Stefan G. Kertesz MD, MSc, Aerin J. deRussy MPH, April E. Hoge MPH, Allyson L. Varley PhD, Sally K. Holmes MBA, Kevin R. Riggs MD, Erika L. Austin PhD, Adam J. Gordon MD, MPH, Sonya E. Gabrielian MD, David E. Pollio PhD, Ann E. Montgomery PhD, Lillian Gelberg MD, Jocelyn L. Steward PhD, Audrey L. Jones PhD, Joshua R. Richman MD, PhD
Objective
To identify organizational service features associated with positive patient ratings of primary care within primary care clinics tailored to accommodate persons with ongoing and recent experiences of homelessness (PEH).
Data Sources and Study Setting
PEH receiving primary care in 29 United States Veterans Health Administration homeless-tailored clinics were surveyed about their primary care experience using the validated Primary Care Quality-Homeless (PCQ-H) survey. Characteristics of the clinics were assessed through surveys of clinic staff using a new organizational survey developed through literature review, site visits, statistical analysis, and consensus deliberation.
Study Design
Cross-sectional examination of patients' ratings of care based on surveys of patients, and of clinic characteristics, analyzed with Classification and Regression Tree (CART) analysis, a form of machine learning.
Data Collection Methods
Patient surveys (n = 3394) were obtained from a random sample of enrolled patients by both mail and telephone by an external survey contractor. Staff (n = 52 from 29 clinics) were interviewed by telephone.
Principal Findings
This analysis identified service features that impact patient experience favorably, including aspects of patient-centeredness, team identity, strong external leadership support, and service that reach beyond traditional primary care clinic confines. Results varied according to the patient experience scale analyzed. Individual characteristics of PEH, such as degree of social support, general health, and unsheltered status, were also correlated with how they rate care.
Conclusions
Organizational characteristics correlate with ratings of primary care from patients with recent and ongoing homelessness. Primary care programs serving homeless individuals can assure better care based on who they hire, how they foster team identity, what services they provide, and the strength of leadership support to protect a homeless-focused mission.
{"title":"Organizational and patient factors associated with positive primary care experiences for veterans with current or recent homelessness","authors":"Stefan G. Kertesz MD, MSc, Aerin J. deRussy MPH, April E. Hoge MPH, Allyson L. Varley PhD, Sally K. Holmes MBA, Kevin R. Riggs MD, Erika L. Austin PhD, Adam J. Gordon MD, MPH, Sonya E. Gabrielian MD, David E. Pollio PhD, Ann E. Montgomery PhD, Lillian Gelberg MD, Jocelyn L. Steward PhD, Audrey L. Jones PhD, Joshua R. Richman MD, PhD","doi":"10.1111/1475-6773.14359","DOIUrl":"10.1111/1475-6773.14359","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To identify organizational service features associated with positive patient ratings of primary care within primary care clinics tailored to accommodate persons with ongoing and recent experiences of homelessness (PEH).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>PEH receiving primary care in 29 United States Veterans Health Administration homeless-tailored clinics were surveyed about their primary care experience using the validated Primary Care Quality-Homeless (PCQ-H) survey. Characteristics of the clinics were assessed through surveys of clinic staff using a new organizational survey developed through literature review, site visits, statistical analysis, and consensus deliberation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Cross-sectional examination of patients' ratings of care based on surveys of patients, and of clinic characteristics, analyzed with Classification and Regression Tree (CART) analysis, a form of machine learning.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection Methods</h3>\u0000 \u0000 <p>Patient surveys (<i>n</i> = 3394) were obtained from a random sample of enrolled patients by both mail and telephone by an external survey contractor. Staff (<i>n</i> = 52 from 29 clinics) were interviewed by telephone.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>This analysis identified service features that impact patient experience favorably, including aspects of patient-centeredness, team identity, strong external leadership support, and service that reach beyond traditional primary care clinic confines. Results varied according to the patient experience scale analyzed. Individual characteristics of PEH, such as degree of social support, general health, and unsheltered status, were also correlated with how they rate care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Organizational characteristics correlate with ratings of primary care from patients with recent and ongoing homelessness. Primary care programs serving homeless individuals can assure better care based on who they hire, how they foster team identity, what services they provide, and the strength of leadership support to protect a homeless-focused mission.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 6","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141898993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kaitlin Fertaly PhD, McKenzie Javorka PhD, Diane Brown MPH, Carly Holman MS, Megan Nelson MSW, Annie Glover PhD, MPH, MPA
Objective
To assess factors impacting obstetric transport and referral processes for pregnant patients experiencing an emergency in a rural state without a perinatal regionalized system of care.
Data Sources and Study Setting
Data is from Critical Access Hospitals (CAHs) without obstetric units and healthcare providers involved in obstetric care and transport at hospitals with varying levels of obstetric capacity in Montana.
Study Design
This mixed-methods study involved surveying CAHs without obstetric units about the hospitals' capacity for obstetric emergencies and transport policies. Survey data were collected from 32 of 34 CAHs without obstetric units (94% response rate) in the fall of 2021. Subsequent interviews were conducted in the fall and winter of 2022–2023 with 20 hospital and emergency medical services (EMS) personnel to provide further insights into the referral and transport process during obstetric emergencies.
Data Collection/Extraction Methods
Survey data were collected using REDCap; interviews were conducted via videoconference. We performed descriptive statistics and Fisher's exact tests for quantitative data. We analyzed qualitative data using a three-phase pragmatic analytic approach.
Principal Findings
The survey of CAHs found 12 of 32 facilities faced difficulties coordinating transport for pregnant patients. Qualitative data indicated this was often due to the state's decentralized transport system. Challenges identified through both quantitative and qualitative data included weather, securing a receiving facility/provider, and coordinating medical transport. Only 10 CAHs reported having written protocols for transporting pregnant patients; of those, four facilities had formal transfer agreements. Qualitative data emphasized variations in awareness and the utility of obstetric transport policies.
Conclusions
A decentralized transport system in a rural state can exacerbate existing challenges faced by providers arranging transport for pregnant patients during an obstetric emergency. State and interfacility policies could enhance the transport process for increased regionalization as well as increased support for and coordination of existing EMS.
{"title":"Obstetric transport in rural settings: Referral and transport of pregnant patients in a state without a perinatal regionalized system of care","authors":"Kaitlin Fertaly PhD, McKenzie Javorka PhD, Diane Brown MPH, Carly Holman MS, Megan Nelson MSW, Annie Glover PhD, MPH, MPA","doi":"10.1111/1475-6773.14365","DOIUrl":"10.1111/1475-6773.14365","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To assess factors impacting obstetric transport and referral processes for pregnant patients experiencing an emergency in a rural state without a perinatal regionalized system of care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Data is from Critical Access Hospitals (CAHs) without obstetric units and healthcare providers involved in obstetric care and transport at hospitals with varying levels of obstetric capacity in Montana.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>This mixed-methods study involved surveying CAHs without obstetric units about the hospitals' capacity for obstetric emergencies and transport policies. Survey data were collected from 32 of 34 CAHs without obstetric units (94% response rate) in the fall of 2021. Subsequent interviews were conducted in the fall and winter of 2022–2023 with 20 hospital and emergency medical services (EMS) personnel to provide further insights into the referral and transport process during obstetric emergencies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Survey data were collected using REDCap; interviews were conducted via videoconference. We performed descriptive statistics and Fisher's exact tests for quantitative data. We analyzed qualitative data using a three-phase pragmatic analytic approach.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>The survey of CAHs found 12 of 32 facilities faced difficulties coordinating transport for pregnant patients. Qualitative data indicated this was often due to the state's decentralized transport system. Challenges identified through both quantitative and qualitative data included weather, securing a receiving facility/provider, and coordinating medical transport. Only 10 CAHs reported having written protocols for transporting pregnant patients; of those, four facilities had formal transfer agreements. Qualitative data emphasized variations in awareness and the utility of obstetric transport policies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>A decentralized transport system in a rural state can exacerbate existing challenges faced by providers arranging transport for pregnant patients during an obstetric emergency. State and interfacility policies could enhance the transport process for increased regionalization as well as increased support for and coordination of existing EMS.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amanda C. Blok PhD, MSN, RN, PHCNS-BC, Connor Drake PhD, MPA, Kasey Decosimo MPH, Leah L. Zullig PhD, MPH, Jaime M. Hughes PhD, MPH, MSW, Nina R. Sperber PhD, Swetha Kota MPH, Emily Franzosa DrPH, Cynthia J. Coffman PhD, Megan Shepherd-Banigan PhD, Trisha Chadduck MSW, Kelli D. Allen PhD, Susan N. Hastings MD, Courtney H. Van Houtven PhD
<div> <section> <h3> Objective</h3> <p>To examine the relationship between site-level adaptation and early adoption of Caregivers Finding Important Resources, Support, and Training (FIRST) training during national implementation across diverse Veteran Health Administration (VA) medical centers.</p> </section> <section> <h3> Data Sources and Study Setting</h3> <p>We enrolled and evaluated 25 VA medical centers (VAMCs). Along with administrative data on site characteristics, we examined site-reported data on adaptations and intervention adoption, defined as ≥4 training classes delivered to ≥5 caregivers at 6 months from April through October 2022.</p> </section> <section> <h3> Study Design</h3> <p>A type III hybrid implementation-effectiveness cluster randomized controlled trial, randomized VAMCs 1:1 to receive foundational (low-touch) implementation support (<i>n</i> = 12) or the addition of enhanced (high-touch) implementation support (<i>n</i> = 13).</p> </section> <section> <h3> Data Collection/Extraction Methods</h3> <p>At key implementation phases, VAMCs were asked to report adaptations including content, contextual modifications (format, setting, personnel, and population), and training of providers. We describe site-level adaptations by arm and by organizational characteristics that included VAMC complexity level, staffing, rurality, and organizational readiness to change. We used qualitative comparative analysis to identify unique adaptations that contributed to intervention adoption at 6 months.</p> </section> <section> <h3> Principal Finding<b>s</b></h3> <p>VAMCs randomized to receive enhanced support reported slightly more adaptations than those randomized to foundational support. At 6 months, VAMCs with two or more adaptations adopted Caregivers FIRST at a higher rate than those with fewer adaptations (90% vs. 44%). Staffing adaptations (e.g., who delivered the intervention), format and content (e.g., modified delivery pace), and referring provider training were unique adaptations to adopting sites.</p> </section> <section> <h3> Conclusions</h3> <p>Site-level adaptations were diverse and occurred more frequently in sites with early adoption of Caregivers FIRST. Future research should identify best practices of supporting and monitoring intervention adaptation. Understanding the role of adaptation in early adoption success could assist ot
{"title":"Adaptations and early adoption of a family caregiver intervention in the Veterans Affairs Health Care System: A multimethod pragmatic approach for national scaling","authors":"Amanda C. Blok PhD, MSN, RN, PHCNS-BC, Connor Drake PhD, MPA, Kasey Decosimo MPH, Leah L. Zullig PhD, MPH, Jaime M. Hughes PhD, MPH, MSW, Nina R. Sperber PhD, Swetha Kota MPH, Emily Franzosa DrPH, Cynthia J. Coffman PhD, Megan Shepherd-Banigan PhD, Trisha Chadduck MSW, Kelli D. Allen PhD, Susan N. Hastings MD, Courtney H. Van Houtven PhD","doi":"10.1111/1475-6773.14360","DOIUrl":"10.1111/1475-6773.14360","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine the relationship between site-level adaptation and early adoption of Caregivers Finding Important Resources, Support, and Training (FIRST) training during national implementation across diverse Veteran Health Administration (VA) medical centers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>We enrolled and evaluated 25 VA medical centers (VAMCs). Along with administrative data on site characteristics, we examined site-reported data on adaptations and intervention adoption, defined as ≥4 training classes delivered to ≥5 caregivers at 6 months from April through October 2022.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>A type III hybrid implementation-effectiveness cluster randomized controlled trial, randomized VAMCs 1:1 to receive foundational (low-touch) implementation support (<i>n</i> = 12) or the addition of enhanced (high-touch) implementation support (<i>n</i> = 13).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>At key implementation phases, VAMCs were asked to report adaptations including content, contextual modifications (format, setting, personnel, and population), and training of providers. We describe site-level adaptations by arm and by organizational characteristics that included VAMC complexity level, staffing, rurality, and organizational readiness to change. We used qualitative comparative analysis to identify unique adaptations that contributed to intervention adoption at 6 months.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Finding<b>s</b></h3>\u0000 \u0000 <p>VAMCs randomized to receive enhanced support reported slightly more adaptations than those randomized to foundational support. At 6 months, VAMCs with two or more adaptations adopted Caregivers FIRST at a higher rate than those with fewer adaptations (90% vs. 44%). Staffing adaptations (e.g., who delivered the intervention), format and content (e.g., modified delivery pace), and referring provider training were unique adaptations to adopting sites.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Site-level adaptations were diverse and occurred more frequently in sites with early adoption of Caregivers FIRST. Future research should identify best practices of supporting and monitoring intervention adaptation. Understanding the role of adaptation in early adoption success could assist ot","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 S2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Leah M. Marcotte MD, MS, Chelle L. Wheat PhD, MPH, Mayuree Rao MD, MS, Edwin S. Wong PhD, Paul Hebert PhD, Karin Nelson MD, MSHS, Jorge Rojas MS, Eric J. Gunnink MS, Ashok Reddy MD, MS
Objective
To evaluate whether the Preventive Health Inventory (PHI)—a virtual care management intervention addressing hypertension and diabetes management implemented nationally in the Veterans Health Administration (VHA)—was delivered equitably among racial/ethnic groups and if existing inequities in hypertension and diabetes outcomes changed following PHI receipt.
Data Sources and Study Setting
We used data from the VHA Corporate Data Warehouse among Veterans enrolled in primary care nationally from February 28, 2021 to March 31, 2022.
Study Design
We used logistic regression to evaluate PHI receipt and hypertension and diabetes outcomes after PHI implementation among Veterans with hypertension and/or diabetes. We conducted unadjusted analyses and analyses adjusting for clinic fixed effects using dummy variables.
Data Collection/Extraction Methods
We identified Veterans engaged in primary care with documented race/ethnicity and hypertension and/or diabetes diagnoses in all months during the study period.
Principle Findings
Prior to PHI, Non-Hispanic Black (NHB) (42.2%) and Hispanic (39.5%) Veterans were less likely to have controlled hypertension vs. Non-Hispanic White (NHW) Veterans (47.5%); NHB Veterans (32.9%) were more likely to have uncontrolled diabetes vs. NHW Veterans (25.1%). Among 1,805,658 Veterans, 5.7% NHW (N = 68,744), 5.6% NHB (N = 22,580), 10.2% Hispanic (N = 13,313), 6.2% Asian/Pacific Islander/Native Hawaiian (N = 1868), 5.1% American Indian/Native Alaskan (N = 744), and 5.6% multiple races or other race (N = 1647) Veterans received PHI. We found no significant racial inequities in PHI receipt in unadjusted and adjusted models. Hypertension and diabetes measures improved more in the intervention group compared with the group who did not receive the intervention. There were no new or worsened inequities after PHI, and in pre-/post-intervention analysis, among NHB Veterans, the inequity in uncontrolled diabetes improved by 1.9 percentage points (95% CI 0.2, 3.6).
Conclusions
Our findings suggest the PHI intervention was equitably deployed across race/ethnicity groups without significantly impacting most existing inequities in diabetes and hypertension.
{"title":"Evaluating equity in a national virtual care management intervention: Delivery and outcomes by race/ethnicity among Veterans with hypertension and diabetes","authors":"Leah M. Marcotte MD, MS, Chelle L. Wheat PhD, MPH, Mayuree Rao MD, MS, Edwin S. Wong PhD, Paul Hebert PhD, Karin Nelson MD, MSHS, Jorge Rojas MS, Eric J. Gunnink MS, Ashok Reddy MD, MS","doi":"10.1111/1475-6773.14352","DOIUrl":"10.1111/1475-6773.14352","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To evaluate whether the Preventive Health Inventory (PHI)—a virtual care management intervention addressing hypertension and diabetes management implemented nationally in the Veterans Health Administration (VHA)—was delivered equitably among racial/ethnic groups and if existing inequities in hypertension and diabetes outcomes changed following PHI receipt.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>We used data from the VHA Corporate Data Warehouse among Veterans enrolled in primary care nationally from February 28, 2021 to March 31, 2022.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We used logistic regression to evaluate PHI receipt and hypertension and diabetes outcomes after PHI implementation among Veterans with hypertension and/or diabetes. We conducted unadjusted analyses and analyses adjusting for clinic fixed effects using dummy variables.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>We identified Veterans engaged in primary care with documented race/ethnicity and hypertension and/or diabetes diagnoses in all months during the study period.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principle Findings</h3>\u0000 \u0000 <p>Prior to PHI, Non-Hispanic Black (NHB) (42.2%) and Hispanic (39.5%) Veterans were less likely to have controlled hypertension vs. Non-Hispanic White (NHW) Veterans (47.5%); NHB Veterans (32.9%) were more likely to have uncontrolled diabetes vs. NHW Veterans (25.1%). Among 1,805,658 Veterans, 5.7% NHW (<i>N</i> = 68,744), 5.6% NHB (<i>N</i> = 22,580), 10.2% Hispanic (<i>N</i> = 13,313), 6.2% Asian/Pacific Islander/Native Hawaiian (<i>N</i> = 1868), 5.1% American Indian/Native Alaskan (<i>N</i> = 744), and 5.6% multiple races or other race (<i>N</i> = 1647) Veterans received PHI. We found no significant racial inequities in PHI receipt in unadjusted and adjusted models. Hypertension and diabetes measures improved more in the intervention group compared with the group who did not receive the intervention. There were no new or worsened inequities after PHI, and in pre-/post-intervention analysis, among NHB Veterans, the inequity in uncontrolled diabetes improved by 1.9 percentage points (95% CI 0.2, 3.6).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our findings suggest the PHI intervention was equitably deployed across race/ethnicity groups without significantly impacting most existing inequities in diabetes and hypertension.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 6","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Todd Brown MSc, Angela Fagerlin PhD, Matthew H. Samore MD, Alex H. S. Harris PhD, Patrick Galyean BS, Susan Zickmund PhD, Warren B. P. Pettey MPH, CPH, Megan E. Vanneman PhD, MPH
Objective
To understand Veterans Health Administration (VA) leaders' information and resource needs for managing post-9/11 Veterans' VA enrollment and retention.
Data Sources and Study Setting
Interviews conducted from March–May 2022 of VA Medical Center (VAMC) leaders (N = 27) across 15 sites, using stratified sampling based on VAMC characteristics: enrollment rates, number of recently separated Veterans in catchment area, and state Medicaid expansion status.
Study Design
Interview questions were developed using Petersen et al.'s Factors Influencing Choice of Healthcare System framework as a guide. Interviews were transcribed verbatim, and two coders analyzed the interviews using Atlas.ti, a qualitative software program. Coders followed the qualitative coding philosophy developed by Crabtree and Miller, a process of developing codes for salient concepts as they are identified during the analysis process.
Data Collection/Extraction Methods
Two coders analyzed 22% (N = 6) of the interviews and discussed and adjudicated any discrepancies. One coder independently coded the remainder of the interviews.
Principal Findings
Several key themes were identified regarding facilitators and barriers for VA enrollment including reputation for high-quality VA care, convenience of VA services, awareness of VA services and benefits, and VA mental health services. Nearly every VA leader actively used tools and data to understand enrollment and retention rates and sought to enroll and retain more Veterans. To improve the management of enrollment and retention, VA leaders would like data shared in an easily understandable format and the capability to share data between the VA and community healthcare systems.
Conclusions
Enrollment and retention information is important for healthcare leaders to guide their health system decisions. Various tools are currently being used to try to understand the data. However, a multifunctional tool is needed to better aggregate the data to provide VA leadership with key information on Veterans' enrollment and retention.
{"title":"Information and resources VA health system leaders need to manage enrollment and retention for Post-9/11 veterans","authors":"Todd Brown MSc, Angela Fagerlin PhD, Matthew H. Samore MD, Alex H. S. Harris PhD, Patrick Galyean BS, Susan Zickmund PhD, Warren B. P. Pettey MPH, CPH, Megan E. Vanneman PhD, MPH","doi":"10.1111/1475-6773.14351","DOIUrl":"10.1111/1475-6773.14351","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To understand Veterans Health Administration (VA) leaders' information and resource needs for managing post-9/11 Veterans' VA enrollment and retention.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Interviews conducted from March–May 2022 of VA Medical Center (VAMC) leaders (N = 27) across 15 sites, using stratified sampling based on VAMC characteristics: enrollment rates, number of recently separated Veterans in catchment area, and state Medicaid expansion status.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Interview questions were developed using Petersen et al.'s <i>Factors Influencing Choice of Healthcare System</i> framework as a guide. Interviews were transcribed verbatim, and two coders analyzed the interviews using Atlas.ti, a qualitative software program. Coders followed the qualitative coding philosophy developed by Crabtree and Miller, a process of developing codes for salient concepts as they are identified during the analysis process.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Two coders analyzed 22% (<i>N</i> = 6) of the interviews and discussed and adjudicated any discrepancies. One coder independently coded the remainder of the interviews.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Several key themes were identified regarding facilitators and barriers for VA enrollment including reputation for high-quality VA care, convenience of VA services, awareness of VA services and benefits, and VA mental health services. Nearly every VA leader actively used tools and data to understand enrollment and retention rates and sought to enroll and retain more Veterans. To improve the management of enrollment and retention, VA leaders would like data shared in an easily understandable format and the capability to share data between the VA and community healthcare systems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Enrollment and retention information is important for healthcare leaders to guide their health system decisions. Various tools are currently being used to try to understand the data. However, a multifunctional tool is needed to better aggregate the data to provide VA leadership with key information on Veterans' enrollment and retention.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gabriela Schmajuk MD, MS, Anna Ware MPH, Jing Li MPH, Gary Tarasovsky BA, Stephen Shiboski PhD, Jennifer L. Barton MD, Karla L. Miller MD, Holly A. Mitchell MD, Jo Dana NP, Kimberly Reiter MD MS, Elizabeth Wahl MD, Karine Rozenberg-Ben-Dror PharmD, Ronald G. Hauser MD, Mary A. Whooley MD
Objective
To develop, deploy, and evaluate a national, electronic health record (EHR)-based dashboard to support safe prescribing of biologic and targeted synthetic disease-modifying agents (b/tsDMARDs) in the United States Veterans Affairs Healthcare System (VA).
Data Sources and Study Setting
We extracted and displayed hepatitis B (HBV), hepatitis C (HCV), and tuberculosis (TB) screening data from the EHR for users of b/tsDMARDs using PowerBI (Microsoft) and deployed the dashboard to VA facilities across the United States in 2022; we observed facilities for 44 weeks post-deployment.
Study Design
We examined the association between dashboard engagement by healthcare personnel and the percentage of patients with all screenings complete (HBV, HCV, and TB) at the facility level using an interrupted time series. Based on frequency of sessions, facilities were grouped into high- and low/none-engagement categories. We modeled changes in complete screening pre- and post-deployment of the dashboard.
Data Collection Methods
All VA facilities were eligible for inclusion; excluded facilities participated in design of the dashboard or had <20 patients receiving b/tsDMARDs. Session counts from facility personnel were captured using PowerBI audit log data. Outcomes were assessed weekly based on EHR data extracted via the dashboard itself.
Principal Findings
Totally 117 facilities (serving a total of 41,224 Veterans prescribed b/tsDMARDs) were included. Before dashboard deployment, across all facilities, 61.5% of patients had all screenings complete, which improved to 66.3% over the course of the study period. The largest improvement (15 percentage points, 60.3%–75.3%) occurred among facilities with high engagement (post-intervention difference in outcome between high and low/none-engagement groups was 0.17 percentage points (pp) per week, 95% confidence interval (0.04 pp, 0.30 pp); p = 0.01).
Conclusions
We observed significant improvements in screening for latent infections among facilities with high engagement with the dashboard, compared with those with fewer sessions.
{"title":"National rollout of a medication safety dashboard to improve testing for latent infections among biologic and targeted synthetic disease-modifying agent users within the Veterans Health Administration","authors":"Gabriela Schmajuk MD, MS, Anna Ware MPH, Jing Li MPH, Gary Tarasovsky BA, Stephen Shiboski PhD, Jennifer L. Barton MD, Karla L. Miller MD, Holly A. Mitchell MD, Jo Dana NP, Kimberly Reiter MD MS, Elizabeth Wahl MD, Karine Rozenberg-Ben-Dror PharmD, Ronald G. Hauser MD, Mary A. Whooley MD","doi":"10.1111/1475-6773.14363","DOIUrl":"10.1111/1475-6773.14363","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To develop, deploy, and evaluate a national, electronic health record (EHR)-based dashboard to support safe prescribing of biologic and targeted synthetic disease-modifying agents (b/tsDMARDs) in the United States Veterans Affairs Healthcare System (VA).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>We extracted and displayed hepatitis B (HBV), hepatitis C (HCV), and tuberculosis (TB) screening data from the EHR for users of b/tsDMARDs using PowerBI (Microsoft) and deployed the dashboard to VA facilities across the United States in 2022; we observed facilities for 44 weeks post-deployment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We examined the association between dashboard engagement by healthcare personnel and the percentage of patients with all screenings complete (HBV, HCV, and TB) at the facility level using an interrupted time series. Based on frequency of sessions, facilities were grouped into high- and low/none-engagement categories. We modeled changes in complete screening pre- and post-deployment of the dashboard.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection Methods</h3>\u0000 \u0000 <p>All VA facilities were eligible for inclusion; excluded facilities participated in design of the dashboard or had <20 patients receiving b/tsDMARDs. Session counts from facility personnel were captured using PowerBI audit log data. Outcomes were assessed weekly based on EHR data extracted via the dashboard itself.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Totally 117 facilities (serving a total of 41,224 Veterans prescribed b/tsDMARDs) were included. Before dashboard deployment, across all facilities, 61.5% of patients had all screenings complete, which improved to 66.3% over the course of the study period. The largest improvement (15 percentage points, 60.3%–75.3%) occurred among facilities with high engagement (post-intervention difference in outcome between high and low/none-engagement groups was 0.17 percentage points (pp) per week, 95% confidence interval (0.04 pp, 0.30 pp); <i>p</i> = 0.01).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We observed significant improvements in screening for latent infections among facilities with high engagement with the dashboard, compared with those with fewer sessions.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"60 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ilias Kyriopoulos PhD, Sara Machado PhD, Irene Papanicolas PhD
Objective
To examine wealth-related inequalities in self-reported health status among older population in the United States and 14 European countries.
Data Sources and Study Setting
We used secondary individual-level data from Health and Retirement Survey (HRS) and the Survey of Health, Ageing, and Retirement in Europe (SHARE) in 2011 and 2019.
Study Design
In this cross-sectional study, we used two waves from HRS (wave 10 and 14) and SHARE (wave 4 and 8) to compare wealth-related health inequality across countries, age groups, and birth cohorts. We estimated Wagstaff concentration indices to measure these inequalities across three age groups (50–59, 60–69, 70–79) and two birth cohorts (1942–1947, 1948–1953) in the US and 14 European countries.
Data Collection/Extraction Methods
We performed secondary analysis of survey data.
Principal Findings
Focusing on older population, we found evidence of wealth-related inequalities in self-reported health status across several high-income countries, with the US demonstrating higher levels of inequality than its European counterparts. The magnitude of these inequalities with respect to wealth remained unchanged over the study period across all countries. Our findings also suggest that wealth-related health inequalities differ at different stages of workforce engagement, especially in the United States. This could be explained either by potential redistributive effects of retirement or by uneven survivor effect, as less wealthy may drop out of the observations at a greater rate partly due to their poorer health.
Conclusions
Wealth-related inequalities in self-reported health status are strong and persistent across countries. Our results suggest that there is meaningful variation across high-income countries in health-wealth dynamics that merits further investigation to better understand whether certain health or welfare systems are more equitable. They also highlight the need to consider social policy and wealth redistribution mechanisms as strategies for improving population health among the less wealthy, in the United States and elsewhere.
{"title":"Wealth-related inequalities in self-reported health status in the United States and 14 high-income countries","authors":"Ilias Kyriopoulos PhD, Sara Machado PhD, Irene Papanicolas PhD","doi":"10.1111/1475-6773.14366","DOIUrl":"10.1111/1475-6773.14366","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine wealth-related inequalities in self-reported health status among older population in the United States and 14 European countries.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>We used secondary individual-level data from Health and Retirement Survey (HRS) and the Survey of Health, Ageing, and Retirement in Europe (SHARE) in 2011 and 2019.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>In this cross-sectional study, we used two waves from HRS (wave 10 and 14) and SHARE (wave 4 and 8) to compare wealth-related health inequality across countries, age groups, and birth cohorts. We estimated Wagstaff concentration indices to measure these inequalities across three age groups (50–59, 60–69, 70–79) and two birth cohorts (1942–1947, 1948–1953) in the US and 14 European countries.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>We performed secondary analysis of survey data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Focusing on older population, we found evidence of wealth-related inequalities in self-reported health status across several high-income countries, with the US demonstrating higher levels of inequality than its European counterparts. The magnitude of these inequalities with respect to wealth remained unchanged over the study period across all countries. Our findings also suggest that wealth-related health inequalities differ at different stages of workforce engagement, especially in the United States. This could be explained either by potential redistributive effects of retirement or by uneven survivor effect, as less wealthy may drop out of the observations at a greater rate partly due to their poorer health.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Wealth-related inequalities in self-reported health status are strong and persistent across countries. Our results suggest that there is meaningful variation across high-income countries in health-wealth dynamics that merits further investigation to better understand whether certain health or welfare systems are more equitable. They also highlight the need to consider social policy and wealth redistribution mechanisms as strategies for improving population health among the less wealthy, in the United States and elsewhere.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 6","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Laura J. Damschroder MPH, MSc, Richard Evans MS, H. Myra Kim ScD, Jeremy Sussman MD, Michelle B. Freitag MPH, Claire H. Robinson MPH, Jennifer A. Burns MHSA, Nicholas R. Yankey MPH, MSW, Julie C. Lowery PhD
<div> <section> <h3> Objective</h3> <p>To test effectiveness of the LEAP (Learn Engage Act Process) Program on engaging frontline Veteran Health Administration (VHA) medical center teams in continuous quality improvement (QI), a core capability for learning health systems.</p> </section> <section> <h3> Data Sources and Study Setting</h3> <p>Data sources included VHA electronic health record (EHR) data, surveys, and LEAP coaching field notes.</p> </section> <section> <h3> Study Design</h3> <p>A staggered difference-in-differences study was conducted. Fifty-five facilities participated in LEAP across eight randomly assigned clusters of 6–8 facilities per cluster over 2 years. Non-participating facilities were used as controls. A MOVE! weight management program team completed a Plan-Do-Study-Act cycle of change supported by learning curriculum, coaching, and virtual collaboratives in LEAP facilities. Primary outcome was program reach to Veterans. A mixed-effects model compared pre- versus post-LEAP periods for LEAP versus control facilities. LEAP adherence, satisfaction, and cost to deliver LEAP were evaluated.</p> </section> <section> <h3> Data Collection/Extraction Methods</h3> <p>Thirty months of facility-level EHR MOVE! enrollment data were included in analyses. LEAP Satisfaction and QI skills were elicited via surveys at baseline and 6-month post-LEAP.</p> </section> <section> <h3> Principal findings</h3> <p>Fifty-five facilities were randomly assigned to eight time-period-based clusters to receive LEAP (71% completed LEAP) and 82 non-participating facilities were randomly assigned as controls. Reach in LEAP and control facilities was comparable in the 12-month pre-LEAP period (<i>p</i> = 0.07). Though LEAP facilities experienced slower decline in reach in the 12-month post-LEAP period compared with controls (<i>p</i> < 0.001), this is likely due to unexplained fluctuations in controls. For LEAP facilities, satisfaction was high (all mean ratings >4 on a 5-point scale), self-reported use of QI methods increased significantly (<i>p</i>-values <0.05) 6 months post-LEAP, and delivery cost was $4024 per facility-based team.</p> </section> <section> <h3> Conclusion</h3> <p>Control facilities experienced declining reach in the 12-month post-LEAP period, but LEAP facilities did not, plus they reported higher engagement in QI, an essential capab
{"title":"Effectiveness of a virtual quality improvement training program to improve reach of weight management programs within a large health system","authors":"Laura J. Damschroder MPH, MSc, Richard Evans MS, H. Myra Kim ScD, Jeremy Sussman MD, Michelle B. Freitag MPH, Claire H. Robinson MPH, Jennifer A. Burns MHSA, Nicholas R. Yankey MPH, MSW, Julie C. Lowery PhD","doi":"10.1111/1475-6773.14344","DOIUrl":"10.1111/1475-6773.14344","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To test effectiveness of the LEAP (Learn Engage Act Process) Program on engaging frontline Veteran Health Administration (VHA) medical center teams in continuous quality improvement (QI), a core capability for learning health systems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Data sources included VHA electronic health record (EHR) data, surveys, and LEAP coaching field notes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>A staggered difference-in-differences study was conducted. Fifty-five facilities participated in LEAP across eight randomly assigned clusters of 6–8 facilities per cluster over 2 years. Non-participating facilities were used as controls. A MOVE! weight management program team completed a Plan-Do-Study-Act cycle of change supported by learning curriculum, coaching, and virtual collaboratives in LEAP facilities. Primary outcome was program reach to Veterans. A mixed-effects model compared pre- versus post-LEAP periods for LEAP versus control facilities. LEAP adherence, satisfaction, and cost to deliver LEAP were evaluated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Thirty months of facility-level EHR MOVE! enrollment data were included in analyses. LEAP Satisfaction and QI skills were elicited via surveys at baseline and 6-month post-LEAP.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal findings</h3>\u0000 \u0000 <p>Fifty-five facilities were randomly assigned to eight time-period-based clusters to receive LEAP (71% completed LEAP) and 82 non-participating facilities were randomly assigned as controls. Reach in LEAP and control facilities was comparable in the 12-month pre-LEAP period (<i>p</i> = 0.07). Though LEAP facilities experienced slower decline in reach in the 12-month post-LEAP period compared with controls (<i>p</i> < 0.001), this is likely due to unexplained fluctuations in controls. For LEAP facilities, satisfaction was high (all mean ratings >4 on a 5-point scale), self-reported use of QI methods increased significantly (<i>p</i>-values <0.05) 6 months post-LEAP, and delivery cost was $4024 per facility-based team.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Control facilities experienced declining reach in the 12-month post-LEAP period, but LEAP facilities did not, plus they reported higher engagement in QI, an essential capab","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 S2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Monica M. Matthieu PhD, LCSW, David A. Adkins MHA, LaCinda Jones MSW, MJ, LISW-S, Ciara M. Oliver MS, Jack H. Suarez BS, Barbara Johnson BA, Mona J. Ritchie PhD, LCSW
Objective
To describe a process model for assisting partners in addressing requirements of legislation and review policy analysis, planning, and evaluation design processes and tools. Throughout its 25-year history, the United States Department of Veterans Affairs (VA) Quality Enhancement Research Initiative (QUERI) program has been a forerunner in partnering with organizational leaders to improve health care. The Foundations of Evidence-based Policymaking Act of 2018 provided new opportunities for QUERI and other implementation scientists to support federal agency leaders in implementing, evaluating, and reporting on congressionally mandated programs. Although implementation scientists have the skills to support partnered implementation and evaluation, these skills must be adapted for congressionally mandated projects as many scientists have limited experience in policy analysis and the intersection of data informing organizational policy, programs, and practices (i.e., evidence-based policy).
Data Sources and Study Setting
During the conduct of four congressionally mandated projects, our national VA QUERI team developed processes and tools to achieve the goals and aims of our VHA partners and to ensure our collective work and reporting met legislative requirements.
Study Design
Our process model, program planning, and analysis tools were informed by an iterative process of refining and adapting the tools over a period of six years, spanning the years 2017 to 2023.
Principal Findings
Work to support our partners was conducted across three phases: preparation and planning, conducting implementation and evaluation, and developing the congressionally mandated report. The processes and tools we developed within the context of mutually respectful and honest partnerships have been critical to our QUERI center's success in this area.
Conclusions
Lessons we learned may help other scientists partnering in VA or other federal agencies to plan, conduct, and report on congressionally mandated projects.
{"title":"Linking implementation science and policy: Process and tools for congressionally mandated implementation, evaluation, and reporting","authors":"Monica M. Matthieu PhD, LCSW, David A. Adkins MHA, LaCinda Jones MSW, MJ, LISW-S, Ciara M. Oliver MS, Jack H. Suarez BS, Barbara Johnson BA, Mona J. Ritchie PhD, LCSW","doi":"10.1111/1475-6773.14357","DOIUrl":"10.1111/1475-6773.14357","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To describe a process model for assisting partners in addressing requirements of legislation and review policy analysis, planning, and evaluation design processes and tools. Throughout its 25-year history, the United States Department of Veterans Affairs (VA) Quality Enhancement Research Initiative (QUERI) program has been a forerunner in partnering with organizational leaders to improve health care. The Foundations of Evidence-based Policymaking Act of 2018 provided new opportunities for QUERI and other implementation scientists to support federal agency leaders in implementing, evaluating, and reporting on congressionally mandated programs. Although implementation scientists have the skills to support partnered implementation and evaluation, these skills must be adapted for congressionally mandated projects as many scientists have limited experience in policy analysis and the intersection of data informing organizational policy, programs, and practices (i.e., evidence-based policy).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>During the conduct of four congressionally mandated projects, our national VA QUERI team developed processes and tools to achieve the goals and aims of our VHA partners and to ensure our collective work and reporting met legislative requirements.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Our process model, program planning, and analysis tools were informed by an iterative process of refining and adapting the tools over a period of six years, spanning the years 2017 to 2023.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Work to support our partners was conducted across three phases: preparation and planning, conducting implementation and evaluation, and developing the congressionally mandated report. The processes and tools we developed within the context of mutually respectful and honest partnerships have been critical to our QUERI center's success in this area.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Lessons we learned may help other scientists partnering in VA or other federal agencies to plan, conduct, and report on congressionally mandated projects.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 S2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kertu Tenso PhD, Yufei Li MPH, Aaron Legler MPH, Izabela Sadej MSW, Aigerim Kabdiyeva MPhil, Melissa M. Garrido PhD, Steven D. Pizer PhD
<div> <section> <h3> Objective</h3> <p>The objective of this analysis was to evaluate the effect of resident program training size on clinician productivity and turnover in the Veterans Health Administration (VHA), the largest education and training platform for medical professionals in the United States.</p> </section> <section> <h3> Data Sources</h3> <p>We retrieved administrative data on training programs and training facilities from the VA Office of Academic Affiliations and the VHA Corporate Data Warehouse. Data on primary care physician shortage areas were retrieved from the Health Resources and Services Administration.</p> </section> <section> <h3> Study Design</h3> <p>We used a quasi-experimental instrumental variables 2SLS design and constructed an exogenous predicted training allocation treatment variable as a function of the total national training program allocation. The outcome was clinical staff productivity and turnover. Secondary analyses stratified results using Health Professional Shortage Areas data (HPSA).</p> </section> <section> <h3> Data Collection/Extraction Methods</h3> <p>Data were obtained for a national dataset of 141 VHA medical facilities and 26 specialties that hosted training programs across 11 years from 2011 to 2021 (<i>N</i> = 132,177).</p> </section> <section> <h3> Principal Findings</h3> <p>Instrumental variables results showed that on average, an increase of one training slot in a specialty leads to a decrease of 0.039 visits per standardized clinic day (<i>p</i> < 0.001) and a 0.02 percentage point increase in turnover (<i>p</i> < 0.001). The direction of this association varied by specialty: while psychiatry and psychology specialties saw a decline in productivity, fields such as primary care and cardiology experienced an increase in productivity. HPSA stratified results indicate that negative effects on productivity and turnover are driven by areas with little to no primary care physician shortage, whereas shortage areas experienced a small increase in productivity and no effect on turnover.</p> </section> <section> <h3> Conclusions</h3> <p>This quasi-experimental evaluation indicates that resident training program size is associated with reduced productivity and increased turnover in specialties such as psychiatry and in facilities with high baseline productivity. However, in specialties like primary care and cardiology, as well as areas with shortages of primary care, larger training programs are associated with increased p
{"title":"Medical training program size and clinical staff productivity and turnover","authors":"Kertu Tenso PhD, Yufei Li MPH, Aaron Legler MPH, Izabela Sadej MSW, Aigerim Kabdiyeva MPhil, Melissa M. Garrido PhD, Steven D. Pizer PhD","doi":"10.1111/1475-6773.14364","DOIUrl":"10.1111/1475-6773.14364","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The objective of this analysis was to evaluate the effect of resident program training size on clinician productivity and turnover in the Veterans Health Administration (VHA), the largest education and training platform for medical professionals in the United States.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources</h3>\u0000 \u0000 <p>We retrieved administrative data on training programs and training facilities from the VA Office of Academic Affiliations and the VHA Corporate Data Warehouse. Data on primary care physician shortage areas were retrieved from the Health Resources and Services Administration.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We used a quasi-experimental instrumental variables 2SLS design and constructed an exogenous predicted training allocation treatment variable as a function of the total national training program allocation. The outcome was clinical staff productivity and turnover. Secondary analyses stratified results using Health Professional Shortage Areas data (HPSA).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Data were obtained for a national dataset of 141 VHA medical facilities and 26 specialties that hosted training programs across 11 years from 2011 to 2021 (<i>N</i> = 132,177).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Instrumental variables results showed that on average, an increase of one training slot in a specialty leads to a decrease of 0.039 visits per standardized clinic day (<i>p</i> < 0.001) and a 0.02 percentage point increase in turnover (<i>p</i> < 0.001). The direction of this association varied by specialty: while psychiatry and psychology specialties saw a decline in productivity, fields such as primary care and cardiology experienced an increase in productivity. HPSA stratified results indicate that negative effects on productivity and turnover are driven by areas with little to no primary care physician shortage, whereas shortage areas experienced a small increase in productivity and no effect on turnover.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This quasi-experimental evaluation indicates that resident training program size is associated with reduced productivity and increased turnover in specialties such as psychiatry and in facilities with high baseline productivity. However, in specialties like primary care and cardiology, as well as areas with shortages of primary care, larger training programs are associated with increased p","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"60 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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