Enhancing Fulfillment Data in Community Practices for Clinical Care and Research (Colorado)

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Summary:

In ambulatory care, there are two major forms of prescription data. Prescribing data represent what clinicians have prescribed for patients—ideally, the intended medication regimen. Fulfillment data represent what patients have received from the pharmacy—the actual medication regimen. Nearly all current medication research uses payer-based medication fulfillment billing records. With the rapid expansion of e-prescribing, electronic health record (EHR) fulfillment data may be more relevant for clinical care and research; however, many questions remain about data accessibility, quality, and utility.

This project evaluated the accessibility and utility of EHR fulfillment data within the Distributed Ambulatory Research in Therapeutics Network (DARTNet). DARTNet is a practice-based network that includes 32 independent and geographically dispersed organizations encompassing more than 1,700 clinicians and 4 million patients.

The specific aims of this project were as follows:

  • Use surveys and interviews to assess the actual status, organizational plans, and barriers for full e-prescribing, capture of fulfillment data, and clinician use of fulfillment data at all DARTNet organizations. 
  • Assess the data’s comprehensiveness and clinical utility in five DARTNet organizations receiving fulfillment data through the e-prescribing-based process, the consent-based process, or both. 
  • Develop and pilot test a patient-level report using clinical, prescribing, and fulfillment data to improve the management of hypertension during the clinical encounter, with subjective assessments of utility by survey and group interviews of clinicians in one DARTNet organization capturing fulfillment data. 

A Web-based survey found that medication fulfillment data was not available at most practices. Interviewees cited the cost of obtaining and integrating medication fulfillment data into the EHR as a barrier. Data extraction identified significant data quality issues originating from a third-party data repository. Lack of confidence in the completeness data severely limited the ability to make conclusions from the available data. Despite barriers to accurate data extraction, a clinical decision support (CDS) tool that alerted physicians to potential lapses in adherence to anti-hypertensive medication was piloted at three DARTNet sites. Although the CDS produced a high number of false positive alerts, providers found the drug adherence decision support pilot valuable and worth expanding.

Enhancing Fulfillment Data in Community Practices for Clinical Care and Research - 2012

Summary Highlights

  • Principal Investigator: 
  • Funding Mechanism: 
    PAR: HS08-269: Exploratory and Developmental Grant to Improve Health Care Quality Through Health Information Technology (IT) (R21)
  • Grant Number: 
    R21 HS 019726
  • Project Period: 
    September 2011 - December 2013
  • AHRQ Funding Amount: 
    $190,455
  • PDF Version: 
    (PDF, 220.04 KB)

Summary: In ambulatory care, there are two major forms of prescription data. Prescribing data represent what clinicians have prescribed for patients—ideally, the intended medication regimen. Fulfillment data represent what patients have received from the pharmacy—the actual medication regimen. While community practices that use electronic prescribing (e-prescribing) are obtaining new access to fulfillment data, many questions about the actual accessibility, comprehensiveness, and utility of these fulfillment data for clinical care and research remain. Better-informed medication management has the potential to improve the quality, safety, and efficiency of the health care system, particularly when there is bidirectional information that includes prescribing and fulfillment data. In a fragmented medical system, providing clinicians with fulfillment data has the potential to improve coordination of care by revealing what other clinicians have prescribed for a patient. It may also help clinicians provide better-informed care by revealing whether a patient has been able to adhere to prescribed drug regimens.

This study strives to extend what occurs at the pharmacy level to include additional information on prescriptions submitted by other providers. The project team has engaged the Distributed Ambulatory Research in Therapeutics Network (DARTNet) to assess and improve the accessibility and utility of fulfillment data in community practices. DARTNet is an electronic practice-based network that includes 32 independent and geographically dispersed organizations encompassing more than 1,700 clinicians and four million patients. Member practices are being surveyed about their use of e-prescribing and the accessibility and utility of fulfillment data in their electronic health records. Fulfillment data is being extracted from five of those practices and assessed for completeness and accuracy. The efficiency of prescribing and fulfillment data to identify unintended continuation of medication and duplication of therapy is also being explored.

Specific Aims:

  • Use surveys and interviews to assess the actual status, organizational plans, and barriers for full e-prescribing, capture of fulfillment data, and clinician use of fulfillment data at all DARTNet organizations. (Ongoing)
  • Assess the data’s comprehensiveness and clinical utility in five DARTNet organizations receiving fulfillment data through the e-prescribing-based process, the consent-based process, or both. (Ongoing)
  • Develop and pilot test a patient-level report used using clinical, prescribing, and fulfillment data to improve the management of hypertension during the clinical encounter, with subjective assessments of utility by survey and group interviews of clinicians in one DARTNet organization capturing fulfillment data. (Ongoing)

2012 Activities: The focus of activity was on fulfillment-data extraction, surveying DARTnet members, communicating with DARTNet coordinators to address challenges, and pilot testing the hypertension report. The extraction of fulfillment data was completed for three of the five sites. The study team prioritized the data extractions from sites that were known to have low percentages of fulfillment records. These sites represent those with fulfillment data from internal pharmacies only, versus additional data from external pharmacy fulfillment. Data extraction for the remaining two sites will be completed in 2013. One site that had agreed to participate in fulfillment data extraction was later found not to be receiving fulfillment data. However, another site that had previously declined the study team’s request for data extraction later agreed to participate after the project team arranged for the vendor to perform the data validation step, consisting of chart reviews, under a blinded protocol.

As last self-reported in the AHRQ Research Reporting System, project progress and activities are on track in some respect but not others, and project budget spending is on target. The project team encountered many challenges in 2012 during the initial phase of data analysis, such as finding significant inconsistencies with fulfillment records and seeing a much greater rate of new prescriptions than anticipated. The irregularities in the data extractions resulted in an extensive and lengthy data cleaning process in which the team had to critically review the data sets and assert many critical assumptions about how to interpret the data. As a result, a 9-month no-cost extension is being applied to the project period. In order to move data extraction forward, the team developed a mitigation strategy to make the SAS code ready to run once the remaining data sets become available.

The project team continued to communicate with the DARTNet coordinators throughout the year to discuss strategies for increasing survey participation, overcoming challenges, and providing updates on project progress. Meanwhile, the distribution of the survey to DARTNet members was completed in 2012 and surveys will continue to be collected until the desired sample size is achieved. The hypertension report using clinical and fulfillment data in two practices is currently being pilot tested.

Preliminary Impact and Findings: The analytics associated with the fulfillment-data extraction uncovered some unexpected challenges in mapping the National Drug Codes (NDC) to Generic Product Identifier codes in order to classify the dispensed medications into the correct class for hypertension, dyslipidemia, and depression. The resulting unmatched NDC codes will limit the generalizability of the overall findings.

Based on data collected from various time periods, another preliminary finding was that the Centers for Medicare & Medicaid’s Meaningful Use requirements appear to have had a significant positive impact on the presence of medication fulfillment data from the participating organizations.

In addition, a large percentage of practices were found to be using e-prescribing, while far fewer receive drug fulfillment data, and only a minimum number of clinicians are using these data for clinical purposes. Upcoming data analysis will help determine if drug fulfillment data, even with perceived completeness gaps and limited value for determining medication compliance, is still useful for detecting drug overlaps across providers or inadvertent drug continuation errors.

Target Population: General

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve the quality and safety of medication management via the integration and utilization of medication management systems and technologies.

Business Goal: Knowledge Creation

Enhancing Fulfillment Data in Community Practices for Clinical Care and Research - 2011

Summary Highlights

  • Principal Investigator: 
  • Funding Mechanism: 
    PAR: HS08-269: Exploratory and Developmental Grant to Improve Health Care Quality through Health Information Technology (IT) (R21)
  • Grant Number: 
    R21 HS 019726
  • Project Period: 
    September 2011 - March 2013
  • AHRQ Funding Amount: 
    $190,455
  • PDF Version: 
    (PDF, 212.81 KB)

Summary: In ambulatory care, there are two major forms of prescription data. Prescribing data represent what clinicians have prescribed for patients-ideally, the intended medication regimen. Fulfillment data represent what patients have received from the pharmacy-the actual medication regimen. While community practices that use electronic prescribing (ePrescribing) are obtaining new access to fulfillment data, many questions still remain about the actual accessibility, comprehensiveness, and utility of these fulfillment data for clinical care and research. Better informed medication management has the potential to improve the quality, safety, and efficiency of the health care system, particularly when there is bidirectional information that includes both prescribing and fulfillment data. In a fragmented medical system, providing clinicians with fulfillment data has the potential to improve coordination of care by revealing what other clinicians have prescribed for a patient. It may also help clinicians provide better informed care by revealing whether a patient has been able to adhere to prescribed drug regiments.

This project is using the Distributed Ambulatory Research in Therapeutics Network (DARTNet) to assess and improve the accessibility and utility of fulfillment data in community practices. The focus of this study strives to extend beyond what occurs at the pharmacy level to include additional information on prescriptions submitted by other providers. DARTNet, funded by the Agency for Healthcare Research and Quality, is an electronic practice-based network that is uniquely qualified for this assessment because it includes 32 independent and geographically-dispersed organizations encompassing more than 1,700 clinicians and 4 million patients. For this project, member practices will be surveyed for their use of ePrescribing and the accessibility and utility of fulfillment data in their electronic health records. Fulfillment data will be extracted from five of those practices and assessed for completeness and accuracy. The utility of using prescribing and fulfillment data to identify unintended continuation of medication and duplication of therapy will be explored.

Specific Aims:

  • Use surveys and interviews to assess the actual status, organizational plans, and barriers for full ePrescribing, capture of fulfillment data, and clinician use of fulfillment data at all DARTNet organizations. (Ongoing)
  • Assess the data's comprehensiveness and clinical utility in five DARTNet organizations receiving fulfillment data through the ePrescribing-based process, the consent-based process, or both. (Ongoing)
  • Develop and pilot test a patient-level report used using clinical, prescribing, and fulfillment data to improve the management of hypertension during the clinical encounter, with subjective assessments of utility by survey and group interviews of clinicians in one DARTNet organization capturing fulfillment data. (Upcoming)

2011 Activities: During the first few months of the project, the focus of activity was on submitting the research application to the Colorado Multiple Institutional Review Board (COMIRB), developing the initial survey among DARTNet organizations, and creating the initial list of proposed data elements for extraction of fulfillment data.

The first submission to COMIRB was made in October 2011, from which minor modifications were requested. The application was re-submitted December 2011 and COMIRB approval was granted on December 21, 2011.

The survey was created using Research Electronic Data Capture (RedCap), a public domain survey tool used in more than 100 institutions. The survey that was developed was based on previous informal email survey questions used in years prior but differs in its ability to include conditional questions using RedCap's logic. Four internal reviewers were used as pilot testers for the survey, resulting in significant modifications during its development, and the addition of conditional logic to improve the flow of questions and topics. More complicated than originally anticipated by the study team, the final version of the survey includes three levels of conditional logic.

In developing the list of proposed data elements, the project team had to consider data availability dependent on the data feeds available at the selected sites. Following a review of the survey data, the team will work with the proposed sites to evaluate data availability for extraction.

As last self-reported in the AHRQ Research Reporting System, project progress and activities are on track, and project budget spending is on target.

Preliminary Impact and Findings: There are no findings to date.

Target Population: General

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve the quality and safety of medication management via the integration and utilization of medication management systems and technologies.

Business Goal: Knowledge Creation

Enhancing Fulfillment Data in Community Practices for Clinical Care and Research - Final Report

Citation:
Kahn M. Enhancing Fulfillment Data in Community Practices for Clinical Care and Research - Final Report. (Prepared by the University of Colorado, Denver under Grant No. R21 HS019726). Rockville, MD: Agency for Healthcare Research and Quality, 2014. (PDF, 1.41 MB)

The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services. (Persons using assistive technology may not be able to fully access information in this report. For assistance, please contact Corey Mackison)
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