Depression Quality of Care: Measuring Quality over Time Using VA Electronic Medical Record Data
ABSTRACT BACKGROUND The Veterans Health Administration (VA) has invested substantially in evidence-based mental health care. Yet no electronic performance measures for assessing the level at which the population of Veterans with depression receive appropriate care have proven robust enough to suppor...
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Veröffentlicht in: | Journal of general internal medicine : JGIM 2016-04, Vol.31 (Suppl 1), p.36-45 |
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creator | Farmer, Melissa M. Rubenstein, Lisa V. Sherbourne, Cathy D. Huynh, Alexis Chu, Karen Lam, Christine A. Fickel, Jacqueline J. Lee, Martin L. Metzger, Maureen E. Verchinina, Lilia Post, Edward P. Chaney, Edmund F. |
description | ABSTRACT
BACKGROUND
The Veterans Health Administration (VA) has invested substantially in evidence-based mental health care. Yet no electronic performance measures for assessing the level at which the population of Veterans with depression receive appropriate care have proven robust enough to support rigorous evaluation of the VA’s depression initiatives.
OBJECTIVE
Our objectives were to develop prototype longitudinal electronic population-based measures of depression care quality, validate the measures using expert panel judgment by VA and non-VA experts, and examine detection, follow-up and treatment rates over a decade (2000–2010). We describe our development methodology and the challenges to creating measures that capture the longitudinal course of clinical care from detection to treatment.
DESIGN AND PARTICIPANTS
Data come from the National Patient Care Database and Pharmacy Benefits Management Database for primary care patients from 1999 to 2011, from nine Veteran Integrated Service Networks.
MEASURES
We developed four population-based quality metrics for depression care that incorporate a 6-month look back and 1-year follow-up: detection of a new episode of depression, 84 and 180 day follow-up, and minimum appropriate treatment 1-year post detection. Expert panel techniques were used to evaluate the measure development methodology and results. Key challenges to creating valid longitudinal measures are discussed.
KEY RESULTS
Over the decade, the rates for detection of new episodes of depression remained stable at 7–8 %. Follow-up at 84 and 180 days were 37 % and 45 % in 2000 and increased to 56 % and 63 % by 2010. Minimum appropriate treatment remained relatively stable over the decade (82–84 %).
CONCLUSIONS
The development of valid longitudinal, population-based quality measures for depression care is a complex process with numerous challenges. If the full spectrum of care from detection to follow-up and treatment is not captured, performance measures could actually mask the clinical areas in need of quality improvement efforts. |
doi_str_mv | 10.1007/s11606-015-3563-4 |
format | Article |
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BACKGROUND
The Veterans Health Administration (VA) has invested substantially in evidence-based mental health care. Yet no electronic performance measures for assessing the level at which the population of Veterans with depression receive appropriate care have proven robust enough to support rigorous evaluation of the VA’s depression initiatives.
OBJECTIVE
Our objectives were to develop prototype longitudinal electronic population-based measures of depression care quality, validate the measures using expert panel judgment by VA and non-VA experts, and examine detection, follow-up and treatment rates over a decade (2000–2010). We describe our development methodology and the challenges to creating measures that capture the longitudinal course of clinical care from detection to treatment.
DESIGN AND PARTICIPANTS
Data come from the National Patient Care Database and Pharmacy Benefits Management Database for primary care patients from 1999 to 2011, from nine Veteran Integrated Service Networks.
MEASURES
We developed four population-based quality metrics for depression care that incorporate a 6-month look back and 1-year follow-up: detection of a new episode of depression, 84 and 180 day follow-up, and minimum appropriate treatment 1-year post detection. Expert panel techniques were used to evaluate the measure development methodology and results. Key challenges to creating valid longitudinal measures are discussed.
KEY RESULTS
Over the decade, the rates for detection of new episodes of depression remained stable at 7–8 %. Follow-up at 84 and 180 days were 37 % and 45 % in 2000 and increased to 56 % and 63 % by 2010. Minimum appropriate treatment remained relatively stable over the decade (82–84 %).
CONCLUSIONS
The development of valid longitudinal, population-based quality measures for depression care is a complex process with numerous challenges. If the full spectrum of care from detection to follow-up and treatment is not captured, performance measures could actually mask the clinical areas in need of quality improvement efforts.</description><identifier>ISSN: 0884-8734</identifier><identifier>EISSN: 1525-1497</identifier><identifier>DOI: 10.1007/s11606-015-3563-4</identifier><identifier>PMID: 26951274</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Cohort Studies ; Databases, Factual - trends ; Decision Trees ; Delphi Technique ; Depression - diagnosis ; Depression - epidemiology ; Depression - therapy ; Electronic Health Records - standards ; Electronic Health Records - trends ; Electronic medical records ; Follow-Up Studies ; Health care ; Health services ; Humans ; Internal Medicine ; Longitudinal Studies ; Medical electronics ; Medicine ; Medicine & Public Health ; Mental depression ; Mental disorders ; Mental health ; Original Research ; Population ; Population Surveillance - methods ; Quality ; Quality control ; Quality of care ; Quality of Health Care - standards ; Quality of Health Care - trends ; United States ; United States Department of Veterans Affairs - standards ; United States Department of Veterans Affairs - trends ; Veterans</subject><ispartof>Journal of general internal medicine : JGIM, 2016-04, Vol.31 (Suppl 1), p.36-45</ispartof><rights>Society of General Internal Medicine 2016</rights><rights>Journal of General Internal Medicine is a copyright of Springer, (2016). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c470t-840051f4ad9815ab754537e52ade7288b9a57d7233d3271bc950e1c55809a11f3</citedby><cites>FETCH-LOGICAL-c470t-840051f4ad9815ab754537e52ade7288b9a57d7233d3271bc950e1c55809a11f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4803680/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4803680/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,41488,42557,51319,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26951274$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Farmer, Melissa M.</creatorcontrib><creatorcontrib>Rubenstein, Lisa V.</creatorcontrib><creatorcontrib>Sherbourne, Cathy D.</creatorcontrib><creatorcontrib>Huynh, Alexis</creatorcontrib><creatorcontrib>Chu, Karen</creatorcontrib><creatorcontrib>Lam, Christine A.</creatorcontrib><creatorcontrib>Fickel, Jacqueline J.</creatorcontrib><creatorcontrib>Lee, Martin L.</creatorcontrib><creatorcontrib>Metzger, Maureen E.</creatorcontrib><creatorcontrib>Verchinina, Lilia</creatorcontrib><creatorcontrib>Post, Edward P.</creatorcontrib><creatorcontrib>Chaney, Edmund F.</creatorcontrib><title>Depression Quality of Care: Measuring Quality over Time Using VA Electronic Medical Record Data</title><title>Journal of general internal medicine : JGIM</title><addtitle>J GEN INTERN MED</addtitle><addtitle>J Gen Intern Med</addtitle><description>ABSTRACT
BACKGROUND
The Veterans Health Administration (VA) has invested substantially in evidence-based mental health care. Yet no electronic performance measures for assessing the level at which the population of Veterans with depression receive appropriate care have proven robust enough to support rigorous evaluation of the VA’s depression initiatives.
OBJECTIVE
Our objectives were to develop prototype longitudinal electronic population-based measures of depression care quality, validate the measures using expert panel judgment by VA and non-VA experts, and examine detection, follow-up and treatment rates over a decade (2000–2010). We describe our development methodology and the challenges to creating measures that capture the longitudinal course of clinical care from detection to treatment.
DESIGN AND PARTICIPANTS
Data come from the National Patient Care Database and Pharmacy Benefits Management Database for primary care patients from 1999 to 2011, from nine Veteran Integrated Service Networks.
MEASURES
We developed four population-based quality metrics for depression care that incorporate a 6-month look back and 1-year follow-up: detection of a new episode of depression, 84 and 180 day follow-up, and minimum appropriate treatment 1-year post detection. Expert panel techniques were used to evaluate the measure development methodology and results. Key challenges to creating valid longitudinal measures are discussed.
KEY RESULTS
Over the decade, the rates for detection of new episodes of depression remained stable at 7–8 %. Follow-up at 84 and 180 days were 37 % and 45 % in 2000 and increased to 56 % and 63 % by 2010. Minimum appropriate treatment remained relatively stable over the decade (82–84 %).
CONCLUSIONS
The development of valid longitudinal, population-based quality measures for depression care is a complex process with numerous challenges. If the full spectrum of care from detection to follow-up and treatment is not captured, performance measures could actually mask the clinical areas in need of quality improvement efforts.</description><subject>Cohort Studies</subject><subject>Databases, Factual - trends</subject><subject>Decision Trees</subject><subject>Delphi Technique</subject><subject>Depression - diagnosis</subject><subject>Depression - epidemiology</subject><subject>Depression - therapy</subject><subject>Electronic Health Records - standards</subject><subject>Electronic Health Records - trends</subject><subject>Electronic medical records</subject><subject>Follow-Up Studies</subject><subject>Health care</subject><subject>Health services</subject><subject>Humans</subject><subject>Internal Medicine</subject><subject>Longitudinal Studies</subject><subject>Medical electronics</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Mental depression</subject><subject>Mental disorders</subject><subject>Mental health</subject><subject>Original Research</subject><subject>Population</subject><subject>Population Surveillance - methods</subject><subject>Quality</subject><subject>Quality control</subject><subject>Quality of care</subject><subject>Quality of Health Care - standards</subject><subject>Quality of Health Care - trends</subject><subject>United States</subject><subject>United States Department of Veterans Affairs - standards</subject><subject>United States Department of Veterans Affairs - trends</subject><subject>Veterans</subject><issn>0884-8734</issn><issn>1525-1497</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp1kU1r3DAQhkVpabbb_IBeiqCXXtxq9GHJOQTCJv2AhNCS5Cq08jhR8FpbyQ7k31dm06Qp9CAEmmfe0bwvIe-AfQLG9OcMULO6YqAqoWpRyRdkAYqrCmSjX5IFM0ZWRgu5R97kfMsYCM7Na7LH60YB13JB7DFuE-Yc4kB_TK4P4z2NHV25hAf0DF2eUhiun0p3mOhF2CC9zPP71RE96dGPKQ7BF74N3vX0J_qYWnrsRveWvOpcn3H_4V6Syy8nF6tv1en51--ro9PKS83GykjGFHTStY0B5dZaSSU0Ku5a1NyYdeOUbjUXohVcw9o3iiF4pQxrHEAnluRwp7ud1htsPQ5jcr3dprBx6d5GF-zzyhBu7HW8s9IwUZezJB8fBFL8NWEe7SZkj33vBoxTtqB1sRhq1RT0wz_obZzSUNazfPa4Ke7zQsGO8inmnLB7_AwwO8dnd_HZEp-d47Oy9Lz_e4vHjj95FYDvgLydc8H0NPr_qr8BqYWkSA</recordid><startdate>20160401</startdate><enddate>20160401</enddate><creator>Farmer, Melissa M.</creator><creator>Rubenstein, Lisa V.</creator><creator>Sherbourne, Cathy D.</creator><creator>Huynh, Alexis</creator><creator>Chu, Karen</creator><creator>Lam, Christine A.</creator><creator>Fickel, Jacqueline J.</creator><creator>Lee, Martin L.</creator><creator>Metzger, Maureen E.</creator><creator>Verchinina, Lilia</creator><creator>Post, Edward P.</creator><creator>Chaney, Edmund F.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7RV</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>8AO</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>K9.</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2O</scope><scope>M7N</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20160401</creationdate><title>Depression Quality of Care: Measuring Quality over Time Using VA Electronic Medical Record Data</title><author>Farmer, Melissa M. ; Rubenstein, Lisa V. ; Sherbourne, Cathy D. ; Huynh, Alexis ; Chu, Karen ; Lam, Christine A. ; Fickel, Jacqueline J. ; Lee, Martin L. ; Metzger, Maureen E. ; Verchinina, Lilia ; Post, Edward P. ; Chaney, Edmund F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c470t-840051f4ad9815ab754537e52ade7288b9a57d7233d3271bc950e1c55809a11f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Cohort Studies</topic><topic>Databases, Factual - trends</topic><topic>Decision Trees</topic><topic>Delphi Technique</topic><topic>Depression - diagnosis</topic><topic>Depression - epidemiology</topic><topic>Depression - therapy</topic><topic>Electronic Health Records - standards</topic><topic>Electronic Health Records - trends</topic><topic>Electronic medical records</topic><topic>Follow-Up Studies</topic><topic>Health care</topic><topic>Health services</topic><topic>Humans</topic><topic>Internal Medicine</topic><topic>Longitudinal Studies</topic><topic>Medical electronics</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Mental depression</topic><topic>Mental disorders</topic><topic>Mental health</topic><topic>Original Research</topic><topic>Population</topic><topic>Population Surveillance - methods</topic><topic>Quality</topic><topic>Quality control</topic><topic>Quality of care</topic><topic>Quality of Health Care - standards</topic><topic>Quality of Health Care - trends</topic><topic>United States</topic><topic>United States Department of Veterans Affairs - standards</topic><topic>United States Department of Veterans Affairs - trends</topic><topic>Veterans</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Farmer, Melissa M.</creatorcontrib><creatorcontrib>Rubenstein, Lisa V.</creatorcontrib><creatorcontrib>Sherbourne, Cathy D.</creatorcontrib><creatorcontrib>Huynh, Alexis</creatorcontrib><creatorcontrib>Chu, Karen</creatorcontrib><creatorcontrib>Lam, Christine A.</creatorcontrib><creatorcontrib>Fickel, Jacqueline J.</creatorcontrib><creatorcontrib>Lee, Martin L.</creatorcontrib><creatorcontrib>Metzger, Maureen E.</creatorcontrib><creatorcontrib>Verchinina, Lilia</creatorcontrib><creatorcontrib>Post, Edward P.</creatorcontrib><creatorcontrib>Chaney, Edmund F.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Nursing & Allied Health Database</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of general internal medicine : JGIM</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Farmer, Melissa M.</au><au>Rubenstein, Lisa V.</au><au>Sherbourne, Cathy D.</au><au>Huynh, Alexis</au><au>Chu, Karen</au><au>Lam, Christine A.</au><au>Fickel, Jacqueline J.</au><au>Lee, Martin L.</au><au>Metzger, Maureen E.</au><au>Verchinina, Lilia</au><au>Post, Edward P.</au><au>Chaney, Edmund F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Depression Quality of Care: Measuring Quality over Time Using VA Electronic Medical Record Data</atitle><jtitle>Journal of general internal medicine : JGIM</jtitle><stitle>J GEN INTERN MED</stitle><addtitle>J Gen Intern Med</addtitle><date>2016-04-01</date><risdate>2016</risdate><volume>31</volume><issue>Suppl 1</issue><spage>36</spage><epage>45</epage><pages>36-45</pages><issn>0884-8734</issn><eissn>1525-1497</eissn><abstract>ABSTRACT
BACKGROUND
The Veterans Health Administration (VA) has invested substantially in evidence-based mental health care. Yet no electronic performance measures for assessing the level at which the population of Veterans with depression receive appropriate care have proven robust enough to support rigorous evaluation of the VA’s depression initiatives.
OBJECTIVE
Our objectives were to develop prototype longitudinal electronic population-based measures of depression care quality, validate the measures using expert panel judgment by VA and non-VA experts, and examine detection, follow-up and treatment rates over a decade (2000–2010). We describe our development methodology and the challenges to creating measures that capture the longitudinal course of clinical care from detection to treatment.
DESIGN AND PARTICIPANTS
Data come from the National Patient Care Database and Pharmacy Benefits Management Database for primary care patients from 1999 to 2011, from nine Veteran Integrated Service Networks.
MEASURES
We developed four population-based quality metrics for depression care that incorporate a 6-month look back and 1-year follow-up: detection of a new episode of depression, 84 and 180 day follow-up, and minimum appropriate treatment 1-year post detection. Expert panel techniques were used to evaluate the measure development methodology and results. Key challenges to creating valid longitudinal measures are discussed.
KEY RESULTS
Over the decade, the rates for detection of new episodes of depression remained stable at 7–8 %. Follow-up at 84 and 180 days were 37 % and 45 % in 2000 and increased to 56 % and 63 % by 2010. Minimum appropriate treatment remained relatively stable over the decade (82–84 %).
CONCLUSIONS
The development of valid longitudinal, population-based quality measures for depression care is a complex process with numerous challenges. If the full spectrum of care from detection to follow-up and treatment is not captured, performance measures could actually mask the clinical areas in need of quality improvement efforts.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>26951274</pmid><doi>10.1007/s11606-015-3563-4</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Cohort Studies Databases, Factual - trends Decision Trees Delphi Technique Depression - diagnosis Depression - epidemiology Depression - therapy Electronic Health Records - standards Electronic Health Records - trends Electronic medical records Follow-Up Studies Health care Health services Humans Internal Medicine Longitudinal Studies Medical electronics Medicine Medicine & Public Health Mental depression Mental disorders Mental health Original Research Population Population Surveillance - methods Quality Quality control Quality of care Quality of Health Care - standards Quality of Health Care - trends United States United States Department of Veterans Affairs - standards United States Department of Veterans Affairs - trends Veterans |
title | Depression Quality of Care: Measuring Quality over Time Using VA Electronic Medical Record Data |
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