Browsing by Author "Health Policy and Management, Richard M. Fairbanks School of Public Health"

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    A decade post-HITECH: Critical access hospitals have electronic health records but struggle to keep up with other advanced functions
    (Oxford University Press, 2021) Apathy, Nate C.; Holmgren, A. Jay; Adler-Milstein, Julia; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Objective: Despite broad electronic health record (EHR) adoption in U.S. hospitals, there is concern that an "advanced use" digital divide exists between critical access hospitals (CAHs) and non-CAHs. We measured EHR adoption and advanced use over time to analyzed changes in the divide. Materials and methods: We used 2008 to 2018 American Hospital Association Information Technology survey data to update national EHR adoption statistics. We stratified EHR adoption by CAH status and measured advanced use for both patient engagement (PE) and clinical data analytics (CDA) domains. We used a linear probability regression for each domain with year-CAH interactions to measure temporal changes in the relationship between CAH status and advanced use. Results: In 2018, 98.3% of hospitals had adopted EHRs; there were no differences by CAH status. A total of 58.7% and 55.6% of hospitals adopted advanced PE and CDA functions, respectively. In both domains, CAHs were less likely to be advanced users: 46.6% demonstrated advanced use for PE and 32.0% for CDA. Since 2015, the advanced use divide has persisted for PE and widened for CDA. Discussion: EHR adoption among hospitals is essentially ubiquitous; however, CAHs still lag behind in advanced use functions critical to improving care quality. This may be rooted in different advanced use needs among CAH patients and lack of access to technical expertise. Conclusions: The advanced use divide prevents CAH patients from benefitting from a fully digitized healthcare system. To close the widening gap in CDA, policymakers should consider partnering with vendors to develop implementation guides and standards for functions like dashboards and high-risk patient identification algorithms to better support CAH adoption.
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    A national overview of nonprofit hospital community benefit programs to address the social determinants of health
    (Oxford University Press, 2023-12-06) Franz, Berkeley; Burns, Ashlyn; Kueffner, Kristin; Bhardwaj, Meeta; Yeager, Valerie A.; Singh, Simone; Puro, Neeraj; Cronin, Cory E.; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Decades of research have solidified the crucial role that social determinants of health (SDOH) play in shaping health outcomes, yet strategies to address these upstream factors remain elusive. The aim of this study was to understand the extent to which US nonprofit hospitals invest in SDOH at either the community or individual patient level and to provide examples of programs in each area. We analyzed data from a national dataset of 613 hospital community health needs assessments and corresponding implementation strategies. Among sample hospitals, 69.3% (n = 373) identified SDOH as a top-5 health need in their community and 60.6% (n = 326) reported investments in SDOH. Of hospitals with investments in SDOH, 44% of programs addressed health-related social needs of individual patients, while the remaining 56% of programs addressed SDOH at the community level. Hospitals that were major teaching organizations, those in the Western region of the United States, and hospitals in counties with more severe housing problems had greater odds of investing in SDOH at the community level. Although many nonprofit hospitals have integrated SDOH-related activities into their community benefit work, stronger policies are necessary to encourage greater investments at the community-level that move beyond the needs of individual patients.
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    Acceptance of Automated Social Risk Scoring in the Emergency Department: Clinician, Staff, and Patient Perspectives
    (University of California, 2024) Mazurenko, Olena; Hirsh, Adam T.; Harle, Christopher A.; McNamee, Cassidy; Vest, Joshua R.; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Introduction: Healthcare organizations are under increasing pressure from policymakers, payers, and advocates to screen for and address patients' health-related social needs (HRSN). The emergency department (ED) presents several challenges to HRSN screening, and patients are frequently not screened for HRSNs. Predictive modeling using machine learning and artificial intelligence, approaches may address some pragmatic HRSN screening challenges in the ED. Because predictive modeling represents a substantial change from current approaches, in this study we explored the acceptability of HRSN predictive modeling in the ED. Methods: Emergency clinicians, ED staff, and patient perspectives on the acceptability and usage of predictive modeling for HRSNs in the ED were obtained through in-depth semi-structured interviews (eight per group, total 24). All participants practiced at or had received care from an urban, Midwest, safety-net hospital system. We analyzed interview transcripts using a modified thematic analysis approach with consensus coding. Results: Emergency clinicians, ED staff, and patients agreed that HRSN predictive modeling must lead to actionable responses and positive patient outcomes. Opinions about using predictive modeling results to initiate automatic referrals to HRSN services were mixed. Emergency clinicians and staff wanted transparency on data inputs and usage, demanded high performance, and expressed concern for unforeseen consequences. While accepting, patients were concerned that prediction models can miss individuals who required services and might perpetuate biases. Conclusion: Emergency clinicians, ED staff, and patients expressed mostly positive views about using predictive modeling for HRSNs. Yet, clinicians, staff, and patients listed several contingent factors impacting the acceptance and implementation of HRSN prediction models in the ED.
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    Analysis of Hospital Quality Measures and Web-Based Chargemasters, 2019: Cross-sectional Study
    (JMIR, 2021-08-19) Patel, Kunal N.; Mazurenko, Olena; Ford, Eric; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Background: The federal health care price transparency regulation from 2019 is aimed at bending the health care cost curve by increasing the availability of hospital pricing information for the public. Objective: This study aims to examine the associations between publicly reported diagnosis-related group chargemaster prices on the internet and quality measures, process indicators, and patient-reported experience measures. Methods: In this cross-sectional study, we collected and analyzed a random 5.02% (212/4221) stratified sample of US hospital prices in 2019 using descriptive statistics and multivariate analysis. Results: We found extreme price variation in shoppable services and significantly greater price variation for medical versus surgical services (P=.006). In addition, we found that quality indicators were positively associated with standard charges, such as mortality (β=.929; P<.001) and readmissions (β=.514; P<.001). Other quality indicators, such as the effectiveness of care (β=-.919; P<.001), efficient use of medical imaging (β=-.458; P=.001), and patient recommendation scores (β=-.414; P<.001), were negatively associated with standard charges. Conclusions: We found that hospital chargemasters display wide variations in prices for medical services and procedures and match variations in quality measures. Further work is required to investigate 100% of US hospital prices posted publicly on the internet and their relationship with quality measures.
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    Assessment of Satisfaction With the Electronic Health Record Among Physicians in Physician-Owned vs Non–Physician-Owned Practices
    (American Medical Association, 2022-04-01) Rotenstein, Lisa S.; Apathy, Nate; Landon, Bruce; Bates, David W.; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Importance: Despite known benefits, electronic health records (EHRs) have had drawbacks for daily practice and the physician experience. There is evidence that physicians practicing in solo or physician-owned practices are more likely to be satisfied with the EHR and experience lower burnout than those practicing in other ownership arrangements; however, it is unclear how practice ownership patterns interact with physicians' experiences with the EHR and documentation in the EHR now that use of these systems is widespread. Objective: To examine the association between practice ownership and physician perceptions of the EHR. Design, setting, and participants: This cross-sectional study included non-federally employed physicians who provided office-based patient care in 2019 and completed the 2019 National Electronic Health Records Survey. The 2019 survey sample consisted of 1524 eligible responses (41.0% unweighted response rate representing 301 603 physicians); of those, 1368 physicians who reported having an EHR and answered questions regarding location ownership were included in the analysis. Data for the 2019 National Electronic Health Records Survey were collected by RTI International from June 14 to December 11, 2019; the current cross-sectional analysis was conducted from October 1 to November 30, 2021. Main outcomes and measures: Satisfaction with the EHR, perceptions of time spent on clinical documentation, and presence of staff support for documentation. Results: Among 1368 respondents (weighted, 270 813 respondents) included in the analysis, 960 respondents (weighted: 185,385 respondents [68.5%]) were male, and 951 respondents (weighted: 200,622 respondents [74.1%]) were over 50 years of age; 766 respondents (weighted, 161 226 respondents [59.5%]) were working in a practice owned by a physician or physician group, and 700 respondents (weighted, 131 284 respondents [48.5%]) were primary care physicians. A total of 602 respondents (weighted, 109 587 physicians [40.5%]) were working in a non-physician-owned practice. Overall, 529 respondents (weighted, 108 093 respondents [68.1%]) working in physician-owned practices reported being satisfied with their EHR vs 320 respondents (weighted, 63 988 respondents [58.5%]) working in non-physician-owned practices (P = .03). Among those working in physician-owned practices, perceptions that time spent on documentation was appropriate (328 physicians [weighted, 71 827 physicians (44.8%)] vs 191 physicians [weighted, 35 447 physicians (32.4%)]; P = .005) and that staff support for documentation was available (289 physicians [weighted, 57 702 physicians (36.0%)] vs 146 physicians [weighted, 29 267 physicians (26.7%)]; P = .02) were significantly higher compared with those working in non-physician-owned practices. Physicians' perceptions of the appropriateness of time spent and the availability of staff support only partially explained the association between practice ownership type and EHR satisfaction. Conclusions and relevance: The results of this nationally representative cross-sectional study suggest that physicians working in physician-owned practices are more likely to be satisfied with the EHR, to have positive perceptions of time spent on documentation, and to have staff support for documentation compared with their counterparts working in non-physician-owned practices. The workflow and cultural forces underlying these differences are important to understand in the setting of known differences in burnout by practice ownership type and ongoing physician group consolidation and acquisition by health care systems.
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    Classifying early infant feeding status from clinical notes using natural language processing and machine learning
    (Springer Nature, 2024-04-03) Lemas, Dominick J.; Du, Xinsong; Rouhizadeh, Masoud; Lewis, Braeden; Frank, Simon; Wright, Lauren; Spirache, Alex; Gonzalez, Lisa; Cheves, Ryan; Magalhães, Marina; Zapata, Ruben; Reddy, Rahul; Xu, Ke; Parker, Leslie; Harle, Chris; Young, Bridget; Louis‑Jaques, Adetola; Zhang, Bouri; Thompson, Lindsay; Hogan, William R.; Modave, François; Health Policy and Management, Richard M. Fairbanks School of Public Health
    The objective of this study is to develop and evaluate natural language processing (NLP) and machine learning models to predict infant feeding status from clinical notes in the Epic electronic health records system. The primary outcome was the classification of infant feeding status from clinical notes using Medical Subject Headings (MeSH) terms. Annotation of notes was completed using TeamTat to uniquely classify clinical notes according to infant feeding status. We trained 6 machine learning models to classify infant feeding status: logistic regression, random forest, XGBoost gradient descent, k-nearest neighbors, and support-vector classifier. Model comparison was evaluated based on overall accuracy, precision, recall, and F1 score. Our modeling corpus included an even number of clinical notes that was a balanced sample across each class. We manually reviewed 999 notes that represented 746 mother-infant dyads with a mean gestational age of 38.9 weeks and a mean maternal age of 26.6 years. The most frequent feeding status classification present for this study was exclusive breastfeeding [n = 183 (18.3%)], followed by exclusive formula bottle feeding [n = 146 (14.6%)], and exclusive feeding of expressed mother’s milk [n = 102 (10.2%)], with mixed feeding being the least frequent [n = 23 (2.3%)]. Our final analysis evaluated the classification of clinical notes as breast, formula/bottle, and missing. The machine learning models were trained on these three classes after performing balancing and down sampling. The XGBoost model outperformed all others by achieving an accuracy of 90.1%, a macro-averaged precision of 90.3%, a macro-averaged recall of 90.1%, and a macro-averaged F1 score of 90.1%. Our results demonstrate that natural language processing can be applied to clinical notes stored in the electronic health records to classify infant feeding status. Early identification of breastfeeding status using NLP on unstructured electronic health records data can be used to inform precision public health interventions focused on improving lactation support for postpartum patients.
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    Closing the Tobacco Treatment Gap: A Qualitative Study of Tobacco Cessation Service Implementation in Community Pharmacies
    (MDPI, 2024-03-28) Ellis Hilts, Katy; Elkhadragy, Nervana; Corelli, Robin L.; Hata, Micah; Tong, Elisa K.; Vitale, Francis M.; Suchanek Hudmon, Karen; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Tobacco use remains a leading preventable cause of morbidity and mortality, with pharmacotherapy and counseling recognized as effective cessation aids. Yet, the potential role of pharmacists and pharmacy technicians in tobacco cessation services is underutilized. This study explores the integration of such services in community pharmacies, identifying facilitators and barriers to their implementation. A qualitative study was conducted across seven community pharmacies in California that were affiliated with the Community Pharmacy Enhanced Services Network. Participants included 22 pharmacists and 26 pharmacy technicians/clerks who completed tobacco cessation training. Data were collected through semi-structured interviews, focusing on experiences with implementing cessation services. The analysis was guided by Rogers' Diffusion of Innovations Theory. MAXQDA software was used for data management and thematic analysis. Sixteen pharmacy personnel participated in the study, highlighting key themes around the integration of cessation services. Compatibility with existing workflows, the importance of staff buy-in, and the crucial role of pharmacy technicians emerged as significant facilitators. Challenges included the complexity of billing for services, software limitations for documenting tobacco use and cessation interventions, and gaps in training for handling complex patient cases. Despite these barriers, pharmacies successfully initiated cessation services, with variations in service delivery and follow-up practices. Community pharmacies represent viable settings for delivering tobacco cessation services, with pharmacists and technicians playing pivotal roles. However, systemic changes are needed to address challenges related to billing, documentation, and training. Enhancing the integration of cessation services in community pharmacies could significantly impact public health by increasing access to effective cessation support.
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    Comparing the performance of screening surveys versus predictive models in identifying patients in need of health-related social need services in the emergency department
    (Public Library of Science, 2024-11-20) Mazurenko, Olena; Hirsh, Adam T.; Harle, Christopher A.; Shen, Joanna; McNamee, Cassidy; Vest, Joshua R.; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Background: Health-related social needs (HRSNs), such as housing instability, food insecurity, and financial strain, are increasingly prevalent among patients. Healthcare organizations must first correctly identify patients with HRSNs to refer them to appropriate services or offer resources to address their HRSNs. Yet, current identification methods are suboptimal, inconsistently applied, and cost prohibitive. Machine learning (ML) predictive modeling applied to existing data sources may be a solution to systematically and effectively identify patients with HRSNs. The performance of ML predictive models using data from electronic health records (EHRs) and other sources has not been compared to other methods of identifying patients needing HRSN services. Methods: A screening questionnaire that included housing instability, food insecurity, transportation barriers, legal issues, and financial strain was administered to adult ED patients at a large safety-net hospital in the mid-Western United States (n = 1,101). We identified those patients likely in need of HRSN-related services within the next 30 days using positive indications from referrals, encounters, scheduling data, orders, or clinical notes. We built an XGBoost classification algorithm using responses from the screening questionnaire to predict HRSN needs (screening questionnaire model). Additionally, we extracted features from the past 12 months of existing EHR, administrative, and health information exchange data for the survey respondents. We built ML predictive models with these EHR data using XGBoost (ML EHR model). Out of concerns of potential bias, we built both the screening question model and the ML EHR model with and without demographic features. Models were assessed on the validation set using sensitivity, specificity, and Area Under the Curve (AUC) values. Models were compared using the Delong test. Results: Almost half (41%) of the patients had a positive indicator for a likely HRSN service need within the next 30 days, as identified through referrals, encounters, scheduling data, orders, or clinical notes. The screening question model had suboptimal performance, with an AUC = 0.580 (95%CI = 0.546, 0.611). Including gender and age resulted in higher performance in the screening question model (AUC = 0.640; 95%CI = 0.609, 0.672). The ML EHR models had higher performance. Without including age and gender, the ML EHR model had an AUC = 0.765 (95%CI = 0.737, 0.792). Adding age and gender did not improve the model (AUC = 0.722; 95%CI = 0.744, 0.800). The screening questionnaire models indicated bias with the highest performance for White non-Hispanic patients. The performance of the ML EHR-based model also differed by race and ethnicity. Conclusion: ML predictive models leveraging several robust EHR data sources outperformed models using screening questions only. Nevertheless, all models indicated biases. Additional work is needed to design predictive models for effectively identifying all patients with HRSNs.
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    Coronavirus is hundreds of times more deadly for people over 60 than people under 40
    (The Conversation US, Inc., 2020-09-10) Menachemi, Nir; Health Policy and Management, Richard M. Fairbanks School of Public Health
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    Declaring racism a public health crisis brings more attention to solving long-ignored racial gaps in health
    (The Conversation US, Inc., 2021-04-22) Halverson, Paul K.; Health Policy and Management, Richard M. Fairbanks School of Public Health