Topic Abstract

Detailed clinical data can improve the quality of observational comparative effectiveness research (OCER) and enhance the quality of clinical care. The Distributed Ambulatory Research in Therapeutics Network (DARTNet) prototype has demonstrated that clinical data can be extracted from, and standardized across, multiple electronic health records (EHRs). DARTNet continues to explore how clinical data from an electronic health record (EHR) can be complemented by other data including medication fulfillment data and data obtained at the point of care to enhance OCER.

The current version of DARTNet is a federated electronic health data network comprised of nine organizations representing over 500 clinicians and over 400,000 patients. This network links geographically and organizationally separate databases in such a way that a single query can run on the separate databases and return results while conforming to each organization’s privacy and confidentiality standards.

The expansion of DARTNet will move it from a prototype to a production system. This means the software will be stabilized and deployed in a production mode. The increase in size and the expansion of the types of data, including greater inclusion of drug fulfillment data and routine capture of billing data, will support the conduct of various future OCER studies.

This project has the following specific aims:

Aim 1: DARTNet expansion:

• Include more primary care practices with a focus on general pediatrics.
• Increase the number and types of clinical specialists.
• Increase the number and types of health care delivery organizations (e.g., hospitals, emergency departments, insurance companies).
• Access additional data including medication fulfillment data, diagnostic data, claims and point-of-care data.

Aim 2: Evaluate alternative data extraction and data sharing arrangements which include:

• Data extraction processes that do not require the independent purchase of clinical decision support software.
• Compare alternative point of care data collection processes.
• Improve the ability to define episodes of illness to evaluate therapeutic outcomes.

Aim 3: Conduct an observational comparative effectiveness and safety study of different therapies for major depression. This will be conducted in three phases:

Phase 1: Claims Database Study

Utilization - Conduct population-based cohort studies of new episodes of major depression using commercially available national claims data. Describe the patterns of use of antidepressants and combinations of other psychotropic drugs, both alone and in combination with psychotherapy. Examine patterns of treatment utilization for pediatric and adult populations separately, and by primary care versus specialty mental health care for the diagnosis of major depression, and prescription of psychotropics.

Comparative Effectiveness of Treatments for Major Depression – Compare the responsiveness and remission rates for major depression treatments, beginning with claims-based data that will be enhanced with clinical data from DARTNet.

Comparative Safety of Antidepressants - Compare rates of adverse drug events to include those causing treatment discontinuation, and suicidality, including ideation and attempts.

Phase 2: DARTNet EHR Database Study

The second phase will compare the findings of the claims database with those from the clinically-enriched DARTNet database.

Phase 3: DARTNet Point-of-Care Data Collection and Practice Facilitation

The third phase of the project will test the point of care (POC) data collection capabilities of DARTNet. This will include assessing PHQ-2 and PHQ-9 scores in eligible patients. POC data will be used in two ways: (1) as a method to establish the start and end dates of depression episodes, to be compared and contrasted with HEDIS definitions and empirically derived approaches; and (2) as additional outcome measures (i.e., changes in overall PHQ-9 scores for the effectiveness aim and in the suicidality-item scores for the safety aim) and covariates (overall PHQ-9 scores as a baseline severity adjustment measure for both the effectiveness and safety aims).

Aim 4: Develop a robust management and governance structure that will support the potential rapid growth of the network and be responsive to various stakeholders with interest in working with the network.