Curr Diab Rep (2014) 14:479 DOI 10.1007/s11892-014-0479-z

HEALTH CARE DELIVERY SYSTEMS IN DIABETES (D WEXLER, SECTION EDITOR)

Rational Use of Electronic Health Records for Diabetes Population Management Emma M. Eggleston & Michael Klompas

Published online: 11 March 2014 # Springer Science+Business Media New York 2014

Abstract Population management is increasingly invoked as an approach to improve the quality and value of diabetes care. Recent emphasis is driven by increased focus on both costs and measures of care as the US moves from fee for service to payment models in which providers are responsible for costs incurred, and outcomes achieved, for their entire patient population. The capacity of electronic health records (EHRs) to create patient registries, apply analytic tools, and facilitate provider- and patient-level interventions has allowed rapid evolution in the scope of population management initiatives. However, findings on the efficacy of these efforts for diabetes are mixed, and work remains to achieve the full potential of an-EHR based population approach. Here we seek to clarify definitions and key domains, provide an overview of evidence for EHR-based diabetes population management, and recommend future directions for applying the considerable power of EHRs to diabetes care and prevention. Keywords Diabetes . Population management . Electronic health record . Electronic medical record . Team-based care . Medical home . Accountable care organization . Quality . Value

This article is part of the Topical Collection on Health Care Delivery Systems in Diabetes E. M. Eggleston (*) : M. Klompas Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, 133 Brookline Avenue, Boston, MA 02215, USA e-mail: [email protected] M. Klompas e-mail: [email protected] E. M. Eggleston Brigham and Women’s Hospital Division of Endocrinology, Diabetes and Hypertension, 221 Longwood Avenue, Boston, MA 02115, USA

Introduction What is Population Management? The national strategy for quality improvement in health care [1] builds directly on the Triple Aim of improving individual care, stabilizing health care costs, and improving population health through population management and collaboration with public health [2••]. The meaningful use legislation of the Affordable Care Act [Health Information Technology for Economic and Clinical Health (HITECH)] supports advances in consistency and quality of EHR data [3]. Each of the 3 stages of meaningful use legislation require population-level targets (Table 1) (www.healthit.gov), and health systems across the country are working to build the human and health information technology (HIT) capacity to employ and refine population-based strategies. The term “Population Management” is increasingly invoked as a potential solution to the persistent gaps in quality and value of care facing the US health care system. Population management is not a new term. It has been used for several decades to describe population-based approaches to improve the clinical care of diabetes [4••, 5–7]. There has been a resurgence of interest in population management in the setting of health care reform as the US moves away from volumebased payment models to those in which providers and practices are responsible for the costs and outcomes of entire patient populations. Reflecting the use of the term for both clinical and financial goals, “population management” has different meanings depending on the user and the context, with varying degrees of emphasis on improvement in processes and outcomes of clinical care vs efficiency and value (quality per expenditure) [8••]. In some instances, for example, as used by the federal Agency for Healthcare Research and Quality (AHRQ), the focus is limited to primary care settings [9], whereas in others it denotes a general approach,

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Table 1 Meaningful use criteria that support population management Stage 1 (2011–2012) Data capture and sharing Core objectives • Use computerized order entry for medications • Provide drug-drug and drug-allergy interaction checks • Maintain an up-to-date problem list of current and active diagnoses • Maintain an active medication list • Maintain an active medication allergy list • Record complete demographics • Record and chart changes in key vital signs including height, weight, blood pressure, and body mass index • Record smoking status • Report ambulatory clinical quality measures to Center for Medicare Services • Implement 1 clinical decision support rule Menu objectives: Practices select from these in stage 1, most become core objectives in Stage 2 • Incorporate clinical laboratory test results into the EHR as structured data • Generate lists of patients by specific conditions (eg, diabetes) • Send patients reminders for preventive care and follow-up visits • Provide patients with timely electronic access to their health information • Generate patient-specific education resources • Perform medication reconciliation • Provide providers with a summary of care prior to each transition of care or referral • Develop capability to submit electronic data to immunization registries • *Develop capability to submit electronic syndromic surveillance data to public health agencies (remains a menu objective) Stage 2 (2014) Information exchange and care Core objectives: Core and menu objectives from Stage 1 plus: coordination • Use computerized order entry for medications, laboratory tests, and radiology orders • Use clinical decision support to improve performance on high-priority health conditions (diabetes) • Use clinically relevant information to send patients reminders for preventative or follow-up care • Use clinically relevant information to provide patients with patient-specific education resources Menu objectives: Practices select from these • Develop capability to submit electronic syndromic surveillance data to public health agencies • Record clinical notes electronically within the EHR • Display diagnostic imaging directly within the EHR • Record patient family health history as structured data • Develop capability to identity and report patients with specific conditions (eg, diabetes) to specialized registries • Develop capability to submit electronic syndromic surveillance data to public health agencies Stage 3 (2016) Improve outcomes Specific objectives for Stage 3 have yet to be finalized. The general principles for Stage 3 are projected to include the following: • Improve the quality, safety, and efficiency of care in order to improve health outcomes • Engage families and patients—recording of patient preferences and engagement in self-management • Access comprehensive patient data through health information exchanges • Improve team based care and coordination of care • Improve population health • Address disparities *Meaningful use contains 2 sets of objectives: “core” (required) and “menu”, (from which practices/health systems choose from a list of options) Sources: http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Stage_2.html http://www.healthit.gov/providers-professionals/meaningful-use-definition-objectives http://www.healthit.gov/providers-professionals/how-attain-meaningful-use http://www.healthit.gov/facas/sites/faca/files/MUWG_Stage3_13_Sep_4_FINAL_0.pdf

or set of approaches, that can be applied regardless of setting or population [2••, 10]. The population of interest may be patients in a delivery system, practice, or provider’s patient panel and defined by disease state (eg, diabetes), risk factors (eg, multiple comorbidities) or other characteristics (eg, polypharmacy). Nonetheless, although the goals, and the

functional domains, of population management vary by setting and intent, the unifying concept is to take a population view of clinical care that extends beyond the collective sum of individual interventions to improve care for entire populations across conditions, care providers, and sites of clinical care.

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Diabetes as a Target for Population Management Diabetes is a condition that seems to stand to benefit from an integrated population-based approach to both prevention and management. It carries a particularly high burden of disease and financial cost to patients, health systems, and society [11, 12•] and is marked by disparities in both care [13–15] and outcomes [16–18]. In addition to increased risk of morbidity and mortality [19, 20] diabetes also has significant impact on psychosocial health and functioning [21, 22]. It requires lifelong intensive medical regimens for many patients and selfcare and behavior change for all [23]. From a health care system view, diabetes is profoundly resource-intensive, accounting for 1 of every 5 direct health care dollars spent [12•]. However, both onset of disease [24–26] and development of complications [27–29] are modifiable with preventive efforts and cost-effectiveness analyses finding these preventive interventions to be cost-saving over time [30, 31]. As a result preventing disease and its complications in high-risk populations while reducing cost and improving care is a high priority for health systems, payers, and public health partners. In addition to preventive intervention, risk stratification in defined populations of patients with existing disease enables targeted management by: (1) disease subtypes (eg, Type1, Type 2, gestational diabetes, MODY, cystic-fibrosis related diabetes); (2) presence of micro and macrovascular complications or high risk comorbidities (psychiatric disease, substance use); (3) emerging populations (diabetes in pregnancy, diabetes in youth); (4) other factors that increase risk of adverse outcome (age, polypharmacy, language barrier). Defining risk and care by population characteristics also facilitates interventions to address persistent disparities in outcomes by race/ethnicity, gender, education and socioeconomic status. However, assigning collective status by characteristic must be done with clarity of both purpose and analytic approach to avoid the potential of erroneous interpretation of risk and inadvertent exacerbation of disparities [32••]. In light of this fit between disease characteristics and a population approach, it is not surprising that diabetes was one of the first targets of early population management interventions [4••, 5–7] or that it remains a leading area of ongoing innovation in population management [33–36].

The Role of Electronic Health Records in Promoting Population Management Much of the ongoing innovation in health care delivery is spurred by the increasing penetration of electronic health record systems into more practices and ongoing improvements in their functionality [37•, 38]. Population management targets are an integral part of all 3 stages of Meaningful

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Use promoted by the Health Information Technology for Economic and Clinical Health (HITECH) act (Table 1). These include creating disease-specific registries, enhancing data sharing, creating decision-support modules with increasing functionality and breadth, and selected diabetes-specific quality measures. The proposed Stage 3 Meaningful Use Criteria are intended to support new models of care, target disparities, allow for functional health information exchange, and improve engagement of patients and families in self-care (www.healthit.gov). However, there is not yet a defined set of consistent EHR-based population management domains in the medical literature. Based on the existing body of work in diabetes and other chronic diseases, 8 primary domains emerge: (1) population definition (including creation of registries); (2) risk stratification and reporting; (3) measurement (processes of care and outcomes); (4) clinical decision support; (5) health information exchange; (6) facilitation of team based care; (7) patient and family engagement; and (8) targeting of disparities. These domains can be grouped into the 2 primary functions of the EHR articulated by Grant and colleagues [39••] in their seminal work on diabetes population management: analytic function and clinical action (capacity for provider and patient intervention). Analytic and measurement functions allow for the identification of populations and creation of registries; stratification by risk factors; ascertainment of measures of care (process and outcome); and tracking of changes in these measures with intervention. These analytic functions in turn support the “doing” functions of population management, such as clinical decision support, team-based care, patient and family engagement, integration of care and care transitions across sites, and targeting of disparities. Analytic Function Analytic tools allow providers to define and risk-stratify populations for targeted management, either with a cross-sectional approach or by creation of diabetes registries to follow patients longitudinally over time [4••, 34, 35, 40–42]. This requires the development of automated algorithms to ascertain cases, risk factors, and care and outcome measures of interest. The detailed longitudinal data available in EHR’s including demographics, clinical history, exam, laboratory orders and values, imaging, pharmacy and, in integrated systems, claims data support the development of increasingly sophisticated detection algorithms. Published algorithms for diabetes ascertainment include detection of metabolic syndrome [43], type 1 vs type 2 diabetes [44], pediatric diabetes [45], newly diagnosed vs longstanding type 2 diabetes [46], and real-time diagnosis of type 2 diabetes [47]. Algorithms that can identify cases and complications with high positive predictive value are critical in EHR-based

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population management. However, this can be challenging with diabetes due to multiple subtypes, varied risk factors, and diverse complications. Validation of ascertainment algorithms and of quality and outcomes measures is of critical importance for data capture and interpretation. Limitations to EHR based case and risk factor ascertainment are detailed elsewhere [44], however, 2 limitations in particular bear mentioning. First, ascertainment of cases, comorbidities, and outcomes via diagnostic codes alone may be insensitive or inaccurate. Clinicians may use an incorrect ICD9 code (eg, type 2 diabetes codes for patients with type 1 disease), they may fail to distinguish between past vs present diseases (eg, coding for gestational diabetes in a patient who has a history of gestational diabetes rather than current and active gestational diabetes), or they may use diagnostic codes for screening activities (eg, assigning a type 2 diabetes when simply ordering a screening test). Coding is particularly problematic in diabetes due to its many disease subtypes, lack of a uniformly applied code to denote “prediabetes”, and lack of clarity on how to code for gestational diabetes vs overt preexisting diabetes in pregnancy. Algorithms that combine diagnostic codes with laboratory values and medications are more sensitive and specific than those employing diagnostic codes alone [43, 44]. Second, because the diagnoses of diabetes, prediabetes, and gestational diabetes are based on glucose alone, current procedural terminology (CPT) codes and results for glucose tests are, as a group, very useful for case ascertainment. However, in some cases, the same CPT codes can applied for differing tests, for example fasting and random glucose, or erroneously applied to different tests (eg, 75 g and 100 g oral glucose tolerance tests), with very different ramifications for case ascertainment and tracking of quality measures. There is also the risk of over, and under, ascertainment of disease based on glycemic testing depending on the test used and the threshold chosen as: (1) A1C, fasting plasma glucose, and oral glucose tolerance tests capture differing proportions, and in some cases differing populations, of patients with dysglycemia [48, 49]; (2) glycemic tests may differ by age, race/ethnicity, and pregnancy status [50–52]. Once a population has been defined and a registry created, automated rules engines can incorporate evidence-based care guidelines (eg, frequency and completeness of recommended tests, appropriate medications, screening, and treatment goals) to generate reports that provide layered views of care gaps by risk status or other characteristic, and guide clinical action. Clinical Action The translation of analytic information to diabetes intervention in the published literature varies by site characteristics, EHR functionality, and by population management goals, but includes clinical decision support [4••, 5–7, 33, 35, 40–42, 53–58], patient decision support [4••, 5, 7, 53, 56]

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coordination of care [4••, 41, 58], patient provider communication [7, 33, 59] and facilitation of team based care [4••, 33, 34, 58]. To date, most efforts have occurred in the primary care setting [4••, 5–7, 33–35, 39••, 40–42, 53–59] including patient centered medical homes [56] and have focused primarily on patients with prediabetes and type 2 diabetes. An example of EHR-based diabetes clinical action is provided in Fig. 1, with a screen shot from TopCare, a population-based information management system used in the Partners HealthCare system for a range of chronic disease management. This view demonstrates both the sophisticated functionality, such as identification of patients for insulin initiation protocols, and the support of team-based care, as seen in the diabetes nurse care manager view, made possible by EHR’s. Few published examples of population management have integrated specialty care or focused on other diabetes subtypes. However, in a multifaceted diabetes management program at Group Health Cooperative of Puget Sound, McCullough and colleagues implemented a “diabetes expert care team”, consisting of a diabetologist and a certified diabetes educator, with the purpose of joint visits with primary care providers [4••]. More recently, King et al described a pilot program of centralized diabetes specialty services to assist primary care management as needed [60]. Population management initiatives led by primary care and with a focus on type 2 diabetes is critical in light of the epidemiology of the disease and the central role of primary care in managing the large majority of diabetes patients. However, there is considerable potential for further extending population management to innovative collaborative approaches with specialty providers and to the development of initiatives that target diabetes subtypes, emerging high risk groups, and vulnerable populations.

Does EHR-Based Population Management Improve Diabetes Care and Outcomes? There are several reviews of the impact of computer-based population management for diabetes care [61••, 62, 63]. Although not limited exclusively to EHR-based interventions, they provide insight into existing evidence. Balas et al reviewed 40 randomized controlled trials of a range of computerized interventions to improve diabetes care measures and intermediate outcomes, including hand-held insulin titration devices and glucose management approaches in pediatric patients. They found variability by individual approach and by care measure, but overall improvement of clinician adherence to measures and a decrease in average glucose and hemoglobin A1c. [62]. Costa et al included observational studies as well as randomized control trials of HIT-based interventions, but limited their review to those that included

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Fig. 1 Screenshot of TopCare Population Management Program. Screen view is that of the “diabetes champion”, a designated nurse care manager who organizes care for patients with diabetes, or at risk for diabetes, in each primary care practice. Patients can be filtered by parameters including A1C, smoking status, age, visit date, and clinical actions, including

initiation of insulin, titration of insulin, or others. Primary care provider views show restricted lists. Once a management option is selected in TopCare, patients are transferred to “rosters” for specific management protocols. Figure courtesy of Adrian Zai

outcome measures. Among 16 studies published between 1999 and 2009, several demonstrated improvement in intermediate outcomes, including A1C lowering, with HIT-based approaches. However, the authors concluded that variability in methodology and quality of the studies, particularly lack of controlling for confounders or alternate explanations of observed effect, precluded conclusion [63]. Claveringa et al performed a systematic review of randomized trials on the impact of 20 different clinical decision support interventions on diabetes processes of care and on intermediate outcomes including A1C, LDL cholesterol, and blood pressure lowering. They found computerized clinical decision support alone or in combination with other interventions improved processes of care, but outcomes were impacted only when clinical decision support was combined with case management or performance feedback [64••]. The results of observational studies comparing the use of an EHR to paper charts are mixed. Cebul et al found a positive effect of EHR use on both diabetes care measure and on intermediate outcomes in a large retrospective cohort comprised of 7 health care organizations with 46 practices, including safety net practices. They compared 4 care standards and 5 intermediate outcomes in paper-based practices vs those with an EHR. Standards of care were impacted by EHR use to a

greater degree than outcomes. They also looked at the effect of insurance type and safety net status. There were no significant differences by insurance type, but the findings were less strong for the uninsured overall. Findings were similar in safety net centers compared with non-safety net centers [65]. Similarly, Reed et al found impact EHR use on diabetes process and outcome measures (A1C and LDL) in a quasiexperimental study of outpatient EHR implementation in 17 medical centers, however, effect size was small [66]. In contrast, Crosson et al found no difference in diabetes care measures and worse intermediate outcomes in practices with an EHR vs those still using paper records [67]. Studies addressing population management in vulnerable populations are few, but Davidson and colleagues demonstrated a decrease in emergency room visits, hospitalizations, and costs after implementing a decision support system integrated with a nurse-based diabetes management program in an underserved minority population [58]. Taken together, these data suggest that EHR-based population management strategies can improve diabetes care. However, the effect appears greatest on process measures, whereas impact on intermediate outcomes is variable and more likely to be effective when integrated with case management and/or clinician feedback.

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Impact of Population Management on Disparities Diabetes is a disease with well-documented disparities by race/ethnicity [68–70] gender [71, 72], income [73, 74], and geography [75, 76]. EHR-based population management has the potential to address these disparities through identification of high risk and vulnerable groups and tailored outreach and management. However, as pointed out by Biag et al in their review of the use of health IT for diabetes quality improvement in minority patients, few studies have been conducted in racial ethnic minorities or resource-poor settings [77••]. They call for health IT interventions that specifically target diabetes processes of care and outcomes in vulnerable populations, provide rigorous evaluation of these interventions, and specific support to develop EHR-based interventions in under resourced settings [77••]. Disparities may also be worsened by the lag in adoption of EHRs in community health centers and rural areas, where many vulnerable patients with diabetes receive care [78]. However, some safety net sites have been leaders in both adoption of EHR’s and in health information exchange [79] and community health centers and safety net providers are specifically supported in federal meaningful use legislation. Outcomes and Process Measures Choice of measure, whether process or outcome, is critical to measurement validity and accuracy and relevance for clinical and patient-centered outcomes. This is particularly important for diabetes care, as process and intermediate outcome measures may not be correlated with clinically meaningful outcomes for all patients [80, 81]. However, from the perspective of effective quality improvement, it can be argued that it is vital to overcome clinical inertia and fragmentation of care and take action to improve the quality, value, and consistency of care, even if measures may initially be imperfect [82]. This tension between measures for the purposes of health system goals that allow rapid assessment and care refinement vs those with longer-term clinical relevance is further complicated by an increasing call to include patient-centered outcomes in care and research [83]. Although most often aligned, patientcentered and population management goals can at times be in direct conflict with one another. For example, medication intensification and glycemic targets are among the process and outcome measures most frequently assessed in population management initiatives, but may directly conflict with the patients’ own goals, even with the best information and shared decision-making. However, they may be unattainable in many patients without hypoglycemia or other adverse effects that negatively impact their quality of life (family and work relationships, sexual function, comfort driving a car, participation in sports or hobbies) or without increasing disease-related stress. In the case of some patients, eg, those who live alone,

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operate vehicles for employment, or have comorbidities that impact response to potential treatment harms (eg, seizure disorder or physical disability), these impacts can extend beyond quality of life to basic functioning. Diabetes was one of the first chronic diseases targeted in delivery and payment initiatives to increase quality and value of care, but also one for which the evidence base for care targets were simultaneously undergoing a revolution in the wake of the ADVANCE, ACCORD, and VADT trials [84–87]. Following these trials suggesting potential harms with very aggressive hemoglobin A1c targets, treatment paradigms and goals are being questioned [80, 88, 89] and new treatment guidelines that allow for individual flexibility with glycemic goals have been proposed [90] (http://www. healthquality.va.gov/diabetes/DM2010_SUM-v4.pdf.). EHR-based population management must be nimble to respond to changes in medical knowledge in order to adjust clinical decision support directives, revise clinical targets, and refocus feedback according to the latest best practices. This degree of responsiveness is not yet possible for many population management initiatives, but the remarkable analytic potential of EHR’s may be able to a) tailor care recommendation to a patient’s clinical status via automated detection algorithms (eg, if advanced age, microvascular complications, or cardiovascular comorbidities suggest adjusted A1C targets) and enable innovative approaches to documenting, and integrating, patient-centered goals with guideline-based targets. Impact of EHR-Based Population Management on the Medical Record The primary purpose of the medical record, whether paper or electronic, is to document the needs and care of individual patients over time and to facilitate communication between providers. These core functions require flexibility and means for clinicians to express complicated and nuanced thoughts; however, flexibility and nuance can be lost with template data structures and fixed measurement requirements. As articulated by others [91, 92] the core purpose of the medical record can become obscured by the competing demands placed upon it for billing, compliance, and medico-legal purposes in the transition to electronic format. The additional goals of population management, public health surveillance, and comparative effectiveness research, although critical to the health of patients and to the viability of the health care system, can also threaten to erode the core function of medical narrative and impair communication between providers and patients. The time required to enter data to meet EHR- based quality measurement criteria reduces time available for discussing, and documenting, information (hypoglycemia, substance use, contraception) that may be more relevant to clinical or patient-centered outcomes than process measures. Moving forward, analytic structures and taxonomies (eg, the required

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data input to meet these diverse demands of the electronic health record) must be designed to derive population management information without destroying the primary patientcentered function of the EHR. This is particularly important in diabetes, in which psychosocial factors and trusting longitudinal relationships with providers are each important to disease management and outcomes, but may not align fully with measurement goals of quality and value initiatives. With forethought in EHR design, balancing these sometimes competing goals may be achievable through an intelligent mix of structured data fields, free text options, and smart analytics, including algorithms to sift through structured data and natural language processing for free text entries.

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Advances in natural language processing may help to capture these elements more consistently. However, diabetes-specific taxonomies are needed across EHR’s to not only reach the full potential of diabetes population management but to minimize underestimation of quality of care with automated populationbased approaches. In a retrospective comparison of automated EHR-derived quality measures vs manual review of the EHR, Parsons et al found that automated EHR analyses significantly undercounted achievements in clinical care measures [97]. Last, there are variables with important ramifications for population approaches to diabetes risk and outcomes and with particular impact on disparities, such as transportation, violence in the home or community, and food security that are not routinely found in the EHR.

Limitations to EHR-Based Population Management There is continued and active debate as to whether the EHR can improve clinical outcomes and value. As pointed out by Bitton [93], despite the great promise of EHR’s for population-based approaches there is a mismatch between their current capabilities and the functionality needed for population management. This mismatch is becoming increasingly untenable in light of emerging delivery systems, such as Accountable Care Organizations, in which care, resources, and payments are integrated. The majority of EHR’s have not yet caught up to this integrated future. They were designed under a fee-for-service environment with the goal of tracking and maximizing billable episodes of care, not to support integrated or team-oriented care [37•, 93] and, with the exception of some large delivery systems, most EHR’s lack interoperability and capacity for data sharing [94]. This is a profound barrier to effective population management in the outpatient setting and in the inpatient to outpatient transition. Transitions of care are further impacted by the dearth of EHR’s in a majority of nursing homes, rehabilitation centers, and psychiatric hospitals—entities not included in federal meaningful use legislation [95]. This has particular ramifications for diabetes patients, in whom psychiatric hospitalizations, rehabilitation stays, or nursing home placements following hospitalization is not uncommon. Beyond functionality and interoperability, there are current limitations to the data structure and integrity in many EHRs [96] that impact the capture, analysis, and communication of population-level data, which in turns limits capacity to build registries, identify risk factors, and measure care and outcomes. Limitations in validity arise from physician miscoding, algorithm programming errors, and mode of entry into EHR (free text vs structured data field). There are several important diabetes-specific risk and care measures that may not be captured in a structured data field. These include hypoglycemia, diet and exercise, adherence discussions, and complications that may not be recorded in the problem list (eg, depression or treatmentrelated anxiety, sexual dysfunction, peripheral neuropathy).

Conclusions and Future Directions Spurred by innovation in health care delivery and federal meaningful use legislation, EHR-based population management approaches are increasingly being used to improve the quality and value of diabetes care. Significant progress has been made in identifying effective population-based approaches, particularly via clinical decision support and patient self-management. These developments are most mature in primary care settings in programs that target type 2 diabetes. However, effectiveness has been demonstrated primarily with process measures, and selected intermediate outcomes (A1C, LDL-levels), rather than clinical outcomes (end-organ damage, mortality). Further work is needed to ensure that population management builds upon process measures and intermediate outcomes to also encompass clinically meaningful, patient-centered outcomes. This work may be supported by the increased capacity to support team based care; share data between sites, and engage patients and families in care that are explicit goals of Meaningful Use stage 3. The tremendous analytic potential of the EHR can also be applied to this effort via the development of advanced algorithms that allow translation of the most up-to-date evidence to decision support and interventions tailored by patient age, duration of disease, comorbidities, and complications. In this way the intensity of treatment can be targeted to those who may gain the most, and avoided in those in whom risk of harm outweighs benefits. There is as yet untapped potential to use EHRs to identify and promote novel approaches to collaborative care between primary care and specialty colleagues, an area of critical importance as the US moves toward integrated care and delivery models. An evolving area of innovation is in the development of tools that enable EHRs to interface with patients, and data they share, via cell-phone, and social media. Last, the application of population management to high-risk and emerging populations, such as type 1 diabetes, diabetes in youth, and diabetes in pregnancy holds great potential for bringing the

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power of the EHR-based population to care for the full spectrum of patients impacted by diabetes and its complications.

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11. Acknowledgments Michael Klompas has received grant support from CDC and the Office of the National Coordinator for Health Information Technology.

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Compliance with Ethics Guidelines Conflict of Interest Emma M. Eggleston declares that she has no conflict of interest. Michael Klompas declares that he has no conflict of interest.

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14. Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.

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Rational use of electronic health records for diabetes population management.

Population management is increasingly invoked as an approach to improve the quality and value of diabetes care. Recent emphasis is driven by increased...
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