same characteristics (margins command in Stata).

In sensitivity analyses, we examined the subset of hospitalizations categorized as nonelective and findings were comparable (see 

Among all 241,510 hospitalizations in 104,126 patients with diabetes from 2005 to 2011, 91,331 occurred before the shared inpatient EHR was implemented and 150,179 occurred after. 

After multivariate adjustment, the rate of any type of ambulatory follow-up encounter within 7 days of hospital discharge decreased from 72.8% to 69.2% after use of the shared EHR (

) and more modestly within 30 days after discharge, from 94.8% to 94.1% after use of the shared EHR (

 < .05). These patterns in care changes associated with shared EHR use were consistent when examining within the 30-day period after hospital discharge (eAppendix).

The type of follow-up care also changed after the shared EHR implementation. The proportion of 7-day follow-up delivered by a combination of telemedicine and laboratory tests and without an in-person office visit increased from 22.9% before use of the shared EHR to 27.0% after (

). Specifically, follow-up with an in-person office visit within 7 days dropped from 55.9% to 50.5% (

 < .05), and use of any laboratory testing within 7 days after discharge dropped from 32.0% to 30.7% (

 < .05). No statistically significant changes occurred in the rates of telephone visits or secure messages (

As a measure of adverse health events or outcomes, there were no statistically significant differences in the rates of ED visits or readmission in the 30 days after hospital discharge associated with use of the integrated hospital EHR. After the integrated EHR was in use, 16.7% of hospital discharges were followed by an ED visit (vs 16.4% before; 

 > .05) and 9.4% were followed by another hospitalization within 30 days (vs 9.5% before; 

In this natural experiment involving the staggered implementation of a commercially available inpatient-outpatient shared EHR that shares patient data across providers in inpatient and ambulatory settings, we found that after hospital discharge, patients with diabetes had a higher likelihood of receiving follow-up care delivered only by a combination of telemedicine (telephone visits and asynchronous secure messages) and laboratory monitoring without a traditional in-person visit. We found slightly lower overall rates of follow-up office visits and outpatient laboratory tests. As a measure of clinical events, we found no statistically significant association between inpatient-outpatient EHR use and rates of return ED visits or hospital readmissions. Together, these findings suggest that the timely availability of complete clinical information from using a shared inpatient-outpatient EHR after a hospital discharge in a setting with strong telemedicine access could shift the method of delivering follow-up care without adversely affecting patient health outcomes as measured by repeat ED visits or readmissions.

Although EHR interoperability and HIE functionality have been consistently promoted as policy priorities for improving the quality and efficiency of the American health care system, there is still limited research evidence to inform policy makers about the effects of continuity in provider access to patient information.

 Our study builds this evidence as one of the largest rigorously designed studies to examine the impact of shared health information access across providers during patient transitions between health care settings. Our findings from patients with diabetes also complement findings of previous studies in the same integrated delivery system, in patients with diabetes and in general patient populations, in which both providers and patients reported that EHR use facilitated care coordination both by providing informational continuity among providers and by supporting direct communication between clinicians and medical staff through electronic messaging tools.

 In a general patient population, primary care providers reported enhanced access to timely and complete patient information and better agreement on treatment goals and roles and responsibilities after the inpatient EHR was in use.

The optimal amount and timing of outpatient follow-up care after hospital discharge are not clear.

 Although some studies have found that follow-up visits or telephone calls after hospital discharge were associated with better patient outcomes, others have found no effect or worse outcomes associated with more follow-up care, and the evidence continues to be mixed.

 In the absence of broad evidence-based clinical guidelines for the optimal type or level of after-discharge follow-up, our study aimed to examine whether use of a shared inpatient-outpatient EHR was associated with shifts in the type of follow-up care delivered after hospital discharge. Because we found that follow-up care was more likely to be delivered through only telemedicine and laboratory tests, without any evidence of adverse changes in downstream health events as measured by ED visits and rehospitalizations, these changes in follow-up care represent potential improvements in the efficiency of health care delivery without adversely affecting quality. Similarly, because the rate of outpatient laboratory tests in this study was lower after using the shared inpatient-outpatient EHR without measurable impact on ED visits or hospitalizations, it appears that EHR-enabled information availability may have decreased unnecessary testing. Our findings are consistent with early evidence from other settings and across patient populations, which have found that electronic health information availability and exchange are associated with reductions in repeat tests and imaging and improve the feasibility of telemedicine follow-up.

Seamless and timely electronic HIE is still limited across the US health care delivery system. Calls for increases in exchange capabilities at hospital discharge are growing, but few settings have measured the large-scale impact of EHR-based information integration across delivery settings.

 Our study finds that movement toward more seamless health information access, even within an already integrated system, can affect the efficiency of follow-up care after hospital discharge without adversely affecting quality. These shifts may also potentially improve patient convenience through telemedicine follow-up without requiring the transportation and cost of making an in-person visit to health care providers. Although patient convenience and access are typically considered primary benefits of telemedicine, our finding of EHR-supported efficient posthospitalization follow-up through telemedicine may also be relevant as current crisis-based social distancing to protect patients and providers from infectious disease spread is rapidly shifting many medical office visits to telemedicine.

Although our study found modest average decreases in in-person visits, cumulative impacts of this magnitude may be substantial if replicated broadly across large numbers of annual hospitalizations in patient populations with chronic conditions. In addition, our finding of no statistically significant differences in clinical events can be interpreted as reassuring that negative impacts of care shifts were not detected. Further research is needed to explore patient follow-up care preferences and the clinical impacts of variations in follow-up care types in more detail.

There are several limitations to the interpretation and generalizability of our study design and setting. First, the natural experiment of shared inpatient-outpatient EHR implementation in this study is historical and may not fully generalize to current practice, which may vary across settings. Because the sharing of patient data across health care settings is still currently evolving, however, the study findings have potential relevance despite the historical study period. Further, the study relevance is supported by examining a study setting with relatively mature telemedicine and a patient portal in which patients could access a portal website to schedule a telemedicine visit to communicate with physicians and to review visit details and laboratory results. The follow-up care patterns we identified would not be feasible without the ability to follow up with patients readily by phone or through secure messages. In other settings, in-person follow-up office visits may be needed to share paper discharge summaries or to notify providers of a recent hospitalization.

 Also, the rates of follow-up care in our study at baseline were higher than previously reported in other settings, and the hospital readmission rates were somewhat lower; therefore, other settings with more limited follow-up patterns at baseline may experience different shifts with the availability of HIE across settings.

 Our study examined patterns in follow-up care after implementing a systemwide EHR system, and we are not able to characterize the clinical appropriateness of any specific follow-up care regimen. Further study of the clinical significance of changes in follow-up care encounter types is needed. This study examined only patients with diabetes, and we cannot generalize directly to other patient populations. Shared EHRs and HIE are only tools to increase information availability; their impact is dependent on the clinical workflow and delivery system context in which they are used.

Overall, in a setting that implemented a shared EHR with seamless HIE between inpatient and outpatient providers, patient follow-up care after hospital discharge was less likely to include an in-person office visit and instead was managed through exchange of asynchronous secure messages, telephone telemedicine, and outpatient laboratory tests. We found no statistically significant change in the rate of return ED visits or readmissions within 30 days after discharge, and we conclude that increased and timely information availability through the shared inpatient-outpatient EHR changed the pattern of follow-up care after discharge, enabling care delivery through non–office visit encounters without adversely affecting patient short-term outcomes.


 Kaiser Permanente Division of Research (MR, JHu, DB, RN, BF), Oakland, CA; Department of Epidemiology and Biostatistics, University of California at San Francisco (RB), San Francisco, CA; Department of Health Policy and Management, Emory University (IG), Atlanta, GA; Kaiser Permanente San Francisco Medical Center (MGJ), South San Francisco, CA; Kaiser Permanente San Rafael Medical Center (DB), San Rafael, CA; Mongan Institute for Health Policy, Massachusetts General Hospital (JHs), Boston, MA; Department of Health Care Policy, Harvard Medical School (JHs), Boston, MA.

 National Institute of Diabetes and Digestive and Kidney Diseases (R01DK085070). The study sponsor had no role in the design and conduct of the study, including the collection, analysis, and interpretation of data and the writing of the article and the decision to submit it for publication.

 Dr Hsu serves as a consultant for Cambridge Health Alliance, Columbia University, Community Servings, Delta Health Alliance, Robert Wood Johnson Foundation, and University of Southern California. The remaining 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 (MR, JHu, RB, IG, MGJ, BF, JHs); acquisition of data (MR, JHu); analysis and interpretation of data (MR, JHu, RB, IG, MGJ, DB, RN, BF, JHs); drafting of the manuscript (MR, JHu, DB); critical revision of the manuscript for important intellectual content (MR, JHu, RB, IG, MGJ, DB, RN, BF, JHs); statistical analysis (JHu, RB, RN); obtaining funding (MR); administrative, technical, or logistic support (MR); and supervision (MR).

 Mary Reed, DrPH, Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612. Email: Mary.E.Reed@kp.org.

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Articles

Using a seamlessly shared inpatient-outpatient electronic health record was associated with greater rates of postdischarge follow-up delivered through telemedicine or laboratory monitoring and without an in-person office visit.

Inpatient-Outpatient Shared Electronic Health Records: Telemedicine and Laboratory Follow-up After Hospital Discharge

 Appropriate selection of patients is key to the success of care management programs (CMPs). Hybrid patient selection approaches, in which large data assets are culled to develop a list of patients for more targeted clinical review, are increasingly common. We sought to describe the patient and practice characteristics associated with high-risk patient identification and selection for a CMP during clinical review, and to explore variation across primary care practices. 

Standardized estimates of Medicare beneficiaries identified as high risk for poor outcomes and high medical expense, and appropriate for a CMP within a large Pioneer Accountable Care Organization, were developed using mixed effects logistic models. Study subjects were 2685 Medicare beneficiaries aged over 18 (includes individuals eligible for Medicare due to a disability) aligned to 35 primary care practices in 2013.

 Independent predictors of patient identification as high risk include older age; higher risk score; recent increases in medical conditions; higher numbers of medical hospitalizations, skilled nursing facility days, and primary care physician visits; and shorter relationships with the primary care physician. Older age, and lower income, but no prior hospice use were independently associated with patient selection for a CMP among the subset of patients identified as being high risk. Adjusted predicted percents of high-risk patients varied significantly across practices overall and for 5 of the 6 patient characteristics that were independently associated with identification as high risk. 

Inconsistency in high-risk patient identification and selection for a CMP may reflect differences in practice resources, but also highlight the need for continual training and feedback in order to protect against unintentional biases.

	Clinician review within hybrid case selection approaches includes decisions about which patients are high risk and, among high-risk patients, identification of the subset of patients most appropriate for a care management program (CMP). 

		Primary care physicians were more likely to recommend high-risk patients who are poorer, older, and not using hospice services for CMP. 

		Significant variations in adjusted proportions of patients selected for care management across practices may reflect differences in practice resources, but may also introduce bias into the process.

		Continual training and feedback to practices on their review decisions may help identify missed patient opportunities.

The observation that health expenditures are concentrated within a relatively small subset of the population is well known by now, with 5% of the population accounting for 50% of medical expense in any given year. One-third of the individuals in this group—frequently those with poor health status and multiple chronic conditions&mdash;remain high-cost the following year,

 making them appropriate for primary care—based care management approaches

 However, although effective care management targeted at the right individuals has been shown to moderate costs and utilization,

 inconsistent results have highlighted the need to appropriately select patients.

There are a host of available case-finding tools and algorithms that can be used to identify individuals appropriate for care management; nevertheless, there is also increasing understanding that these tools have limitations.

 Many case-finding tools are proprietary “black boxes” that create a single score to rank patients according to risk.

 Additionally, these tools can vary considerably in terms of their input criteria, and there is no standardization of definitions across tools

—most importantly, the input criteria cannot be modified to meet specific program characteristics and operational realities.

Data timing and completeness are a challenge for all case-finding algorithms, especially those based on paid claims. Delays in data capture can result in the identification of patients who are no longer enrolled within the health plan or practice, as well as identification of patients who no longer require intervention.

 Incomplete data, such as removal of substance use, HIV/AIDS, and in some states, mental health claims,

 paint incomplete pictures of patient needs. In addition, demographic and psychosocial characteristics are often incomplete or missing from readily available administrative data (eg, poverty, education, living situation). The absence of patient psychosocial data is particularly challenging, as these data could help to identify cohorts of patients who would be most likely helped by care management programs (CMPs)

; for example, addressing cost-related medication underuse among low-income patients may improve diabetes control and reduce inappropriate emergency department utilization.

 Although many healthcare systems do collect psychosocial data on patients, system alignment issues often preclude use of more granular and descriptive data for case finding.

Because of these limitations, case finding based on administrative data is typically not the only method used.

 Patient health-risk assessments and clinician referrals are techniques frequently used to identify patients for case management and can provide more detailed and actionable data, but they require time and resources to implement.

 Often, multiple case-finding methodologies, including predicted risk scores, utilization triggers/markers, medication adherence and gaps in care, and clinician referrals, are used in concert.

Case-finding approaches that rely on multiple methodologies are typically termed “hybrid approaches” and can vary widely, but generally refer to the use of multiple methodologies—often in sequence&mdash;to refine case finding. Within this evaluation, we studied a sequential approach to case finding that uses an algorithm to comb through administrative claims and identify a preliminary set of potentially high-risk patients. This preliminary set of patients is then reviewed by physicians and nurse care managers who make the final decision of whether to refer a patient for care management. This sequential hybrid approach is thought to be reliable

Clinician referral, within the boundaries of a pre-selected list, focuses the referral process and reduces the burden on clinicians, while at the same time ensuring primary care practice and physician (PCP) involvement. This involvement may increase buy-in of the program, which is especially important for programs embedded within physician practices. Clinician input can also help to identify patients with more modifiable health risks, including those with sub-optimal health literacy and health insight, social and home environment challenges, and/or inadequate coping skills and financial resources.

 For example, primary care physicians are more likely to describe patients with poorly controlled diabetes, inadequate insurance, or with mental health or substance use disorders, as complex.

However, clinician referral may also introduce unanticipated variations in patient selection.

 Often, there are no specific criteria that physicians are asked to consider when identifying appropriate patients, leaving the door open to unintentional biases.

 Appropriate clinician referral into CMPs requires that clinicians have a clear understanding of the characteristics that place patients at higher risk for poor health outcomes, as well as the benefits the CMP could provide.

In this study, we sought to understand the unique patient characteristics associated with both patient identification as high risk for poor outcomes and subsequent clinician referral to care management. We also sought to describe variations in patient identification and selection for care management across PCPs overall, and for important patient characteristics. We examined the selection decisions during the second year of the CMP to avoid biases associated with the ramp-up of the program.

The Partners Healthcare Pioneer Accountable Care Organization (ACO)—1 of 4 Pioneer ACOs operating in eastern Massachusetts&mdash;is among the largest nationwide, and the only Pioneer ACO that exclusively aligns patients to primary care physicians. One of the signature initiatives within the Partners Pioneer ACO is the CMP. The CMP is a longitudinal program that relies on nurse care managers working in conjunction with primary care physicians to improve care quality and control healthcare utilization for referred patients. After a successful trial within the Medicare High Cost Beneficiary Demonstration Program,

 the CMP was implemented within all Partners Healthcare PCPs under the Medicare Pioneer ACO contract, starting in January 2012. Partners Healthcare uses a hybrid approach for patient selection, combining a claims-based algorithm with clinician review to identify high-risk patients who could benefit from the CMP.

The population for this study included traditional Medicare beneficiaries within the Partners Healthcare Pioneer ACO who were identified using a claims-based algorithm as potentially high risk for poor outcomes in the second year of the program (2013). By the second year, all practices had experience performing the clinical review and had 1 or more ACO patients who were actively engaged in CMP. In total, 7651 (12%) of 63,629 Medicare patients were identified by the Partners algorithm. Among individuals with a predictive risk score for future total medical expense via Optum ImpactPro software version 6.0 (Optum, Inc, Eden Prairie, Minnesota) that exceeds a minimum threshold, the Partners algorithm uses combinations of chronic conditions and patterns of healthcare utilization to identify individuals potentially at high risk and appropriate for subsequent clinical review. The Partners algorithm was developed with extensive input from an advisory committee of operational and clinical experts within the organization. During the clinical review process, PCPs receive detailed information on chronic conditions, utilization patterns, and predictive scores that triggered identification by the algorithm. PCPs had considerable latitude when selecting patients for care management, but were provided additional guidance through reference guides and regional medical directors who received orientation and training on the list review process.

In order to study clinician review decisions during the second year, this analysis excluded 2605 patients who had been identified and reviewed in the prior year, as well as 1267 patients who had moved, died before the review process, were not community-dwelling (eg, residing in long-term care), switched to a Medicare Advantage plan, or did not have a relationship with a Partners primary care clinician. Lists of the remaining 3779 patients (5.9% of 63,629 total Medicare ACO patients) were distributed to practice management organizations and, eventually, to PCPs and clinicians for final identification of individuals appropriate for the primary care—based CMP. We then further excluded 788 patients aligned to 44 PCPs that did not make differential review decisions (eg, identifying none or all patients as high risk for poor outcomes or requiring care management), as well as 306 patients who were not classified by the practice prior to the release of the 2014 list. Thus, the final study population consisted of 2685 patients who received primary care within 35 practices.

The dependent variables are the decisions made by primary care clinicians and their practices regarding whether patients were identified as high risk for poor outcomes and whether patients could benefit from CMP. 

 and include patient demographic characteristics, area-level poverty (American Community Survey 2012 5-Year Estimates

), prospective risk score and changes in the number of conditions from calendar year 2012 to the year directly preceding the first list review (May 1, 2012-April 30, 2013) (CMS-Hierarchical Condition Categories Risk Adjustment Model

), healthcare utilization (ED visits, medical hospitalizations, skilled nursing and hospice service use) in the 12 months prior, and characteristics of the patient’s relationship with their PCP. Practice characteristics include practice size, level of affiliation with the healthcare organization, whether the practice is a community healthcare center, years of experience with care management, CMP capacity, and level of practice readiness for patient-centered medical home transformation. Models were also adjusted for primary care physician characteristics, including primary care physician age, full-/part-time status, and duration of employment within the healthcare organization.

Descriptive analyses of the patient- and practice-level characteristics associated with patient identification as high risk for poor outcomes and selection for care management were explored using bivariate analyses. Standardized estimates of patient identification as high risk and selection for CMP were developed using logistic regression models with robust standard errors clustering at the practice. For regression models, we used the subset of explanatory variables that were independently predictive of either patient identification as high risk for poor outcomes or selection for care management at 

 <.20, and used a similar approach to develop standardized estimates at the practice level.

We explored the extent to which practices consistently identified patients with specific characteristics as high risk for poor outcomes. Specifically, we examined 6 patient characteristics that were independently associated with being considered high risk for poor outcomes and that were present in at least 10% of patients in our sample; similar models were used for each, as described above, to develop standardized practice-level estimates of identification as high risk among patients with and without the characteristic. Full interaction models did not enhance predictive ability and decreased the number of practices with stable estimates; therefore, we only included interactions between PCP and the characteristic of interest in our final models.

Global tests were used to test for differences in overall practice marginal estimates and interaction marginal estimates. Chi-square statistics were used to describe the relationship between overall practice marginal estimates and practice estimates for individuals with specific characteristics.

The study was approved by the Partners Institutional Review Board.

Among Medicare ACO patients identified by the claims-based algorithm and who also passed an administrative review (roughly 5.9% of total Medicare ACO patients), 86.3% were thought to be high risk for poor outcomes by their primary care providers; 77.2% of these patients were referred to a nurse-led CMP within the PCP.

A number of patient characteristics were independently associated with patient identification as high risk during the clinician review. Specifically, independent predictors of high risk include advanced age, higher prospective risk score, a recent increase in diagnosed conditions, and higher numbers of medical hospitalizations and days in skilled nursing facility (SNF) care in the prior year. Patients identified as high risk also were more likely to have more visits with their PCP in the prior 12 months, but shorter relationships with their PCP than patients not identified as high risk. The patient characteristics with the strongest independent association with being identified as high risk include age 85 years or older (93.1%), 2 or more hospitalizations in the prior year (91.5%), and 30 or more SNF days during the prior year (92.7%) (Table 1).

Among high-risk patients, advanced age was also an independent predictor of patient referral to a CMP. However, when determining whether high-risk patients would benefit from care management, clinicians were also more likely to select individuals without prior hospice use and those living in lower-income areas. The adjusted proportions of high-risk patients 85 years or older selected for care management was 80.1%, and between 81.2% and 81.9% among patients residing in areas with more than 10% of individuals living in poverty (Table 1).

Lists of patients identified by the claims-based algorithm were reviewed within 35 practices where patients received their primary care. On average, practices reviewed 76 algorithm-identified Medicare ACO beneficiaries (range = 5-248) in 2013. Adjusted proportions of patients identified as high risk and referred to a CMP varied according to practice characteristics. Specifically, after adjusting for patient characteristics and other practice characteristics, practices with 6 or more physicians (88.8%), non—community health centers (87.3%), and practices without the capacity to provide care management to all patients (93.5%) had the largest adjusted proportions of algorithm-identified ACO patients identified as high risk during clinical review. Among high-risk patients, adjusted proportions selected for care management were highest within practices employed by the health delivery organization (80.8%) and non–community health center practices (78.4%) (

After adjusting for patient and primary care physician characteristics and controlling for the clustering of patients within PCPs, practices varied significantly in terms of both the adjusted proportion of patients identified as high risk (36% to 99%; 

 <.001) and the proportion of patients referred to the CMP (18% to 96% ; 

Hybrid approaches allow for clinician input into case finding for care management, but training and monitoring is required to protect against unintentional biases.

Richard Brand, PhD

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