Design of a National Distributed Health Data Network

Maro, Judith C.; Platt, Richard; Holmes, John H.; Strom, Brian L.; Hennessy, Sean; Lazarus, Ross; Brown, Jeffrey S.

doi:10.7326/0003-4819-151-5-200909010-00139

Cited by 151 publications

(121 citation statements)

References 22 publications

Supporting

Mentioning

119

Contrasting

Order By: Relevance

“…Other foundational principles of the Mini-Sentinel Distributed Database include that only the minimum necessary amount of data leave the data partner sites, data security is maintained, local control is optimized, and confidentiality of access and use of each organization' s data are maintained for network actvities. [20][21][22] The Mini-Sentinel common data model includes linked tables of patient enrollment, demographic, diagnoses, procedures, and outpatient pharmacy claims data from a source US population exceeding 150 million individual records. 17,18,23 The data tables in the Mini-Sentinel Distributed Database currently are obtained from the source data of 18 data partners, including large US health insurance plans and integrated health care delivery systems.…”

Section: Methodsmentioning

confidence: 99%

Adherence to Guidelines for Glucose Assessment in Starting Second-Generation Antipsychotics

Raebel

Penfold

McMahon³

et al. 2014

Pediatrics

View full text Add to dashboard Cite

OBJECTIVES: In 2003, the US Food and Drug Administration issued warnings about hyperglycemia and diabetes with second-generation antipsychotics (SGAs); guidelines have recommended metabolic screening since 2004. However, little is known of contemporary practices of glucose screening among youth initiating SGAs. Our objective was to evaluate baseline glucose assessment among youth in the Mini-Sentinel Distributed Database starting an SGA. METHODS: The cohort included youth ages 2 through 18 newly initiating SGAs January 1, 2006, through December 31, 2011, across 10 sites. Baseline glucose was defined as fasting/random glucose or hemoglobin A1c (GLU) measurement occurring relative to first SGA dispensing. Differences in GLU assessment were evaluated with χ2 tests and logistic regression. RESULTS: The cohort included 16 304 youth; 60% boys; mean age 12.8 years. Risperidone was most commonly started (43%). Eleven percent (n = 1858) had GLU assessed between 90 days before and 3 days after first dispensing. Assessment varied across SGAs (olanzapine highest), sites (integrated health care systems higher), ages (16–18 highest), years (2007 highest), and gender (female higher; all P < .001). GLU assessment among those starting olanzapine was more likely than among those starting quetiapine (odds ratio [OR]: 1.72 [95% confidence interval (CI): 1.37–2.18]), aripiprazole (OR: 1.49 [95% CI: 1.18–1.87]), or risperidone (OR: 1.61 [95% CI: 1.28–2.03]). CONCLUSIONS: Few children and adolescents starting SGA have baseline glucose assessed. This is concerning because those at high diabetes risk may not be identified. Further, lack of screening impedes determining the contribution of SGAs to hyperglycemia development.

show abstract

Section: Methodsmentioning

confidence: 99%

Adherence to Guidelines for Glucose Assessment in Starting Second-Generation Antipsychotics

Raebel

Penfold

McMahon³

et al. 2014

Pediatrics

View full text Add to dashboard Cite

show abstract

“…Pediatric research programs that historically developed customized and unique data definitions, data collection forms, and data management systems are yielding to reusable and sharable informatics infrastructures that support a wide range of clinical research programs. [1][2][3] Similar approaches for collecting and reusing detailed clinical data also support nationalperformanceimprovementcollaboratives and disease-specific registries. [4][5][6][7] These new informatics tools can expand the size, scope, and reach of child health research by connecting to clinical settings that previously were unable to participate.…”

mentioning

confidence: 99%

Building a Common Pediatric Research Terminology for Accelerating Child Health Research

et al. 2014

View full text Add to dashboard Cite

Longitudinal observational clinical data on pediatric patients in electronic format is becoming widely available. A new era of multiinstitutional data networks that study pediatric diseases and outcomes across disparate health delivery models and care settings are also enabling an innovative collaborative rapid improvement paradigm called the Learning Health System. However, the potential alignment of routine clinical care, observational clinical research, pragmatic clinical trials, and health systems improvement requires a data infrastructure capable of combining information from systems and workflows that historically have been isolated from each other. Removing barriers to integrating and reusing data collected in different settings will permit new opportunities to develop a more complete picture of a patient' s care and to leverage data from related research studies. One key barrier is the lack of a common terminology that provides uniform definitions and descriptions of clinical observations and data. A well-characterized terminology ensures a common meaning and supports data reuse and integration. A common terminology allows studies to build upon previous findings and to reuse data collection tools and data management processes. We present the current state of terminology harmonization and describe a governance structure and mechanism for coordinating the development of a common pediatric research terminology that links to clinical terminologies and can be used to align existing terminologies. By reducing the barriers between clinical care and clinical research, a Learning Health System can leverage and reuse not only its own

show abstract

“…They can become information silos, delaying the ability to answer important public health questions on the natural history of the disease, class-wide effects, or comparative benefits and risks. 4,5 One remedy is to create a distributed electronic health data network, [6][7][8] which can increase sample size without centralizing or combining patient-level data.…”

Section: Introductionmentioning

confidence: 99%

“…These extract databases remain under the institution's direct control, mitigating legal, proprietary, privacy, and security concerns with respect to dealing with privately held, protected, and identifiable patient data. [6][7][8]25 The extract databases adhere to a common data model, including identical file structures, data fields, and coding systems.…”

Section: Introductionmentioning

confidence: 99%

Orphan Therapies: Making Best Use of Postmarket Data

2014

Self Cite

View full text Add to dashboard Cite

BACKGROUND: Postmarket surveillance of the comparative safety and efficacy of orphan therapeutics is challenging, particularly when multiple therapeutics are licensed for the same orphan indication. To make best use of product-specific registry data collected to fulfill regulatory requirements, we propose the creation of a distributed electronic health data network among registries. Such a network could support sequential statistical analyses designed to detect early warnings of excess risks. We use a simulated example to explore the circumstances under which a distributed network may prove advantageous. METHODS: We perform sample size calculations for sequential and non-sequential statistical studies aimed at comparing the incidence of hepatotoxicity following initiation of two newly licensed therapies for homozygous familial hypercholesterolemia. We calculate the sample size savings ratio, or the proportion of sample size saved if one conducted a sequential study as compared to a non-sequential study. Then, using models to describe the adoption and utilization of these therapies, we simulate when these sample sizes are attainable in calendar years. We then calculate the analytic calendar time savings ratio, analogous to the sample size savings ratio. We repeat these analyses for numerous scenarios. KEY RESULTS: Sequential analyses detect effect sizes earlier or at the same time as non-sequential analyses. The most substantial potential savings occur when the market share is more imbalanced (i.e., 90 % for therapy A) and the effect size is closest to the null hypothesis. However, due to low exposure prevalence, these savings are difficult to realize within the 30-year time frame of this simulation for scenarios in which the outcome of interest occurs at or more frequently than one event/ 100 person-years. CONCLUSIONS: We illustrate a process to assess whether sequential statistical analyses of registry data performed via distributed networks may prove a worthwhile infrastructure investment for pharmacovigilance.

show abstract

Design of a National Distributed Health Data Network

Cited by 151 publications

References 22 publications

Adherence to Guidelines for Glucose Assessment in Starting Second-Generation Antipsychotics

Adherence to Guidelines for Glucose Assessment in Starting Second-Generation Antipsychotics

Building a Common Pediatric Research Terminology for Accelerating Child Health Research

Orphan Therapies: Making Best Use of Postmarket Data

Contact Info

Product

Resources

About