Healthcare-The Journal of Delivery Science and Innovation最新文献

{"title":"The need for patient decision aids in acute care settings","authors":"Joshua E. Rosen , David R. Flum , Joshua M. Liao","doi":"10.1016/j.hjdsi.2022.100639","DOIUrl":"10.1016/j.hjdsi.2022.100639","url":null,"abstract":"","PeriodicalId":29963,"journal":{"name":"Healthcare-The Journal of Delivery Science and Innovation","volume":"11 3","pages":"Article 100639"},"PeriodicalIF":2.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10621932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An assessment of the association between patient race and prior authorization program determinations in the context of radiation therapy","authors":"Adam C. Powell ,&nbsp;Christopher T. Lugo ,&nbsp;Jeremy T. Pickerell ,&nbsp;James W. Long ,&nbsp;Bryan A. Loy ,&nbsp;Amin J. Mirhadi","doi":"10.1016/j.hjdsi.2023.100704","DOIUrl":"10.1016/j.hjdsi.2023.100704","url":null,"abstract":"<div><h3>Background</h3><p>When a physician determines that a patient needs radiation therapy (RT), they submit an RT order to a prior authorization program which assesses guideline-concordance. A rule-based clinical decision support system (CDSS) evaluates whether the order is appropriate or potentially non-indicated. If potentially non-indicated, a board-certified oncologist discusses the order with the ordering physician. After discussion, the order is authorized, modified, withdrawn, or recommended for denial. Although patient race is not captured during ordering, bias prior to and during ordering, or during the discussion, may influence outcomes. This study evaluated if associations existed between race and order determinations by the CDSS and by the overall prior authorization program.</p></div><div><h3>Methods</h3><p>RT orders placed in 2019, pertaining to patients with Medicare Advantage health plans from one national organization, were analyzed. The association between race and prior authorization outcomes was examined for RT orders for all cancers, and then separately for breast, lung, and prostate cancers. Analyses controlled for the patient’s age, urbanicity, and the median income in the patient’s ZIP code. Adjusted analyses were conducted on unmatched and racially-matched samples.</p></div><div><h3>Results</h3><p>Of the 10,145 patients included in the sample, 8,061 (79.5%) were White and 2,084 (20.5%) were Black. Race was not found to have a significant association with CDSS or prior authorization outcomes in any of the analyses.</p></div><div><h3>Conclusions</h3><p>CDSS and prior authorization outcomes suggested similar rates of clinical appropriateness of orders for patients, regardless of race.</p></div><div><h3>Implications</h3><p>Prior authorization utilizing rule-based CDSS was capable of enforcing guidelines without introducing racial bias.</p></div>","PeriodicalId":29963,"journal":{"name":"Healthcare-The Journal of Delivery Science and Innovation","volume":"11 3","pages":"Article 100704"},"PeriodicalIF":2.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10285075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of a pediatric bed prioritization process in a rural Minnesota community-based hospital","authors":"Brian N. Bartlett ,&nbsp;Shylah A. Cassidy ,&nbsp;Tiffany L. Geib ,&nbsp;Wade A. Johnson ,&nbsp;April D. Lanz ,&nbsp;Kathleen S. Linnemann ,&nbsp;Hannah M. Rushing ,&nbsp;Julie M. Sanger ,&nbsp;Nadine N. Vanhoudt","doi":"10.1016/j.hjdsi.2023.100703","DOIUrl":"10.1016/j.hjdsi.2023.100703","url":null,"abstract":"<div><p>Inpatient capacity constraints have been a pervasive challenge for hospitals throughout the COVID-19 pandemic. The Mayo Clinic Health System — Southwest Minnesota region primarily serves patients in rural southwestern Minnesota and part of Iowa and consists of 1 postacute care hospital, 1 tertiary care medical center, and 3 critical access hospitals. The main hub, Mayo Clinic Health System in Mankato, Minnesota, has a pediatric unit with dedicated pediatric hospitalists. To address the growing demand for adult inpatient beds at the height of the pandemic, the pediatric unit was opened to allow adult patients to be admitted when necessary. For several months, adult inpatient capacity exceeded 90%, which decreased the number of available pediatric (vs adult) beds throughout Minnesota, particularly in rural communities. Data for the health system showed that children were most affected because transfers to the next available hospitals for pediatric cases were 55 miles away or more. To address this gap, the hospital team successfully trialed a pediatric bed prioritization guideline that reduced pediatric transfers by 40%. This was accomplished by prioritizing the last remaining inpatient bed on the pediatric unit for pediatric patients only. This process not only reduced pediatric transfers but also increased unique patient admissions because of an average lower length of stay for pediatric patients compared with adult patients.</p></div>","PeriodicalId":29963,"journal":{"name":"Healthcare-The Journal of Delivery Science and Innovation","volume":"11 3","pages":"Article 100703"},"PeriodicalIF":2.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10333808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intensive care management for high-risk veterans in a patient-centered medical home – do some veterans benefit more than others?","authors":"Kaylyn E. Swankoski ,&nbsp;Ashok Reddy ,&nbsp;David Grembowski ,&nbsp;Evelyn T. Chang ,&nbsp;Edwin S. Wong","doi":"10.1016/j.hjdsi.2023.100677","DOIUrl":"10.1016/j.hjdsi.2023.100677","url":null,"abstract":"<div><h3>Background</h3><p>Primary care<span><span> intensive management programs utilize interdisciplinary care teams to comprehensively meet the complex care needs of patients at high risk for hospitalization. The mixed evidence on the effectiveness of these programs focuses on average </span>treatment effects that may mask heterogeneous treatment effects (HTEs) among subgroups of patients. We test for HTEs by patients’ demographic, economic, and social characteristics.</span></p></div><div><h3>Methods</h3><p><span>Retrospective analysis<span> of a VA randomized quality improvement trial. 3995 primary care patients at high risk for hospitalization were randomized to primary care intensive management (n = 1761) or usual primary care (n = 1731). We estimated HTEs on ED and hospital utilization one year after randomization using model-based </span></span>recursive partitioning and a pre-versus post-with control group framework. Splitting variables included administratively collected demographic characteristics, travel distance, copay exemption, risk score for future hospitalizations, history of hospital discharge against medical advice, homelessness, and multiple residence ZIP codes.</p></div><div><h3>Results</h3><p>There were no average or heterogeneous treatment effects of intensive management one year after enrollment. The recursive partitioning algorithm identified variation in effects by risk score, homelessness, and whether the patient had multiple residences in a year. Within each distinct subgroup, the effect of intensive management was not statistically significant.</p></div><div><h3>Conclusions</h3><p>Primary care intensive management did not affect acute care use of high-risk patients on average or differentially for patients defined by various demographic, economic, and social characteristics.</p></div><div><h3>Implications</h3><p>Reducing acute care use for high-risk patients is complex, and more work is required to identify patients positioned to benefit from intensive management programs.</p></div>","PeriodicalId":29963,"journal":{"name":"Healthcare-The Journal of Delivery Science and Innovation","volume":"11 2","pages":"Article 100677"},"PeriodicalIF":2.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9604727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How alignment between health systems and their embedded research units contributes to system learning","authors":"Michael I. Harrison ,&nbsp;Amanda E. Borsky","doi":"10.1016/j.hjdsi.2023.100688","DOIUrl":"10.1016/j.hjdsi.2023.100688","url":null,"abstract":"<div><h3>Background</h3><p>There is growing interest in the contributions of embedded, learning health system<span> (LHS), research within healthcare delivery systems. We examined the organization of LHS research units and conditions affecting their contributions to system improvement and learning.</span></p></div><div><h3>Methods</h3><p>We conducted 12 key-informant and 44 semi-structured interviews in six delivery systems engaged in LHS research. Using rapid qualitative analysis, we identified themes and compared: successful versus challenging projects; LHS units and other research units in the same system; and LHS units in different systems.</p></div><div><h3>Results</h3><p>LHS units operate both independently and as subunits within larger research centers. Contributions of LHS units to improvements and learning are influenced by alignment of facilitating factors within units, within the broader system, and between unit and host system. Key alignment factors were availability of internal (system) funding directing researchers’ work toward system priorities; researchers’ skills and experiences that fit a system’s operational needs; LHS unit subculture supporting system improvement and collaboration with clinicians and other internal stakeholders; applications of external funding to system priorities; and executive leadership for system-wide learning. Mutual understanding and collaboration between researchers, clinicians, and leaders was fostered through direct consultation between LHS unit leaders and system executives and engagement of researchers in clinical and operational activities.</p></div><div><h3>Conclusions</h3><p>Embedded researchers face significant challenges to contributing to system improvement and learning. Nevertheless, when appropriately led, organized, and supported by internal funding, they may learn to collaborate effectively with clinicians and system leaders in advancing care delivery toward the learning health system ideal.</p></div>","PeriodicalId":29963,"journal":{"name":"Healthcare-The Journal of Delivery Science and Innovation","volume":"11 2","pages":"Article 100688"},"PeriodicalIF":2.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9608793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lessons on regulation and implementation from the first FDA-cleared autonomous AI - Interview with Chairman and Founder of Digital Diagnostics Michael Abramoff","authors":"Kaushik P. Venkatesh,&nbsp;Gabriel Brito","doi":"10.1016/j.hjdsi.2023.100692","DOIUrl":"10.1016/j.hjdsi.2023.100692","url":null,"abstract":"","PeriodicalId":29963,"journal":{"name":"Healthcare-The Journal of Delivery Science and Innovation","volume":"11 2","pages":"Article 100692"},"PeriodicalIF":2.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9979180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An equity-focused approach to improving access to COVID-19 vaccination using mobile health clinics","authors":"Carlene A. Mayfield ,&nbsp;Jennifer S. Priem ,&nbsp;Michael Inman ,&nbsp;Trent Legare ,&nbsp;Jennifer Snow ,&nbsp;Elizabeth Wallace","doi":"10.1016/j.hjdsi.2023.100690","DOIUrl":"10.1016/j.hjdsi.2023.100690","url":null,"abstract":"<div><p>This article describes the implementation of an equity-focused strategy to increase the uptake of COVID-19 vaccination among communities of color and in traditionally underserved geographic areas using mobile health clinics (MHCs). The MHC Vaccination Program was implemented through a large integrated healthcare system in North Carolina using a grassroots development and engagement strategy along with a robust model for data-informed decision support to prioritize vulnerable communities. Several valuable lessons from this work can replicated for future outreach initiatives and community-based programming:</p><p>•Health systems can no longer operate under the assumption that community members will come to them, particularly those experiencing compounding social and economic challenges. The MHC model had to be a proactive outreach to community members, rather than a responsive delivery mechanism.</p><p>•Barriers to access included financial, legal, and logistical challenges, in addition to mistrust among historically underserved and marginalized communities.</p><p>•A MHC model can be adaptable and responsive to data-informed decision-making approaches for targeted service delivery.</p><p>•A MHC model is not a one-dimensional solution to access, but part of a broader strategy to create diverse points of entry into the healthcare system that fall within the rhythm of life of community members.</p></div>","PeriodicalId":29963,"journal":{"name":"Healthcare-The Journal of Delivery Science and Innovation","volume":"11 2","pages":"Article 100690"},"PeriodicalIF":2.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10033254/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9596679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does use of primary care-based behavioral health programs differ by race and ethnicity? Evidence from a multi-site collaborative care model","authors":"Benjamin Kovachy ,&nbsp;Trina Chang ,&nbsp;Christine Vogeli ,&nbsp;Suzanne Tolland ,&nbsp;Susan Garrels ,&nbsp;Brent P. Forester ,&nbsp;Vicki Fung","doi":"10.1016/j.hjdsi.2023.100676","DOIUrl":"10.1016/j.hjdsi.2023.100676","url":null,"abstract":"<div><h3>Background</h3><p>Collaborative care models (CoCM) that integrate mental health and primary care<span> improve outcomes and could help address racial and ethnic mental health disparities. We examined whether use of these programs differs by race/ethnicity.</span></p></div><div><h3>Methods</h3><p>This retrospective study examined two CoCM interventions implemented across primary care clinics in a large health system in Massachusetts: 1) a primary care-based behavioral health program for depression or anxiety (IMPACT model) and 2) referral to community-based specialty care services (Resource-finding). Outcomes included enrollment, non-completion, and symptom screening rates, and discharge status for Black, Hispanic and White patients referred for CoCM, 2017–2019.</p></div><div><h3>Results</h3><p><span>Black and Hispanic vs. White patients referred to CoCM (n = 17,280) were more likely to live in high poverty ZIP codes (34% and 40% vs. 9%). Rates of program enrollment, non-completion, and symptom screening were similar across groups (e.g., 76%, 77%, and 75% of Black, Hispanic, and White patients enrolled). Hispanic vs. White patients were more likely to be enrolled in IMPACT (56%) vs. Resource-finding (43%). Among those completing IMPACT, Hispanic vs. White patients were more likely to be stepped to </span>psychiatry vs. discharged to their primary care provider (51% vs. 20%, aOR = 1.55, 95% CI: 1.02–2.35).</p></div><div><h3>Conclusions</h3><p>Black and Hispanic patients referred to CoCM were similarly likely to use the program as White patients. Hispanic patients completing IMPACT were more frequently referred to psychiatry.</p></div><div><h3>Implications</h3><p>These results highlight the promise of CoCMs for engaging minority populations in mental healthcare. Hispanic patients may benefit from additional intervention or earlier linkage to specialty care.</p></div>","PeriodicalId":29963,"journal":{"name":"Healthcare-The Journal of Delivery Science and Innovation","volume":"11 2","pages":"Article 100676"},"PeriodicalIF":2.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10257753/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9669596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving primary care team functioning through evidence based quality improvement: A comparative case study","authors":"Helen Ovsepyan ,&nbsp;Emmeline Chuang ,&nbsp;Julian Brunner ,&nbsp;Alison B. Hamilton ,&nbsp;Jack Needleman ,&nbsp;MarySue Heilemann ,&nbsp;Ismelda Canelo ,&nbsp;Elizabeth M. Yano","doi":"10.1016/j.hjdsi.2023.100691","DOIUrl":"10.1016/j.hjdsi.2023.100691","url":null,"abstract":"<div><h3>Background</h3><p><span>Provision of team-based primary care (PC) is associated with improved care quality, but limited empirical evidence guides practices on how to optimize team functioning. We examined how evidence-based quality improvement (EBQI) was used to change PC team processes. EBQI activities were supported by research-clinical partnerships and included multilevel </span>stakeholder engagement, external facilitation, technical support, formative feedback, QI training, local QI development and across-site collaboration to share proven practices.</p></div><div><h3>Methods</h3><p>We used a comparative case study in two VA medical centers (Sites A and B) that engaged in EBQI between 2014 and 2016. We analyzed multiple qualitative data sources: baseline and follow-up interviews with key stakeholders and provider team (“teamlet”) members (n = 64), and EBQI meeting notes, reports, and supporting materials.</p></div><div><h3>Results</h3><p>Site A's QI project entailed engaging in structured daily huddles using a huddle checklist and developing a protocol clarifying team member roles and responsibilities; Site B initiated weekly virtual team meetings that spanned two practice locations. Respondents from both sites perceived these projects as improving team structure and staffing, team communications, role clarity, staff voice and personhood, accountability, and ultimately, overall team functioning over time.</p></div><div><h3>Conclusion</h3><p>EBQI enabled local QI teams and other stakeholders to develop and implement innovations to improve PC team processes and characteristics in ways that improved teamlet members’ perceptions of team functioning.</p></div><div><h3>Implications</h3><p>EBQI's multi-level approach may empower staff and facilitate innovation by and within teams, making it an effective implementation strategy for addressing unique practice-based challenges and supporting improvements in team functioning across varied clinical settings.</p></div><div><h3>Level of evidence</h3><p>VI.</p></div>","PeriodicalId":29963,"journal":{"name":"Healthcare-The Journal of Delivery Science and Innovation","volume":"11 2","pages":"Article 100691"},"PeriodicalIF":2.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9597772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing real-time prescription benefit tools: Early experiences from 5 academic medical centers","authors":"Jing Luo ,&nbsp;Rachel Wong ,&nbsp;Tanvi Mehta ,&nbsp;Jeremy I. Schwartz ,&nbsp;Jeremy A. Epstein ,&nbsp;Erika Smith ,&nbsp;Nitu Kashyap ,&nbsp;Fasika A. Woreta ,&nbsp;Kristian Feterik ,&nbsp;Michael J. Fliotsos ,&nbsp;Bradley H. Crotty","doi":"10.1016/j.hjdsi.2023.100689","DOIUrl":"10.1016/j.hjdsi.2023.100689","url":null,"abstract":"<div><h3>Background</h3><p>Medication price transparency tools are increasingly available, but data on their use, and their potential effects on prescribing behavior, patient out of pocket (OOP) costs, and clinician workflow integration, is limited.</p></div><div><h3>Objective</h3><p>To describe the implementation experiences with real-time prescription benefit (RTPB) tools at 5 large academic medical centers and their early impact on prescription ordering.</p></div><div><h3>Design</h3><p>and Participants: In this cross-sectional study, we systematically collected information on the characteristics of RTPB tools through discussions with key stakeholders at each of the five organizations. Quantitative encounter data, prescriptions written, and RTPB alerts/estimates and prescription adjustment rates were obtained at each organization in the first three months after “go-live” of the RTPB system(s) between 2019 and 2020.</p></div><div><h3>Main measures</h3><p>Implementation characteristics, prescription orders, cost estimate retrieval rates, and prescription adjustment rates.</p></div><div><h3>Key results</h3><p>Differences were noted with respect to implementation characteristics related to RTPB tools. All of the organizations with the exception of one chose to display OOP cost estimates and suggested alternative prescriptions automatically. Differences were also noted with respect to a patient cost threshold for automatic display. In the first three months after “go-live,” RTPB estimate retrieval rates varied greatly across the five organizations, ranging from 8% to 60% of outpatient prescriptions. The prescription adjustment rate was lower, ranging from 0.1% to 4.9% of all prescriptions ordered.</p></div><div><h3>Conclusions</h3><p>In this study reporting on the early experiences with RTPB tools across five academic medical centers, we found variability in implementation characteristics and population coverage. In addition RTPB estimate retrieval rates were highly variable across the five organizations, while rates of prescription adjustment ranged from low to modest.</p></div>","PeriodicalId":29963,"journal":{"name":"Healthcare-The Journal of Delivery Science and Innovation","volume":"11 2","pages":"Article 100689"},"PeriodicalIF":2.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9977045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}