Rational use of electronic health records for diabetes population management

The health data utility and the resurgence of health information exchanges as a national resource

OBJECTIVES

(1) Describe the evolution of Health Information Exchanges (HIEs) into Health Data Utilities (HDUs); (2) Provide motivation for HDUs as a public strategic investment target.

MATERIALS AND METHODS

We examine trends in developing HIEs into HDUs and compare their criticality to that of the national highway system as an investment in the public good.

RESULTS

We propose that investment in HDUs is essential for our nation's healthcare data ecosystem. This investment will address the increased need for healthcare delivery and public health data.

DISCUSSION

HDUs can meet the current and future needs of healthcare delivery and public health surveillance. Their structure and capabilities will underpin their success to support data-driven decision-making.

CONCLUSION

Transforming HIEs into HDUs is essential to realizing the vision of a distributed and connected healthcare data system. Public funding is critical for this model's success, similar to the continued investment in the national highway system.

Implementation and Impact of Intimate Partner Violence Screening Expansion in the Veterans Health Administration: Protocol for a Mixed Methods Evaluation

BACKGROUND

Intimate partner violence (IPV) is a significant public health problem with far-reaching consequences. The health care system plays an integral role in the detection of and response to IPV. Historically, the majority of IPV screening initiatives have targeted women of reproductive age, with little known about men's IPV screening experiences or the impact of screening on men's health care. The Veterans Health Administration (VHA) has called for an expansion of IPV screening, providing a unique opportunity for a large-scale evaluation of IPV screening and response across all patient populations.

OBJECTIVE

In this protocol paper, we describe the recently funded Partnered Evaluation of Relationship Health Innovations and Services through Mixed Methods (PRISM) initiative, aiming to evaluate the implementation and impact of the VHA's IPV screening and response expansion, with a particular focus on identifying potential gender differences.

METHODS

The PRISM Initiative is guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) and Consolidated Framework for Implementation Research (CFIR 2.0) frameworks. We will use mixed methods data from 139 VHA facilities to evaluate the IPV screening expansion, including electronic health record data and qualitative interviews with patients, clinicians, and national IPV program leadership. Quantitative data will be analyzed using a longitudinal observational design with repeated measurement periods at baseline (T0), year 1 (T1), and year 2 (T2). Qualitative interviews will focus on identifying multilevel factors, including potential implementation barriers and facilitators critical to IPV screening and response expansion, and examining the impact of screening on patients and clinicians.

RESULTS

The PRISM initiative was funded in October 2023. We have developed the qualitative interview guides, obtained institutional review board approval, extracted quantitative data for baseline analyses, and began recruitment for qualitative interviews. Reports of progress and results will be made available to evaluation partners and funders through quarterly and end-of-year reports. All data collection and analyses across time points are expected to be completed in June 2026.

CONCLUSIONS

Findings from this mixed methods evaluation will provide a comprehensive understanding of IPV screening expansion at the VHA, including the implementation and impact of screening and the scope of IPV detected in the VHA patient population. Moreover, data generated by this initiative have critical policy and clinical practice implications in a national health care system.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/59918.

Examining incidence of diabetes in people with HIV: tracking the shift in traditional and HIV-related risk factors

BACKGROUND AND OBJECTIVE

The risk factors of diabetes mellitus (DM) in people with HIV (PWH) may be dynamic in a life course manner. This study aimed to describe incidence of DM and investigate the trajectory of changes in risk factor associated with DM incidence over around 15 years among a statewide cohort of PWH in South Carolina (SC).

DESIGN

This is a population-based cohort study.

METHODS

Data were retrieved from the integrated statewide electronic health records between 2006 and 2020 in SC. Separate subgroup analysis was conducted according to the patients' different follow up duration (i.e., 5, 10, and 15 years) to observe the evolving risk factors of DM development, using multivariable logistic regressions.

RESULTS

The DM incidence among a total of 9115 PWH was 8.9 per 1000 person-years. In the overall model, being >60 years old, hypertension, and obesity were positively associated with DM while alcohol consumption, years of HIV diagnosis and high percentage days of viral suppression were negatively associated with the outcome. In the subgroup analyses, similar risk factors were observed. The odds of DM increased in a graded fashion with age. Hypertension was positively associated with DM in all groups and retention to care was negatively associated with the outcome in groups 1 and 3.

CONCLUSION

This large-scale population-based study has revealed a relatively lower incidence of DM among PWH than some other US States. The evolving risk factors over time underline the need for maintaining retention to care to prevent the occurrence of DM.

Development and implementation of an automated electronic health record-linked registry for emergency general surgery

INTRODUCTION

Despite adoption of the emergency general surgery (EGS) service by hospitals nationally, quality improvement (QI) and research for this patient population are challenging because of the lack of population-specific registries. Past efforts have been limited by difficulties in identifying EGS patients within institutions and labor-intensive approaches to data capture. Thus, we created an automated electronic health record (EHR)-linked registry for EGS.

METHODS

We built a registry within the Epic EHR at University of California San Diego for the EGS service. Existing EHR labels that identified patients seen by the EGS team were used to create our automated inclusion rules. Registry validation was performed using a retrospective cohort of EGS patients in a 30-month period and a 1-month prospective cohort. We created quality metrics that are updated and reported back to clinical teams in real time and obtained aggregate data to identify QI and research opportunities. A key metric tracked is clinic schedule rate, as we care that discontinuity postdischarge for the EGS population remains a challenge.

RESULTS

Our registry captured 1,992 patient encounters with 1,717 unique patients in the 30-month period. It had a false-positive EGS detection rate of 1.8%. In our 1-month prospective cohort, it had a false-positive EGS detection rate of 0% and sensitivity of 85%. For quality metrics analysis, we found that EGS patients who were seen as consults had significantly lower clinic schedule rates on discharge compared with those who were admitted to the EGS service (85% vs. 60.7%, p < 0.001).

CONCLUSION

An EHR-linked EGS registry can reliably conduct capture data automatically and support QI and research.

LEVEL OF EVIDENCE

Prognostic and epidemiological, level III.

A process to deduplicate individuals for regional chronic disease prevalence estimates using a distributed data network of electronic health records

Introduction

Learning health systems can help estimate chronic disease prevalence through distributed data networks (DDNs). Concerns remain about bias introduced to DDN prevalence estimates when individuals seeking care across systems are counted multiple times. This paper describes a process to deduplicate individuals for DDN prevalence estimates.

Methods

We operationalized a two-step deduplication process, leveraging health information exchange (HIE)-assigned network identifiers, within the Colorado Health Observation Regional Data Service (CHORDS) DDN. We generated prevalence estimates for type 1 and type 2 diabetes among pediatric patients (0-17 years) with at least one 2017 encounter in one of two geographically-proximate DDN partners. We assessed the extent of cross-system duplication and its effect on prevalence estimates.

Results

We identified 218 437 unique pediatric patients seen across systems during 2017, including 7628 (3.5%) seen in both. We found no measurable difference in prevalence after deduplication. The number of cases we identified differed slightly by data reconciliation strategy. Concordance of linked patients' demographic attributes varied by attribute.

Conclusions

We implemented an HIE-dependent, extensible process that deduplicates individuals for less biased prevalence estimates in a DDN. Our null pilot findings have limited generalizability. Overlap was small and likely insufficient to influence prevalence estimates. Other factors, including the number and size of partners, the matching algorithm, and the electronic phenotype may influence the degree of deduplication bias. Additional use cases may help improve understanding of duplication bias and reveal other principles and insights. This study informed how DDNs could support learning health systems' response to public health challenges and improve regional health.

LV Prasad Eye Institute EyeSmart electronic medical record-based analytics of big data: LEAD-Uveitis Report 1: Demographics and clinical features of uveitis in a multi-tier hospital based network in Southern India

Purpose

To describe the demographics and epidemiology of uveitis presenting to a multi-tier ophthalmology hospital network in Southern India.

Methods

Cross-sectional hospital-based study of 19,352 patients with uveitis presenting between March 2012 and August 2018.

Results

In total, 1,734,272 new patients were seen across the secondary and tertiary centers of our multi-tier ophthalmology hospital network during the study period. Among them, 25,353 eyes of 19,352 patients were diagnosed with uveitis and were included in the study. Uveitis constituted 1.11% of all cases. The majority of patients were male (60.33%) and had unilateral (68.09%) affliction. The most common age group was 21-50 years with 12,204 (63.06%) patients. The most common type of uveitis was anterior uveitis, which was seen in 7380 (38.14%) patients, followed by posterior uveitis in 5397 (23.89%) patients. Among the infectious causes, tuberculosis was the most common etiology (2551 patients, 13%) followed by toxoplasmosis (1147 patients, 6%).

Conclusion

Uveitis constituted 1.11% of all cases presenting to our clinics. It was more common in the age group of 21-50 and was predominantly unilateral. Anterior uveitis was the most common subtype seen in 38%.

Practical use of electronic health records among patients with diabetes in scientific research

Electronic health (medical) records, which are also considered as patients’ information that are routinely collected, provide a great chance for researchers to develop an epidemiological understanding of disease. Electronic health records systems cannot develop without the advance of computer industries. While conducting clinical trials that are always costly, feasible and reasonable analysis of routine patients’ information is more cost-effective and reflective of clinical practice, which is also called real world study. Real world studies can be well supported by big data in healthcare industry. Real world studies become more and more focused and important with the development of evidence-based medicine. These big data will definitely help in making decisions, making policies and guidelines, monitoring of effectiveness and safety on new drugs or technologies. Extracting, cleaning, and analyzing such big data will be a great challenge for clinical researchers. Successful applications and developments of electronic health record in western countries (eg, disease registries, health insurance claims, etc) have provided a clear direction for Chinese researchers. However, it is still at primary stages in China. This review tries to provide a full perspective on how to translate the electronic health records into scientific achievements, for example, among patients with diabetes. As a summary in the end, resource sharing and collaborations are highly recommended among hospitals and healthcare groups.

Electronic health record-based disease surveillance systems: A systematic literature review on challenges and solutions

OBJECTIVE

Disease surveillance systems are expanding using electronic health records (EHRs). However, there are many challenges in this regard. In the present study, the solutions and challenges of implementing EHR-based disease surveillance systems (EHR-DS) have been reviewed.

MATERIALS AND METHODS

We searched the related keywords in ProQuest, PubMed, Web of Science, Cochrane Library, Embase, and Scopus. Then, we assessed and selected articles using the inclusion and exclusion criteria and, finally, classified the identified solutions and challenges.

RESULTS

Finally, 50 studies were included, and 52 unique solutions and 47 challenges were organized into 6 main themes (policy and regulatory, technical, management, standardization, financial, and data quality). The results indicate that due to the multifaceted nature of the challenges, the implementation of EHR-DS is not low cost and easy to implement and requires a variety of interventions. On the one hand, the most common challenges include the need to invest significant time and resources; the poor data quality in EHRs; difficulty in analyzing, cleaning, and accessing unstructured data; data privacy and security; and the lack of interoperability standards. On the other hand, the most common solutions are the use of natural language processing and machine learning algorithms for unstructured data; the use of appropriate technical solutions for data retrieval, extraction, identification, and visualization; the collaboration of health and clinical departments to access data; standardizing EHR content for public health; and using a unique health identifier for individuals.

CONCLUSIONS

EHR systems have an important role in modernizing disease surveillance systems. However, there are many problems and challenges facing the development and implementation of EHR-DS that need to be appropriately addressed.

Big data and the eyeSmart electronic medical record system - An 8-year experience from a three-tier eye care network in India

Purpose

To assess the demographic details and distribution of ocular disorders in patients presenting to a three-tier eye care network in India using electronic medical record (EMR) systems across an 8-year period using big data analytics.

Methods

An 8-year retrospective review of all the patients who presented across the three-tier eye care network of L.V. Prasad Eye Institute was performed from August 2010 to August 2018. Data were retrieved using an in-house eyeSmart EMR system. The demographic details and clinical presentation and ocular disease profile of all the patients were analyzed in detail.

Results

In an 8-year period, a total of 2,270,584 patients were captured on the EMR system with 4,730,221 consultations. More than half of the patients presented at tertiary centers (n = 1,174,643, 51.73%), a quarter at the secondary centers (n = 564,251, 24.85%) followed by the vision centers (n = 531,690, 23.42%). The ratio of males and females was 1.18:1. Most common states of presentation were Andhra Pradesh (n = 1,103,733, 48.61%) and Telangana (n = 661,969, 29.15%). In total, 3,721,051 ocular diagnosis instances were documented in the patients. Most common ocular disorders were related to cornea and anterior segment (n = 1,347,754, 36.22%) followed by refractive error (n = 1,133,078, 30.45%).

Conclusion

This study depicts the demographic details and distribution of various ocular disorders in a very large cohort of patients. There is a need to adopt digitization in geographies that cater to large populations to enable insightful research. The implementation of EMR systems enables structured data for research purposes and the development of real-time analytics for the same.

The Longitudinal Epidemiologic Assessment of Diabetes Risk (LEADR): Unique 1.4 M patient Electronic Health Record cohort

BACKGROUND

The Longitudinal Epidemiologic Assessment of Diabetes Risk (LEADR) study uses a novel Electronic Health Record (EHR) data approach as a tool to assess the epidemiology of known and new risk factors for type 2 diabetes mellitus (T2DM) and study how prevention interventions affect progression to and onset of T2DM. We created an electronic cohort of 1.4 million patients having had at least 4 encounters with a healthcare organization for at least 24-months; were aged ≥18 years in 2010; and had no diabetes (i.e., T1DM or T2DM) at cohort entry or in the 12 months following entry. EHR data came from patients at nine healthcare organizations across the U.S. between January 1, 2010-December 31, 2016.

RESULTS

Approximately 5.9% of the LEADR cohort (82,922 patients) developed T2DM, providing opportunities to explore longitudinal clinical care, medication use, risk factor trajectories, and diagnoses for these patients, compared with patients similarly matched prior to disease onset.

CONCLUSIONS

LEADR represents one of the largest EHR databases to have repurposed EHR data to examine patients' T2DM risk. This paper is first in a series demonstrating this novel approach to studying T2DM.

IMPLICATIONS

Chronic conditions that often take years to develop can be studied efficiently using EHR data in a retrospective design.

LEVEL OF EVIDENCE

While much is already known about T2DM risk, this EHR's cohort's 160 M data points for 1.4 M people over six years, provides opportunities to investigate new unique risk factors and evaluate research hypotheses where results could modify public health practice for preventing T2DM.

Applying population health approaches to improve safe anticoagulant use in the outpatient setting: the DOAC Dashboard multi-cohort implementation evaluation study protocol

BACKGROUND

Use of direct oral anticoagulants (DOAC) is rapidly growing for treatment of atrial fibrillation and venous thromboembolism. However, incorrect dosing of these medications is common and puts patients at risk of adverse drug events. One way to improve safe prescribing is the use of population health tools, including interactive dashboards built into the electronic health record (EHR). As such tools become more common, exploring ways to understand which aspects are effective in specific settings and how to effectively adapt and implement in existing anticoagulation clinics across different health systems is vital.

METHODS

This three-phase project will evaluate a current nation-wide implementation effort of the DOAC Dashboard in the Veterans Health Administration (VHA) using both quantitative and qualitative methods. Informed by this evaluation, the DOAC Dashboard will be implemented in four new health systems using an implementation strategy derived from the VHA experience and interviews with providers in those new health systems. Quantitative evaluation of the VHA and non-VHA implementation will follow the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework. Qualitative interviews with stakeholders will be analyzed using the Consolidated Framework for Implementation Research and Technology Acceptance Models to identify key determinants of implementation success.

DISCUSSION

This study will (1) evaluate the implementation of an EHR-based population health tool for medication management within a large, nation-wide, highly integrated health system; (2) guide the adoption in a set of four different health systems; and (3) evaluation that multi-center implementation effort. These findings will help to inform future EHR-based implementation efforts in a wide variety of health care settings.

Development of Patient Databases for Endocrinological Clinical and Pharmaceutical Trials: A Survey

BACKGROUND

According to European legislation, a clinical trial is a research involving patients, which also includes a research end-product. The main objective of the clinical trial is to prove that the research product, i.e. a proposed medication or treatment, is effective and safe for patients. The implementation, development, and operation of a patient database, which will function as a matrix of samples with the appropriate parameterization, may provide appropriate tools to generate samples for clinical trials.

AIMS

The aim of the present work is to review the literature with respect to the up-to-date progress on the development of databases for clinical trials and patient recruitment using free and open-source software in the field of endocrinology.

METHODS

An electronic literature search was conducted by the authors from 1984 to June 2019. Original articles and systematic reviews selected, and the titles and abstracts of papers screened to determine whether they met the eligibility criteria, and full texts of the selected articles were retrieved.

RESULTS

The present review has indicated that the electronic health records are related with both the patient recruitment and the decision support systems in the domain of endocrinology. The free and open-source software provides integrated solutions concerning electronic health records, patient recruitment, and the decision support systems.

CONCLUSION

The patient recruitment relates closely to the electronic health record. There is maturity at the academic and research level, which may lead to good practices for the deployment of the electronic health record in selecting the right patients for clinical trials.

Electronic primary dental care records in research: A case study of validation and quality assurance strategies

BACKGROUND

In dentistry, the use of electronic patient records for research is underexplored. The aim of this paper is to describe a case study process of obtaining research data (sociodemographic, clinical and workforce) from electronic primary care dental records, and outlining data cleaning and validation strategies. This study was undertaken at the University of Portsmouth Dental Academy (UPDA), which is a centre of education, training and provision of state funded services (National Health Services). UPDA's electronic patient management system is R4/Clinical +. This is a widely used system in general dental practices in the UK.

METHOD

A two-phase process, involving first Pilot and second Main data extraction were undertaken. Using System Query Language (SQL), data extracts containing variables related to patients' demography, socio-economic status and dental care received were generated. A data cleaning and validation exercise followed, using a combination of techniques including Maletic and Marcus's (2000) general framework for data cleaning and Rahm and Haido's (2010) principles of data cleaning.

RESULTS

The findings of the case study support the use of a two-phase data extraction process. The data validation processes highlighted the need for both manual and analytical strategies when cleaning these data. Finally, the process demonstrated that electronic dental records can be validated and used for epidemiological and heath service research. The potential to generalise findings is great due to the large number of records. There are, however, limitations to the data which need to be considered, relating to quality (data input), database structure and interpretation of data codes.

CONCLUSION

Electronic dental records are useful in health service research, epidemiological studies and skill mix research. Researchers should work closely with clinicians, managers and software developers to ensure that the data generated are accurate, valid and generalisable. Following data extraction the researchers need to adapt stringent validation and data cleaning strategies to guarantee that the extracted electronic data are accurate.

Assessing variability in compliance with recommendations given by the International Diabetes Federation (IDF) for patients with type 2 diabetes in primary care using electronic records. The APNA study

OBJECTIVE

Assess compliance with the IDF recommendations for patients with Diabetes Type2 (DM2), and its variability, by groups of doctors and nurses who provide primary care services in Navarre (Spain).

MATERIALS AND METHODOLOGIES

A cross-sectional study of a population of 462,568 inhabitants, aged ≥18 years in 2013, attended by 381 units of doctor/nurse (quota). Clinical data were collected retrospectively through electronic records. Using cluster analysis, we identified two groups of units according to the score for each indicator. We calculated the Odds Ratio, adjusted for age sex, BMI, socioeconomic status and smoking, for complying with each recommendation whether a patient was treated by one of the quota from the highest score to the lowest. 30,312 patients with DM2 were identified: prevalence: 6.39%; coefficient of variation between UDN: 22.8%; biggest cluster 7.7% and smallest 5.3%; OR=1.54 (1.50-1.58). The HbA1c control at ≤8% was 82.8% (82.2-83.3) and >9% was 7.6% (7.3-8.0), with OR 1.79 (1.69-1.89) and 2.62 (2.36-2.91) respectively. Control of BP and LDL-C show significant differences between the clusters.

CONCLUSIONS

An important variability was identified according to the doctor treating patients. The average HbA1c control is acceptable being limited in BP and LDL-C.

Digital health technology and diabetes management

Diabetes care is largely dependent on patient self-management and empowerment, given that patients with diabetes must make numerous daily decisions as to what to eat, when to exercise, and determine their insulin dose and timing if required. In addition, patients and providers are generating vast amounts of data from many sources, including electronic medical records, insulin pumps, sensors, glucometers, and other wearables, as well as evolving genomic, proteomic, metabolomics, and microbiomic data. Multiple digital tools and apps have been developed to assist patients to choose wisely, and to enhance their compliance by using motivational tools and incorporating incentives from social media and gaming techniques. Healthcare teams (HCTs) and health administrators benefit from digital developments that sift through the enormous amounts of patient-generated data. Data are acquired, integrated, analyzed, and presented in a self-explanatory manner, highlighting important trends and items that require attention. The use of decision support systems may propose data-driven actions that, for the most, require final approval by the patient or physician before execution and, once implemented, may improve patient outcomes. The digital diabetes clinic aims to incorporate all digital patient data and provide individually tailored virtual or face-to-face visits to those persons who need them most. Digital diabetes care has demonstrated only modest HbA1c reduction in multiple studies and borderline cost-effectiveness, although patient satisfaction appears to be increased. Better understanding of the barriers to digital diabetes care and identification of unmet needs may yield improved utilization of this evolving technology in a safe, effective, and cost-saving manner.

Data collection of patients with diabetes in family medicine: a study in north-eastern Italy

Background

Studies on data collection and quality of care in Italian family medicine are lacking. The aim of this study was to assess the completeness of data collection of patients with diabetes in a large sample of family physicians in the province of Verona, Veneto region, a benchmark for the Italian National Health System.

Methods

We extracted the data on all the patients with diabetes from the electronic health records of 270 family physicians in 2006 and 2009. We reported the percentage of patients with data recorded for 12 indicators of performance derived from the National Institute for Clinical Excellence diabetes guidelines. Secondarily, we assessed quality of care using the Q-score (the lower the score, the greater the risk of cardiovascular events).

Results

Patients with diabetes were 18,507 in 2006 and 20,744 in 2009, and the percentage of patients registered as having diabetes was 4.9% and 5.4% of the total population, respectively (p < 0.001). Data collection improved for all the indicators between 2006 and 2009 but the performance was still low at the end of the study period: patients with no data recorded were 42% in 2006 and 32% in 2009, while patients with data recorded for ≥5 indicators were 9% in 2006 and 17% in 2009. The Q-score improved (mean ± SD, 20.7 ± 3.0 in 2006 vs 21.3 ± 3.6 in 2009, p < 0.001) but most patients were at increased risk of cardiovascular events in both years (Q-score ≤ 20).

Conclusions

We documented an improvement in data collection and quality of care for patients with diabetes during the study period. Nonetheless, data collection was still unsatisfactory in comparison with international benchmarks in 2009. Structural interventions in the organization of family medicine, which have not been implemented since the study period, should be prioritised in Italy.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-017-2508-5) contains supplementary material, which is available to authorized users.

Incorporação de Tecnologias de Informação e Comunicação e qualidade na atenção básica em saúde no Brasil

Resumo: As Tecnologias de Informação e Comunicação (TIC) - meios para tratar informação e agilizar comunicação - contribuem para o cuidado. Este artigo descreve a incorporação de TIC na atenção básica e sua associação com a qualidade, utilizando Programa Nacional de Melhoria do Acesso e da Qualidade da Atenção Básica (PMAQ). É um estudo transversal. O universo englobou 17.053 equipes. Criou-se o Índice de Incorporação de Tecnologias de Informação e Comunicação (ITIC) englobando: infraestrutura, sistemas e utilização de informação. Para as associações, realizou-se análise de regressão. Somente 13,5% das equipes possuem grau alto de TIC. É na utilização da informação que se observou a maior força de associação. As TIC contribuem para a melhoria da qualidade da atenção básica.

Health Systems Approaches to Diabetes Screening and Prevention in Women with a History of Gestational Diabetes

Gestational diabetes (GDM) is associated with a high risk of future type 2 diabetes. Despite multiple clinical guidelines highlighting the importance of screening in this high-risk population, many health systems report that fewer than 50 % of eligible women are screened in the postpartum period, and little is known about screening beyond the first postpartum year. Systems-level approaches to screening for and prevention of type 2 diabetes in women with a history of GDM are therefore an opportunity for quality improvement. This review will discuss the literature on interventions to improve screening at the systems level and highlight successful strategies as well as gaps in the existing literature. Future directions for intervention research are suggested.

National Strategies to Decrease the Burden of Diabetes and Its Complications

Comparative results from national strategies for diabetes care and prevention are needed to understand the impact and barriers encountered during the implementation process. Long-term outcomes are limited, but results on intermediate outcomes and processes of diabetes care measures are available from translational research studies. In this narrative review, we highlight programs with nationwide reach, targeting various ethnic, racial, and socioeconomic populations with diabetes. We describe the implementation strategies, the impact on clinical outcomes, specific barriers, and cost-effectiveness results of national efforts aimed at improving diabetes care and prevention in the USA.

Regularity of follow-up, glycemic burden, and risk of microvascular complications in patients with type 2 diabetes: a 9-year follow-up study

AIMS

To assess the relationship between regularity of follow-up and risk of complications in patients with type 2 diabetes (T2DM) followed up for 9 years at a tertiary diabetes center in India.

METHODS

We compared glycemic burden [cumulative time spent above a HbA1c of 53 mmol/mol (7 %)] and incidence of diabetes complications (retinopathy, neuropathy, nephropathy, peripheral arterial disease, coronary heart disease) between 1,783 T2DM patients with "regular follow-up" (minimum of three visits and two HbA1c tests every year from 2003 to 2012), and 1,798 patients with "irregular follow-up" (two visits or less and one HbA1c or less per year during the same time period), retrospectively identified from medical records. Cox proportional hazards models were used to estimate risk associated with diabetes complications.

RESULTS

Compared to those with regular follow-up, the irregular follow-up group had significantly higher mean fasting and postprandial plasma glucose, HbA1c, glycemic burden, total and LDL cholesterol, and triglycerides at every time point during the 9 years of follow-up. Those with irregular follow-up had double the total and mean monthly glycemic burden and 1.98 times higher risk of retinopathy (95 % CI 1.62, 2.42) and 2.11 times higher risk of nephropathy (95 % CI 1.73, 2.58) compared to those with regular follow-up, even after adjusting for time-varying confounding variables. Complications tended to develop significantly earlier and were more severe in those with irregular follow-up.

CONCLUSION

Among patients with type 2 diabetes, regular follow-up was associated with significantly lower glycemic burden and lower incidence of retinopathy and nephropathy over a 9-year period.

Supporting Practices to Adopt Registry-Based Care (SPARC): protocol for a randomized controlled trial

BACKGROUND

Diabetes is predicted to increase in incidence by 42% from 1995 to 2025. Although most adults with diabetes seek care from primary care practices, adherence to treatment guidelines in these settings is not optimal. Many practices lack the infrastructure to monitor patient adherence to recommended treatment and are slow to implement changes critical for effective management of patients with chronic conditions. Supporting Practices to Adopt Registry-Based Care (SPARC) will evaluate effectiveness and sustainability of a low-cost intervention designed to support work process change in primary care practices and enhance focus on population-based care through implementation of a diabetes registry.

METHODS

SPARC is a two-armed randomized controlled trial (RCT) of 30 primary care practices in the Virginia Ambulatory Care Outcomes Research Network (ACORN). Participating practices (including control groups) will be introduced to population health concepts and tools for work process redesign and registry adoption at a meeting of practice-level implementation champions. Practices randomized to the intervention will be assigned study peer mentors, receive a list of specific milestones, and have access to a physician informaticist. Peer mentors are clinicians who successfully implemented registries in their practices and will help champions in the intervention practices throughout the implementation process. During the first year, peer mentors will contact intervention practices monthly and visit them quarterly. Control group practices will not receive support or guidance for registry implementation. We will use a mixed-methods explanatory sequential design to guide collection of medical record, participant observation, and semistructured interview data in control and intervention practices at baseline, 12 months, and 24 months. We will use grounded theory and a template-guided approach using the Consolidated Framework for Implementation Research to analyze qualitative data on contextual factors related to registry adoption. We will assess intervention effectiveness by comparing changes in patient-level hemoglobin A1c scores from baseline to year 1 between intervention and control practices.

DISCUSSION

Findings will enhance our understanding of how to leverage existing practice resources to improve diabetes care in primary care practices by implementing and using a registry. SPARC has the potential to validate the effectiveness of low-cost implementation strategies that target practice change in primary care.

TRIAL REGISTRATION

NCT02318108.

Developing a communitywide electronic health record disease registry in primary care practices: lessons learned from the Western new york beacon community

BACKGROUND AND INTRODUCTION

Disease registries, as part of electronic health records (EHRs), have shown promise in improving care and outcomes. However, little is known about how best to implement them across communities, especially in communities that are not highly integrated. The Western New York (WNY) primary care community consists largely of independent practices using at least 20 different EHR products. This paper discusses the processes undertaken to develop a communitywide EHR disease registry in WNY, improvements it engendered, barriers overcome, and the lessons learned.

METHODS

HEALTHeLINK, under the Office of the National Coordinator for Health Information Technology Beacon Community Initiative, reached out to 98 primary care practices in the WNY region to establish EHR-based diabetes registries. Working with practices, community partners, and vendors, registry specifications were created. The registry was piloted with practices using one local vendor's EHR product and then rolled out to other practices, including five other EHR products. Using identified and de-identified registry datasets, quality benchmarking within and between practices and population health management were undertaken.

FINDINGS

From 2011 to 2013, the WNY Beacon Community assisted 98 practices (344 providers) serving over 50,000 adult diabetic patients. A major focus was on EHR registry development across diverse systems, and overcoming the challenges this presented. The Beacon diabetes registry was implemented at 85 of the 98 targeted practices. Of these registries, 65 met the criteria described in a later section for quality benchmarking and population health management purposes. Practices received quarterly benchmark reports summarizing their performance on key diabetes quality metrics and were compared to community practice averages. Practices used their registries for population health management by identifying and targeting patients in need of follow-up or specific diabetes-related care.

DISCUSSION AND CONCLUSION

The creation of the registry infrastructure required unified registry technical specifications as well as close collaboration between all parties involved. The WNY experience showed that a useful disease registry can be established in a community largely consisting of numerous disparate primary care practices. This laid the groundwork for the future use of EHR data for a variety of purposes in the community. The methods used and lessons learned through this endeavor may benefit other communities in a similar position, with several disconnected EHRs, to establish unified registries.