Construction of a Multisite DataLink Using Electronic Health Records for the Identification, Surveillance, Prevention, and Management of Diabetes Mellitus: The SUPREME-DM Project

Nichols, Gregory A.; Desai, Jay; Lafata, Jennifer Elston; Lawrence, Jean M.; O’Connor, Patrick J.; Pathak, Ram D.; Raebel, Marsha A.; Reid, Robert J.; Selby, Joseph V.; Silverman, Barbara; Steiner, John F.; Stewart, Walter F.; Vupputuri, Suma; Waitzfelder, Beth; Clarke, Christina L; Donahoo, William T.; Goodrich, Glenn K.; Paolino, Andrea; Schroeder, Emily B.; Shainline, Michael; Xu, Stan; Bounds, Lora; Gundersen, Gabrielle; Newton, Katherine M.; Rillamas‐Sun, Eileen; Geise, Brandon D.; Harris, Ronald I.; Stametz, Rebecca; Yan, Xiaowei; Akkerman, Nonna; Dobie, Liz; Li, Aida; Morris, Heather; Thomas, Abraham; Becker, Mary; Ohnsorg, Kris; Sherwood, Nancy E.; Ahmed, Ameena T.; Nakasato, Cynthia; Parker, John V.; Williams, Rebecca; Chou, Cathy; Ferrara, Assiamira; Karter, Andy; Neugebauer, Romain; Selby, Joe V.; Schmittdiel, Julie A.; Swain, Bix E.; Hazlehurst, Brian; Hillier, Teresa A.; Kimes, Terry; Kopp, Eric; Kurtz, Stephen G.; Sapp, Daniel S.; Preciado, Melissa; Zhang, Jian Leon; Zheng, Chengyi; Butler, Melissa G.; Owen-Smith, Ashli; Ren, Junling; Roblin, Douglas W.; Acharya, Amit; Miller, Aaron W.; Rasmussen, Luke V.; Siegler, Trish; Heymann, Anthony; Segal, Jodi B; Heisler, Michele

doi:10.5888/pcd9.110311

Cited by 111 publications

(85 citation statements)

References 19 publications

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“…Perhaps the greatest example of this kind is the SUPREME-DM project [2]. The study included a US network of 11 large health maintenance organizations gathering over 15 million members from their health plans between 2005 and 2009, of whom over 1 million (6.9%) met the criteria for diabetes set through a sophisticated algorithm including diagnoses, tests, and prescriptions.…”

Section: An Overview Of Best Practices In Diabetes Registersmentioning

confidence: 99%

“…According to the Directive, by 2014 a person with diabetes should be able to: (1) access information on the average quality of care provided by accredited centers in Europe, (2) bring personal data to a provider located in a different country and be able to add own data to the local register, and (3) extract personal data from the local registry and be able to transfer its content back to the country of origin. On the other hand, care providers should also be able to reciprocally exchange personal data according to the needs of the patient.…”

Section: International Developments and Future Perspectivesmentioning

confidence: 99%

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Standardized Information Exchange in Diabetes: Integrated Registries for Governance, Research, and Clinical Practice

Carinci¹,

Iorio²,

Benedetti³

2014

Frontiers in Diabetes

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To fight diabetes effectively, both local action and global engagement are needed. All of the potential stakeholders are necessary for collating complete data and using the information to improve policy and practice on a daily basis. A substantial contribution is required by those who deliver care regularly to people with diabetes, monitoring the status and progression of the disease with better knowledge of the problem and at a lower cost per contact. As an integral part of a long-term strategy, diabetes registers use information recorded at the point of care to improve quality of care through continuous monitoring of outcomes and prompt identification of subjects at increased risk of developing complications. Modern electronic diabetes registers substantially differ from simple electronic databases, as their role is at the core of a sophisticated network, where anything related to the condition of the individual can be measured with information on all the other levels of the healthcare system. In this chapter, we provide an overview of recent experiences in this field as well as challenges in the implementation of integrated registers, outlining roles and responsibilities of principal stakeholders and providing a snapshot of the best structure required to implement an integrated diabetes register. The chapter closes with a brief presentation of the relevant European legislation and the possible use of the recent achievements delivered by the European Union-funded public health projects BIRO and EUBIROD. The conclusions underpin the importance to follow up all recent experiences concluded positively, with consistent initiatives aimed at engaging governments, health professionals, and the whole community in the safe exchange of diabetes information, which is in everyone's interest.A modern diabetes register represents the fundamental pillar of regional/national strategies against diabetes. Through the standardized use of electronic medical records and the adoption of evidence-based definitions, registries can feed a con-

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Section: An Overview Of Best Practices In Diabetes Registersmentioning

confidence: 99%

Section: International Developments and Future Perspectivesmentioning

confidence: 99%

Standardized Information Exchange in Diabetes: Integrated Registries for Governance, Research, and Clinical Practice

Carinci¹,

Iorio²,

Benedetti³

2014

Frontiers in Diabetes

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“…Patients were identified as having diabetes if they had two outpatient visits or one inpatient discharge with an associated diagnostic code for diabetes (i.e., ICD-9 250.xx) or they had at least one dispensing of any glucose-lowering medication (exclusive of metformin) within the two-year period ending December 31, 2010. 25 To ensure adequate capture of pharmacy dispensing data, we included only those patients with continuous insurance coverage with a drug benefit between January 1, 2009, and December 31, 2010.…”

Section: Sample and Settingmentioning

confidence: 99%

“…Using the large patient populations and extensive clinical data available within the SUrveillance, PREvention, and ManagEment of Diabetes Mellitus (SUPREME-DM) consortium, 25 we describe the achievement of cardiometabolic risk factor control among insured black and white patients with diabetes and poor baseline risk factor control. We then examine the extent to which medication adherence, controlling for treatment intensification, may explain black-white differences in the subsequent achievement of cardiometabolic risk factor control among these insured patients.…”

Section: Introductionmentioning

confidence: 99%

Medication Adherence Does Not Explain Black-White Differences in Cardiometabolic Risk Factor Control among Insured Patients with Diabetes

et al. 2015

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BACKGROUND: Among patients with diabetes, racial differences in cardiometabolic risk factor control are common. The extent to which differences in medication adherence contribute to such disparities is not known. We examined whether medication adherence, controlling for treatment intensification, could explain differences in risk factor control between black and white patients with diabetes. METHODS: We identified three cohorts of black and white patients treated with oral medications and who had poor risk factor control at baseline (2009): those with glycated hemoglobin (HbA1c) >8 % (n=37,873), low-density lipoprotein cholesterol (LDL-C) >100 mg/dl (n=27,954), and systolic blood pressure (SBP) >130 mm Hg (n=63,641). Subjects included insured adults with diabetes who were receiving care in one of nine U.S. integrated health systems comprising the SUrveillance, PREvention, and ManagEment of Diabetes Mellitus (SUPREME-DM) consortium. Baseline and follow-up risk factor control, sociodemographic, and clinical characteristics were obtained from electronic health records. Pharmacydispensing data were used to estimate medication adherence (i.e., medication refill adherence [MRA]) and treatment intensification (i.e., dose increase or addition of new medication class) between baseline and follow-up. County-level income and educational attainment were estimated via geocoding. Logistic regression models were used to test the association between race and follow-up risk factor control. Models were specified with and without medication adherence to evaluate its role as a mediator. RESULTS: We observed poorer medication adherence among black patients than white patients (p < 0.01): 50.6 % of blacks versus 39.7 % of whites were not highly adherent (i.e., MRA <80 %) to HbA1c oral medication(s); 58.4 % of blacks and 46.7 % of whites were not highly adherent to lipid medication(s); and 33.4 % of blacks and 23.7 % of whites were not highly adherent to BP medication(s). Across all cardiometabolic risk factors, blacks were significantly less likely to achieve control (p<0.01): 41.5 % of blacks and 45.8 % of whites achieved HbA1c <8 %; 52.6 % of blacks and 60.8 % of whites achieved LDL-C <100; and 45.7 % of blacks and 53.6 % of whites achieved SBP <130. Adjusting for medication adherence/treatment intensification did not alter these patterns or model fit statistics. CONCLUSIONS: Medication adherence failed to explain observed racial differences in the achievement of HbA1c, LDL-C, and SBP control among insured patients with diabetes.

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“…2 Health insurance facilitates access to health care services and improves outcomes; 3-5 therefore these gaps are concerning. Electronic health record (EHR) functions used to manage chronic disease 6,7 have been shown to be effective in tracking patients' health insurance coverage, 8 and panel man- From the Department of Family Medicine, Oregon Healthagement systems could be adapted to identify patients and reach out to those without insurance. 9 Practice-based research networks (PBRNs) are an ideal setting in which to develop and test the effectiveness of tools to monitor patients' insurance status 8 and to facilitate communications to patients about new health insurance programs.…”

mentioning

confidence: 99%

Using Geographic Information Systems (GIS) to Identify Communities in Need of Health Insurance Outreach: An OCHIN Practice-based Research Network (PBRN) Report

Angier

Likumahuwa

Finnegan

et al. 2014

The Journal of the American Board of Family Medicine

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Background: Our. practice-based research network (PBRN) is conducting an outreach intervention to increase health insurance coverage for patients seen in the network. To assist with outreach site selection, we sought an understandable way to use electronic health record (EHR) data to locate uninsured patients.Methods: Health insurance information was displayed within a web-based mapping platform to demonstrate the feasibility of using geographic information systems (GIS) to visualize EHR data. This study used EHR data from 52 clinics in the OCHIN PBRN. We included cross-sectional coverage data for patients aged 0 to 64 years with at least 1 visit to a study clinic during 2011 (n ‫؍‬ 228,284).Results: Our PBRN was successful in using GIS to identify intervention sites. Through use of the maps, we found geographic variation in insurance rates of patients seeking care in OCHIN PBRN clinics. Insurance rates also varied by age: The percentage of adults without insurance ranged from 13.2% to 86.8%; rates of children lacking insurance ranged from 1.1% to 71.7%. GIS also showed some areas of households with median incomes that had low insurance rates.Discussion: EHR data can be imported into a web-based GIS mapping tool to visualize patient information. Using EHR data, we were able to observe smaller areas than could be seen using only publicly available data. Using this information, we identified appropriate OCHIN PBRN clinics for dissemination of an EHR-based insurance outreach intervention. GIS could also be used by clinics to visualize other patient-level characteristics to target clinic outreach efforts or interventions. (J Am Board Fam Med 2014;27:804 -810.)

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Construction of a Multisite DataLink Using Electronic Health Records for the Identification, Surveillance, Prevention, and Management of Diabetes Mellitus: The SUPREME-DM Project

Cited by 111 publications

References 19 publications

Standardized Information Exchange in Diabetes: Integrated Registries for Governance, Research, and Clinical Practice

Standardized Information Exchange in Diabetes: Integrated Registries for Governance, Research, and Clinical Practice

Medication Adherence Does Not Explain Black-White Differences in Cardiometabolic Risk Factor Control among Insured Patients with Diabetes

Using Geographic Information Systems (GIS) to Identify Communities in Need of Health Insurance Outreach: An OCHIN Practice-based Research Network (PBRN) Report

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