Pub Date : 2025-08-07DOI: 10.1016/j.jcjq.2025.08.002
Nathan L Delafield MD (is Physician, Department of Community Internal Medicine, and Associate Program Director, Internal Medicine Residency Program, Mayo Clinic, Phoenix), Amogh Havanur MD, MPH (is Physician and Senior Associate Consultant, Department of Hospital Internal Medicine, Mayo Clinic, Phoenix), Timethia J. Bonner DPM, PhD (is Research Associate, Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota), Robert Horsley MD (is Internist, Department of Community Internal Medicine, Mayo Clinic, Phoenix), Carolyn Mead-Harvey MS (is Biostatistician, Division of Biostatistics, Mayo Clinic, Phoenix), Sophie Bersoux MD, MPH (is Internist, Department of Medicine, Mayo Clinic, Phoenix), Kama White MD (is Faculty, Internal Medicine Residency, Department of Medicine, Creighton University, Phoenix, and Physician, Valleywise Health, Phoenix), Nyima Ali MD (is Clinical Assistant Professor, Department of Obstetrics and Gynecology, Creighton University, Phoenix, and Obstetrician and Gynecologist, Valleywise Health, Phoenix), Adam J. Milam MD, PhD (is Professor and Cardiothoracic Anesthesiologist, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Phoenix. Please address correspondence to Adam J. Milam)
Background
This Innovation Report describes the feasibility and impact of an intervention focused on community-based social support to address social determinants of health (SDoH).
Methods
This study followed adult patients (N = 12) referred by primary care teams at a Federally Qualified Health Center (FQHC) due to unresolved SDoH needs. Over 12 months, community volunteers (the Open Table Network Table) were paired with patients to address their primary SDoH needs. Primary outcomes included the implementation of the Open Table Network Table and resolution of the patients’ primary SDoH needs, measured by achieving patient-defined goals. Secondary outcomes evaluated intervention impact on patient resilience, healthcare utilization, and other SDoH metrics (for example, economic stability). Semistructured qualitative interviews were conducted with participants postintervention. Primary outcomes were assessed qualitatively; secondary outcomes were tested using paired analyses.
Results
Twelve patients were enrolled over 12 months, and their SDoH needs ranged from healthcare access to social isolation. Community volunteers resolved 91.7% of these primary SDoH needs, with a median volunteer effort of 47.5 hours (interquartile range [IQR] 22.5–73.0) provided to each participant. No statistically significant differences were seen in patient resilience, other SDoH metrics, or healthcare utilization postintervention. The intervention was implemented (that is, the intervention was feasible), but there were challenges to implementation, including recruitment of patients, the need for multiple connections, and the time commitment required by volunteers.
Conclusion
The Open Table Network Table was implemented at an FQHC but required considerable volunteer effort and creativity. Further research is needed to assess the scalability, sustainability, and long-term social and clinical impact of this intervention.
{"title":"A Community-Based Intervention to Address Social Determinants of Health: A Pilot Study","authors":"Nathan L Delafield MD (is Physician, Department of Community Internal Medicine, and Associate Program Director, Internal Medicine Residency Program, Mayo Clinic, Phoenix), Amogh Havanur MD, MPH (is Physician and Senior Associate Consultant, Department of Hospital Internal Medicine, Mayo Clinic, Phoenix), Timethia J. Bonner DPM, PhD (is Research Associate, Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota), Robert Horsley MD (is Internist, Department of Community Internal Medicine, Mayo Clinic, Phoenix), Carolyn Mead-Harvey MS (is Biostatistician, Division of Biostatistics, Mayo Clinic, Phoenix), Sophie Bersoux MD, MPH (is Internist, Department of Medicine, Mayo Clinic, Phoenix), Kama White MD (is Faculty, Internal Medicine Residency, Department of Medicine, Creighton University, Phoenix, and Physician, Valleywise Health, Phoenix), Nyima Ali MD (is Clinical Assistant Professor, Department of Obstetrics and Gynecology, Creighton University, Phoenix, and Obstetrician and Gynecologist, Valleywise Health, Phoenix), Adam J. Milam MD, PhD (is Professor and Cardiothoracic Anesthesiologist, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Phoenix. Please address correspondence to Adam J. Milam)","doi":"10.1016/j.jcjq.2025.08.002","DOIUrl":"10.1016/j.jcjq.2025.08.002","url":null,"abstract":"<div><h3>Background</h3><div>This Innovation Report describes the feasibility and impact of an intervention focused on community-based social support to address social determinants of health (SDoH).</div></div><div><h3>Methods</h3><div>This study followed adult patients (<em>N</em> = 12) referred by primary care teams at a Federally Qualified Health Center (FQHC) due to unresolved SDoH needs. Over 12 months, community volunteers (the Open Table Network Table) were paired with patients to address their primary SDoH needs. Primary outcomes included the implementation of the Open Table Network Table and resolution of the patients’ primary SDoH needs, measured by achieving patient-defined goals. Secondary outcomes evaluated intervention impact on patient resilience, healthcare utilization, and other SDoH metrics (for example, economic stability). Semistructured qualitative interviews were conducted with participants postintervention. Primary outcomes were assessed qualitatively; secondary outcomes were tested using paired analyses.</div></div><div><h3>Results</h3><div>Twelve patients were enrolled over 12 months, and their SDoH needs ranged from healthcare access to social isolation. Community volunteers resolved 91.7% of these primary SDoH needs, with a median volunteer effort of 47.5 hours (interquartile range [IQR] 22.5–73.0) provided to each participant. No statistically significant differences were seen in patient resilience, other SDoH metrics, or healthcare utilization postintervention. The intervention was implemented (that is, the intervention was feasible), but there were challenges to implementation, including recruitment of patients, the need for multiple connections, and the time commitment required by volunteers.</div></div><div><h3>Conclusion</h3><div>The Open Table Network Table was implemented at an FQHC but required considerable volunteer effort and creativity. Further research is needed to assess the scalability, sustainability, and long-term social and clinical impact of this intervention.</div></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"51 11","pages":"Pages 727-732"},"PeriodicalIF":2.4,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-07DOI: 10.1016/j.jcjq.2025.08.001
Meriel McCollum PhD, RN (is Nurse Scientist, Advocate Health, Milwaukee.), Yimei Wu RN, MHS (is Evidence-Based Practice Coordinator, Wellstar Health System.), LeeAnna Spiva PhD, RN (is Assistant Vice President, Nursing Practice and Operations, Wellstar Health System. Please address correspondence to Meriel McCollum)
Background
Predictive models using machine learning technology are increasingly being incorporated into electronic health records to support staff in risk assessment and prediction of adverse outcomes. There is little research available related to how this technology fits into the nursing workflow or its effects on nurse behaviors or actual patient outcomes.
Methods
Retrospective data from four medical/surgical units were examined to explore nurse interactions with the interruptive alerts produced by the model and their chronological relation to actual falls.
Results
During the study period, 1.5% of all admissions resulted in at least one fall, and 87.0% of admissions resulted in at least one fall alert being produced by the system. Most alerts (57.3%) were dismissed by the receiver using the Snooze to Review option, and 22.0% of alerts were shown to staff members other than the primary nurse caring for the patient. Most falls (89.3%) were preceded by an alert being shown to any staff member, but a smaller number of falls (38.7%) were preceded by an alert being shown to the primary nurse.
Conclusion
In most fall cases in this sample, the primary nurse caring for the patient had never been exposed to an alert. However, most alerts were dismissed by nurses using the Snooze to Review option. Further research is needed to understand the relationship between nurse exposure to interruptive alerts and associated actions taken by nursing staff to prevent falls. Machine learning technology should be carefully studied and optimized to suit the needs and workflow of the staff and patients it is intended to serve.
{"title":"Prediction or Prevention? Nurse Interactions with an Electronic Early Warning System for Fall Risk","authors":"Meriel McCollum PhD, RN (is Nurse Scientist, Advocate Health, Milwaukee.), Yimei Wu RN, MHS (is Evidence-Based Practice Coordinator, Wellstar Health System.), LeeAnna Spiva PhD, RN (is Assistant Vice President, Nursing Practice and Operations, Wellstar Health System. Please address correspondence to Meriel McCollum)","doi":"10.1016/j.jcjq.2025.08.001","DOIUrl":"10.1016/j.jcjq.2025.08.001","url":null,"abstract":"<div><h3>Background</h3><div>Predictive models using machine learning technology are increasingly being incorporated into electronic health records to support staff in risk assessment and prediction of adverse outcomes. There is little research available related to how this technology fits into the nursing workflow or its effects on nurse behaviors or actual patient outcomes.</div></div><div><h3>Methods</h3><div>Retrospective data from four medical/surgical units were examined to explore nurse interactions with the interruptive alerts produced by the model and their chronological relation to actual falls.</div></div><div><h3>Results</h3><div>During the study period, 1.5% of all admissions resulted in at least one fall, and 87.0% of admissions resulted in at least one fall alert being produced by the system. Most alerts (57.3%) were dismissed by the receiver using the Snooze to Review option, and 22.0% of alerts were shown to staff members other than the primary nurse caring for the patient. Most falls (89.3%) were preceded by an alert being shown to any staff member, but a smaller number of falls (38.7%) were preceded by an alert being shown to the primary nurse.</div></div><div><h3>Conclusion</h3><div>In most fall cases in this sample, the primary nurse caring for the patient had never been exposed to an alert. However, most alerts were dismissed by nurses using the Snooze to Review option. Further research is needed to understand the relationship between nurse exposure to interruptive alerts and associated actions taken by nursing staff to prevent falls. Machine learning technology should be carefully studied and optimized to suit the needs and workflow of the staff and patients it is intended to serve.</div></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"51 11","pages":"Pages 690-694"},"PeriodicalIF":2.4,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145058539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-05DOI: 10.1016/j.jcjq.2025.07.009
Michael D. Stocker MD, MPH (formerly Resident Physician, Department of Emergency Medicine, Vanderbilt University Medical Center (VUMC), Nashville, Tennessee, is Emergency Medical Services Fellow, University of Cincinnati Health.), Chrissie Schaeffer DNP, APRN (is Clinical Nurse Specialist, Department of Emergency Medicine, VUMC.), Randy Cox MPH (is Senior Patient Safety Advisor, Department of Quality, Safety and Risk Prevention, VUMC.), Emily Tew BSN, RN (is Assistant Nurse Manager, Department of Emergency Medicine, VUMC.), Kaitlyn Jensen BSN, RN (is Registered Nurse, Department of Emergency Medicine, VUMC.), Kimberley Smith BSN, RN (is Registered Nurse, Department of Emergency Medicine, VUMC.), Mitchell Sexton MBA (is Principal Analytics Consultant, Enterprise Analytics, VUMC.), Brian Bales MD (is Assistant Professor and Assistant Quality Medical Director, Department of Emergency Medicine, VUMC.), Amina Belghit MA (is Research Analyst, Department of Emergency Medicine, VUMC.), Jonathan W. Andereck MD, MBA (is Assistant Professor and Quality Medical Director, Department of Emergency Medicine, VUMC.), David P. Johnson MD (is Professor and Director of Quality, Department of Pediatrics, VUMC.), J. Christopher Champion MD, MBA (is Associate Professor and Executive Director for Regional Quality, Safety and Risk Prevention, Department of Emergency Medicine, VUMC.), William B. Stubblefield MD, MPH (is Assistant Professor and Emergency Medicine Physician, Department of Emergency Medicine, VUMC. Please address correspondence to William B. Stubblefield)
Background
Timely diagnosis of ST-segment elevation myocardial infarction (STEMI) in the emergency department (ED) is dependent on electrocardiogram (ECG) completion. The American Heart Association recommends ECG testing within 10 minutes of arrival for patients with symptoms concerning for acute coronary syndrome. The authors aimed to increase the percentage of patients with door-to-ECG (DTE) times of < 10 minutes from 53.7% to > 75%.
Methods
We initiated a quality improvement project at an academic, quaternary care ED in June 2022. Patients included were adults (age > 30 years) who presented as walk-ins to ED triage with chest pain and received a cardiac troponin order. The primary measure was the percentage of patients with an ECG completed within 10 minutes of registration. Secondary measures included mean DTE time and mean time to STEMI activation. Statistical process control charts were used to analyze intervention impact.
Results
Successful completion of ECGs within 10 minutes increased from 53.7% to 80.0% despite rising patient volumes. Three separate centerline shifts were associated with three interventions: (1) physical relocation of a pivot nurse to identify patients on arrival and dedicated space for rapid ECG acquisition; (2) staff education and recognition of high performers; (3) increased waiting room monitoring staff. DTE time was monitored for one year with no additional interventions, and the centerline decreased to 71.3%.
Conclusion
The authors used rapid Plan-Do-Study-Act (PDSA) cycle changes to improve DTE within 10 minutes to > 80% before declining to 71.3% during the maintenance phase. Modification of nursing roles and positions, staff education, recognition of high performers, and increased staffing were drivers of improvement. These improvements are translatable to other departments seeking to improve DTE metrics and may be largely sustained without active surveillance or additional interventions.
{"title":"Improving Door-to-ECG Time at a Quaternary Care Emergency Department","authors":"Michael D. Stocker MD, MPH (formerly Resident Physician, Department of Emergency Medicine, Vanderbilt University Medical Center (VUMC), Nashville, Tennessee, is Emergency Medical Services Fellow, University of Cincinnati Health.), Chrissie Schaeffer DNP, APRN (is Clinical Nurse Specialist, Department of Emergency Medicine, VUMC.), Randy Cox MPH (is Senior Patient Safety Advisor, Department of Quality, Safety and Risk Prevention, VUMC.), Emily Tew BSN, RN (is Assistant Nurse Manager, Department of Emergency Medicine, VUMC.), Kaitlyn Jensen BSN, RN (is Registered Nurse, Department of Emergency Medicine, VUMC.), Kimberley Smith BSN, RN (is Registered Nurse, Department of Emergency Medicine, VUMC.), Mitchell Sexton MBA (is Principal Analytics Consultant, Enterprise Analytics, VUMC.), Brian Bales MD (is Assistant Professor and Assistant Quality Medical Director, Department of Emergency Medicine, VUMC.), Amina Belghit MA (is Research Analyst, Department of Emergency Medicine, VUMC.), Jonathan W. Andereck MD, MBA (is Assistant Professor and Quality Medical Director, Department of Emergency Medicine, VUMC.), David P. Johnson MD (is Professor and Director of Quality, Department of Pediatrics, VUMC.), J. Christopher Champion MD, MBA (is Associate Professor and Executive Director for Regional Quality, Safety and Risk Prevention, Department of Emergency Medicine, VUMC.), William B. Stubblefield MD, MPH (is Assistant Professor and Emergency Medicine Physician, Department of Emergency Medicine, VUMC. Please address correspondence to William B. Stubblefield)","doi":"10.1016/j.jcjq.2025.07.009","DOIUrl":"10.1016/j.jcjq.2025.07.009","url":null,"abstract":"<div><h3>Background</h3><div>Timely diagnosis of ST-segment elevation myocardial infarction (STEMI) in the emergency department (ED) is dependent on electrocardiogram (ECG) completion. The American Heart Association recommends ECG testing within 10 minutes of arrival for patients with symptoms concerning for acute coronary syndrome. The authors aimed to increase the percentage of patients with door-to-ECG (DTE) times of < 10 minutes from 53.7% to > 75%.</div></div><div><h3>Methods</h3><div>We initiated a quality improvement project at an academic, quaternary care ED in June 2022. Patients included were adults (age > 30 years) who presented as walk-ins to ED triage with chest pain and received a cardiac troponin order. The primary measure was the percentage of patients with an ECG completed within 10 minutes of registration. Secondary measures included mean DTE time and mean time to STEMI activation. Statistical process control charts were used to analyze intervention impact.</div></div><div><h3>Results</h3><div>Successful completion of ECGs within 10 minutes increased from 53.7% to 80.0% despite rising patient volumes. Three separate centerline shifts were associated with three interventions: (1) physical relocation of a pivot nurse to identify patients on arrival and dedicated space for rapid ECG acquisition; (2) staff education and recognition of high performers; (3) increased waiting room monitoring staff. DTE time was monitored for one year with no additional interventions, and the centerline decreased to 71.3%.</div></div><div><h3>Conclusion</h3><div>The authors used rapid Plan-Do-Study-Act (PDSA) cycle changes to improve DTE within 10 minutes to > 80% before declining to 71.3% during the maintenance phase. Modification of nursing roles and positions, staff education, recognition of high performers, and increased staffing were drivers of improvement. These improvements are translatable to other departments seeking to improve DTE metrics and may be largely sustained without active surveillance or additional interventions.</div></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"51 11","pages":"Pages 701-710"},"PeriodicalIF":2.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145080691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-05DOI: 10.1016/j.jcjq.2025.07.010
Lauren Bangerter PhD, MA (is Scientific Director, Health Economics and Aging Research Institute, MedStar Health Research Institute, Columbia, Maryland, and Assistant Professor, Department of Family Medicine, Georgetown University School of Medicine.), Garrett Zabala MS (is Human Factors Research Engineer, National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, DC.), Nicole E. Werner PhD, MS (is Associate Professor, Department of Anesthesiology, and Director, Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center.), Yijung K. Kim PhD, MS (formerly Research Scientist, Health Economics and Aging Research Institute, is Senior Research Analyst, Aledade, Inc., Austin, Texas.), Katharine Adams MS (is Data Scientist, Center for Biostatistics, Informatics, and Data Science, MedStar Health Research Institute.), Allan Fong MS (is Senior Research Scientist, Center for Biostatistics, Informatics, and Data Science, MedStar Health Research Institute.), Raj Ratwani PhD, MPH (is Director, National Center for Human Factors in Healthcare, MedStar Health Research Institute, and Professor, Department of Emergency Medicine, Georgetown University School of Medicine. Please address correspondence to Lauren R. Bangerter)
Background
People living with dementia (PLWD) are hospitalized at higher rates than those without dementia and are particularly vulnerable to safety events in the hospital. This study aimed to characterize the scope of clinician-reported safety events in PLWD, identify contributing factors from the perspective of reporting clinicians, and categorize clinician recommendations for system improvement.
Methods
The authors analyzed safety events reported by clinicians between January 2018 and July 2023 through a voluntary reporting system at a 10-hospital health system in the mid-Atlantic region, representing a broad spectrum of hospitals and patient populations. A total of 1,287 clinician-reported safety events in PLWD were identified using a keyword search. Two researchers coded the event reports using validated taxonomies to classify contributing factors and clinician recommendations for improvement.
Results
The most common clinician-reported safety events among PLWD were skin/tissue injuries (59.4%), falls (17.2%), and safety/security issues (6.9%). The most frequently cited contributing factors were situational factors (70.0%) and active failures (11.2%). Most clinician reports (65.6%) did not include any recommendation for improvement; 30.0% included person-based recommendations, and only 4.4% included system-based recommendations.
Conclusion
Health systems should prioritize the prevention of pressure injuries and falls—two of the most common and preventable safety events. Effective interventions should integrate both person-based (for example, staff training, patient/family education) and system-based (for example, policies, protocols) strategies to improve safety for PLWD in the hospital.
{"title":"A Multihospital Analysis of Clinician-Reported Safety Events in People Living with Dementia: Contributing Factors and System Recommendations","authors":"Lauren Bangerter PhD, MA (is Scientific Director, Health Economics and Aging Research Institute, MedStar Health Research Institute, Columbia, Maryland, and Assistant Professor, Department of Family Medicine, Georgetown University School of Medicine.), Garrett Zabala MS (is Human Factors Research Engineer, National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, DC.), Nicole E. Werner PhD, MS (is Associate Professor, Department of Anesthesiology, and Director, Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center.), Yijung K. Kim PhD, MS (formerly Research Scientist, Health Economics and Aging Research Institute, is Senior Research Analyst, Aledade, Inc., Austin, Texas.), Katharine Adams MS (is Data Scientist, Center for Biostatistics, Informatics, and Data Science, MedStar Health Research Institute.), Allan Fong MS (is Senior Research Scientist, Center for Biostatistics, Informatics, and Data Science, MedStar Health Research Institute.), Raj Ratwani PhD, MPH (is Director, National Center for Human Factors in Healthcare, MedStar Health Research Institute, and Professor, Department of Emergency Medicine, Georgetown University School of Medicine. Please address correspondence to Lauren R. Bangerter)","doi":"10.1016/j.jcjq.2025.07.010","DOIUrl":"10.1016/j.jcjq.2025.07.010","url":null,"abstract":"<div><h3>Background</h3><div>People living with dementia (PLWD) are hospitalized at higher rates than those without dementia and are particularly vulnerable to safety events in the hospital. This study aimed to characterize the scope of clinician-reported safety events in PLWD, identify contributing factors from the perspective of reporting clinicians, and categorize clinician recommendations for system improvement.</div></div><div><h3>Methods</h3><div>The authors analyzed safety events reported by clinicians between January 2018 and July 2023 through a voluntary reporting system at a 10-hospital health system in the mid-Atlantic region, representing a broad spectrum of hospitals and patient populations. A total of 1,287 clinician-reported safety events in PLWD were identified using a keyword search. Two researchers coded the event reports using validated taxonomies to classify contributing factors and clinician recommendations for improvement.</div></div><div><h3>Results</h3><div>The most common clinician-reported safety events among PLWD were skin/tissue injuries (59.4%), falls (17.2%), and safety/security issues (6.9%). The most frequently cited contributing factors were situational factors (70.0%) and active failures (11.2%). Most clinician reports (65.6%) did not include any recommendation for improvement; 30.0% included person-based recommendations, and only 4.4% included system-based recommendations.</div></div><div><h3>Conclusion</h3><div>Health systems should prioritize the prevention of pressure injuries and falls—two of the most common and preventable safety events. Effective interventions should integrate both person-based (for example, staff training, patient/family education) and system-based (for example, policies, protocols) strategies to improve safety for PLWD in the hospital.</div></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"51 11","pages":"Pages 711-718"},"PeriodicalIF":2.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145053497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-25DOI: 10.1016/j.jcjq.2025.07.008
Kathleen Drago MD (is Associate Professor, Division of General Internal Medicine and Geriatrics, Oregon Health & Science University.), Bryanna De Lima MPH (is Data Analyst, Division of General Internal Medicine and Geriatrics, Oregon Health & Science University. Please address correspondence to Bryanna De Lima)
Background
Hospitalized older adults are at greater risk for hospital-acquired complications than their younger counterparts. The Age-Friendly Health Systems 4Ms care delivery framework—What Matters, Mentation, Mobility, and Medication—provides evidence-based practices to improve care for older adults. This study assessed if 4Ms care in the hospital was associated with better patient outcomes and lower costs.
Methods
The authors retrospectively analyzed adults aged 65 years and older hospitalized at an academic hospital from September 2020 through December 2023 based on age-friendly status. Primary outcomes were length of stay (LOS), total charges, and 30-day hospital and emergency department (ED) readmissions. Linear regression models were used for LOS and total charges. Survival analyses and Cox proportional hazards models analyzed the 30-day hospital and ED readmissions. All models used propensity score matching to minimize confounding. Subgroup analyses were based on high and low case mix index (CMI).
Results
The sample included 20,202 admissions for patients aged 65 years and older. The hospitalized older adults receiving 4Ms care had 15.5% lower hospital charges (95% confidence interval [CI] 13.02–17.92), 5.2% shorter stays (95% CI 2.91–7.37), and had a 10.4% lower rate of hospital and ED readmissions (hazard ratio 0.90, 95% CI 0.84–0.95) than those not receiving 4Ms care. The 4Ms recipients with a higher CMI had lower charges, shorter lengths of stay, and a lower risk of readmission than recipients with a lower CMI.
Conclusion
The 4Ms care delivery framework was associated with reduced inpatient utilization and overall cost of care. These results support reliable delivery of the 4Ms to benefit older hospitalized adults.
背景：住院的老年人发生医院获得性并发症的风险高于年轻人。老年人友好型卫生系统4Ms护理提供框架——“重要的是什么”、“心理状态”、“行动能力”和“药物”——为改善老年人护理提供了循证实践。这项研究评估了在医院的4Ms护理是否与更好的患者预后和更低的成本有关。方法：作者回顾性分析了2020年9月至2023年12月在某学术医院住院的65岁及以上老年人。主要结局是住院时间（LOS）、总费用和30天医院和急诊部（ED）再入院。LOS和总收费采用线性回归模型。生存分析和Cox比例风险模型分析了30天住院和急诊室再入院情况。所有模型都使用倾向评分匹配来最小化混淆。亚组分析基于高、低病例混合指数（CMI）。结果：样本包括20,202例入院的65岁及以上患者。与未接受4Ms护理的老年人相比，接受4Ms护理的住院老年人住院费用降低15.5%(95%可信区间[CI] 13.02-17.92)，住院时间缩短5.2% (95% CI 2.91-7.37)，住院和急诊科再入院率降低10.4%（风险比0.90,95% CI 0.84-0.95）。与CMI较低的接受者相比，CMI较高的4Ms接受者的费用较低，住院时间较短，再入院风险较低。结论：4Ms护理交付框架与降低住院利用率和总体护理成本有关。这些结果支持4Ms的可靠输送，使住院的老年人受益。
{"title":"Association of Age-Friendly Hospital Care and Patient Outcomes for Older Adults","authors":"Kathleen Drago MD (is Associate Professor, Division of General Internal Medicine and Geriatrics, Oregon Health & Science University.), Bryanna De Lima MPH (is Data Analyst, Division of General Internal Medicine and Geriatrics, Oregon Health & Science University. Please address correspondence to Bryanna De Lima)","doi":"10.1016/j.jcjq.2025.07.008","DOIUrl":"10.1016/j.jcjq.2025.07.008","url":null,"abstract":"<div><h3>Background</h3><div>Hospitalized older adults are at greater risk for hospital-acquired complications than their younger counterparts. The Age-Friendly Health Systems 4Ms care delivery framework—What Matters, Mentation, Mobility, and Medication—provides evidence-based practices to improve care for older adults. This study assessed if 4Ms care in the hospital was associated with better patient outcomes and lower costs.</div></div><div><h3>Methods</h3><div>The authors retrospectively analyzed adults aged 65 years and older hospitalized at an academic hospital from September 2020 through December 2023 based on age-friendly status. Primary outcomes were length of stay (LOS), total charges, and 30-day hospital and emergency department (ED) readmissions. Linear regression models were used for LOS and total charges. Survival analyses and Cox proportional hazards models analyzed the 30-day hospital and ED readmissions. All models used propensity score matching to minimize confounding. Subgroup analyses were based on high and low case mix index (CMI).</div></div><div><h3>Results</h3><div>The sample included 20,202 admissions for patients aged 65 years and older. The hospitalized older adults receiving 4Ms care had 15.5% lower hospital charges (95% confidence interval [CI] 13.02–17.92), 5.2% shorter stays (95% CI 2.91–7.37), and had a 10.4% lower rate of hospital and ED readmissions (hazard ratio 0.90, 95% CI 0.84–0.95) than those not receiving 4Ms care. The 4Ms recipients with a higher CMI had lower charges, shorter lengths of stay, and a lower risk of readmission than recipients with a lower CMI.</div></div><div><h3>Conclusion</h3><div>The 4Ms care delivery framework was associated with reduced inpatient utilization and overall cost of care. These results support reliable delivery of the 4Ms to benefit older hospitalized adults.</div></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"51 11","pages":"Pages 695-700"},"PeriodicalIF":2.4,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-23DOI: 10.1016/j.jcjq.2025.07.007
Eric G. Poon MD, MPH (is Chief Health Information Officer, Duke University Health System, Durham, North Carolina, and Professor, Department of Medicine and Department of Biostatistics and Bioinformatics, Duke University School of Medicine.), Andrew L. Rosenberg MD (is Chief Information Officer, Michigan Medicine, Ann Arbor, Michigan.), Adam B. Landman MD, MS, MIS, MHS (is Chief Information Officer, Mass General Brigham, Boston, and Associate Professor of Emergency Medicine, Harvard Medical School.), Tejal K. Gandhi MD, MPH (is Chief Safety and Transformation Officer, Press Ganey Associates LLC, Boston. Please address correspondence to Eric G. Poon)
Background
The US healthcare system is currently facing significant challenges in quality, affordability, and labor shortages. Artificial intelligence (AI) promises to transform healthcare delivery by making it safer, more effective, less wasteful, and more patient-centered. With more than $30 billion invested in healthcare AI companies in the past three years, the proliferation of AI solutions is expected to bring much-needed relief to the strained healthcare industry. To harness the current enthusiasm for AI in healthcare, we can draw parallels to the adoption of electronic health records (EHRs) under the HITECH Act of 2009. EHR adoption has been widespread and has contributed to significant health information technology spending, but it has also brought unintended consequences, such as clinician burnout, workarounds, and mixed impacts on patient safety and quality measures.
The EHR Era vs. the AI Era: Differences
This article grounds the discussion by first reviewing the key differences between the EHR implementation era that followed the passage of HITECH and the current AI era. The authors identified three characteristics of the AI era that distinguish it from the EHR implementation era: different regulatory and legislative context, diminished capacity of the workforce to absorb new work, and an accelerated pace of change.
Lessons from EHR Implementation to Carry Forward to AI Implementation
Based on the collective experience of the authorship team and published literature on EHR and AI implementation, the authors identified five critical lessons from the EHR implementation era that organizations deploying AI must consider: (1) respect the human element, (2) build strong organizational governance, (3) adapt leadership and culture, (4) ready the workforce, and (5) build for the long term.
Conclusion
By applying these lessons, organizational leaders can realize the potential of AI to improve patient outcomes and transform healthcare delivery.
{"title":"Déjà Vu? How Might Lessons Learned from Electronic Health Record Implementation Apply to Artificial Intelligence?","authors":"Eric G. Poon MD, MPH (is Chief Health Information Officer, Duke University Health System, Durham, North Carolina, and Professor, Department of Medicine and Department of Biostatistics and Bioinformatics, Duke University School of Medicine.), Andrew L. Rosenberg MD (is Chief Information Officer, Michigan Medicine, Ann Arbor, Michigan.), Adam B. Landman MD, MS, MIS, MHS (is Chief Information Officer, Mass General Brigham, Boston, and Associate Professor of Emergency Medicine, Harvard Medical School.), Tejal K. Gandhi MD, MPH (is Chief Safety and Transformation Officer, Press Ganey Associates LLC, Boston. Please address correspondence to Eric G. Poon)","doi":"10.1016/j.jcjq.2025.07.007","DOIUrl":"10.1016/j.jcjq.2025.07.007","url":null,"abstract":"<div><h3>Background</h3><div>The US healthcare system is currently facing significant challenges in quality, affordability, and labor shortages. Artificial intelligence (AI) promises to transform healthcare delivery by making it safer, more effective, less wasteful, and more patient-centered. With more than $30 billion invested in healthcare AI companies in the past three years, the proliferation of AI solutions is expected to bring much-needed relief to the strained healthcare industry. To harness the current enthusiasm for AI in healthcare, we can draw parallels to the adoption of electronic health records (EHRs) under the HITECH Act of 2009. EHR adoption has been widespread and has contributed to significant health information technology spending, but it has also brought unintended consequences, such as clinician burnout, workarounds, and mixed impacts on patient safety and quality measures.</div></div><div><h3>The EHR Era vs. the AI Era: Differences</h3><div>This article grounds the discussion by first reviewing the key differences between the EHR implementation era that followed the passage of HITECH and the current AI era. The authors identified three characteristics of the AI era that distinguish it from the EHR implementation era: different regulatory and legislative context, diminished capacity of the workforce to absorb new work, and an accelerated pace of change.</div></div><div><h3>Lessons from EHR Implementation to Carry Forward to AI Implementation</h3><div>Based on the collective experience of the authorship team and published literature on EHR and AI implementation, the authors identified five critical lessons from the EHR implementation era that organizations deploying AI must consider: (1) respect the human element, (2) build strong organizational governance, (3) adapt leadership and culture, (4) ready the workforce, and (5) build for the long term.</div></div><div><h3>Conclusion</h3><div>By applying these lessons, organizational leaders can realize the potential of AI to improve patient outcomes and transform healthcare delivery.</div></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"51 11","pages":"Pages 681-689"},"PeriodicalIF":2.4,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-20DOI: 10.1016/j.jcjq.2025.07.006
Courtney Sump MD, MSc (Assistant Professor, Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine.), Hadley Sauers-Ford MPH, CCRP (is Senior Clinical Research Coordinator, Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center.), Sinem Toraman Turk PhD (is Associate Research Scientist, Yale Global Health Leadership Initiative, Department of Health Policy and Management, Yale School of Public Health.), Kylee Denker MSN, RN, NE-BC (is Clinical Director, Home Care Agency and Remote Patient Monitoring, Cincinnati Children’s Hospital Medical Center.), Carlos Casillas MD, MPH (is Assistant Professor, Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine.), Joanna Thomson MD, MPH (is Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, and Professor, Department of Pediatrics, University of Cincinnati College of Medicine. Please send correspondence to Courtney Sump)
Background
Although telehealth has potential to improve access to care by eliminating barriers such as transportation and childcare, it also may result in disparate access for certain populations. The aim of this study was to gain an in-depth understanding of telehealth access at a large quaternary care children’s hospital.
Methods
This qualitative study employed purposive sampling and semistructured interviews of key personnel across our institution, including caregivers, clinical providers, and telehealth operational leads and staff. Interviews targeting access to telehealth were recorded and transcribed verbatim. Using an inductive, thematic approach, each interview was coded independently by two study team members. The authors identified preliminary themes and iteratively reviewed interviews and codes to finalize themes with illustrative quotes.
Results
The authors interviewed 25 participants and identified four themes: (1) Telehealth may perpetuate health disparities, including provider reluctance to offer telehealth to patients with a preferred language other than English; (2) Telehealth can help patients receive the right care, at the right place and time; (3) There are numerous facilitators to telehealth’s uptake, including provider and caregiver buy-in and optimal physical workspace; and (4) There are challenges in its execution that lead to decreased uptake.
Conclusion
Telehealth has many challenges to successful execution but is an integral component to providing the right care at the right place and time. This study was unique in capturing perspectives of multidisciplinary members of the healthcare team in addition to patient caregivers to provide a wide variety of perspectives on access to telehealth. The findings in this single-site, qualitative study identify that real and perceived assumptions about who is best suited for telehealth care may perpetuate health disparities and exacerbate gaps in access to care.
{"title":"Telehealth for Pediatric Patients: Facilitators, Barriers, and Impact on Disparities","authors":"Courtney Sump MD, MSc (Assistant Professor, Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine.), Hadley Sauers-Ford MPH, CCRP (is Senior Clinical Research Coordinator, Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center.), Sinem Toraman Turk PhD (is Associate Research Scientist, Yale Global Health Leadership Initiative, Department of Health Policy and Management, Yale School of Public Health.), Kylee Denker MSN, RN, NE-BC (is Clinical Director, Home Care Agency and Remote Patient Monitoring, Cincinnati Children’s Hospital Medical Center.), Carlos Casillas MD, MPH (is Assistant Professor, Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine.), Joanna Thomson MD, MPH (is Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, and Professor, Department of Pediatrics, University of Cincinnati College of Medicine. Please send correspondence to Courtney Sump)","doi":"10.1016/j.jcjq.2025.07.006","DOIUrl":"10.1016/j.jcjq.2025.07.006","url":null,"abstract":"<div><h3>Background</h3><div>Although telehealth has potential to improve access to care by eliminating barriers such as transportation and childcare, it also may result in disparate access for certain populations. The aim of this study was to gain an in-depth understanding of telehealth access at a large quaternary care children’s hospital.</div></div><div><h3>Methods</h3><div>This qualitative study employed purposive sampling and semistructured interviews of key personnel across our institution, including caregivers, clinical providers, and telehealth operational leads and staff. Interviews targeting access to telehealth were recorded and transcribed verbatim. Using an inductive, thematic approach, each interview was coded independently by two study team members. The authors identified preliminary themes and iteratively reviewed interviews and codes to finalize themes with illustrative quotes.</div></div><div><h3>Results</h3><div>The authors interviewed 25 participants and identified four themes: (1) Telehealth may perpetuate health disparities, including provider reluctance to offer telehealth to patients with a preferred language other than English; (2) Telehealth can help patients receive the right care, at the right place and time; (3) There are numerous facilitators to telehealth’s uptake, including provider and caregiver buy-in and optimal physical workspace; and (4) There are challenges in its execution that lead to decreased uptake.</div></div><div><h3>Conclusion</h3><div>Telehealth has many challenges to successful execution but is an integral component to providing the right care at the right place and time. This study was unique in capturing perspectives of multidisciplinary members of the healthcare team in addition to patient caregivers to provide a wide variety of perspectives on access to telehealth. The findings in this single-site, qualitative study identify that real and perceived assumptions about who is best suited for telehealth care may perpetuate health disparities and exacerbate gaps in access to care.</div></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"51 10","pages":"Pages 632-641"},"PeriodicalIF":2.4,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144955012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-18DOI: 10.1016/j.jcjq.2025.07.004
Elizabeth Mort MD, MPH (is Editor-in-Chief, The Joint Commission Journal on Quality and Patient Safety, and Vice President and Chief Medical Officer, Joint Commission, Oakbrook Terrace, Illinois. Please address correspondence to Dr. Elizabeth Mort)
{"title":"Remembering Lucian Leape","authors":"Elizabeth Mort MD, MPH (is Editor-in-Chief, The Joint Commission Journal on Quality and Patient Safety, and Vice President and Chief Medical Officer, Joint Commission, Oakbrook Terrace, Illinois. Please address correspondence to Dr. Elizabeth Mort)","doi":"10.1016/j.jcjq.2025.07.004","DOIUrl":"10.1016/j.jcjq.2025.07.004","url":null,"abstract":"","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"51 9","pages":"Page 514"},"PeriodicalIF":2.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144768663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-18DOI: 10.1016/j.jcjq.2025.07.003
{"title":"An Interview with Lucian Leape","authors":"","doi":"10.1016/j.jcjq.2025.07.003","DOIUrl":"10.1016/j.jcjq.2025.07.003","url":null,"abstract":"","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"51 9","pages":"Pages 515-519"},"PeriodicalIF":2.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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