Pub Date : 2024-08-30DOI: 10.1016/j.jcjq.2024.08.007
Shravan Asthana, Luis Gago, Joshua Garcia, Molly Beestrum, Teresa Pollack, Lori Post, Cynthia Barnard, Mita Sanghavi Goel
Background: Housing instability in the United States is a critical social determinant of health, influencing health outcomes and health care utilization. This scoping review aimed to analyze literature on US health system screening and response programs addressing housing instability, highlighting methodologies, geographic and demographic variations, and policy implications.
Methods: Adhering to PRISMA-ScR guidelines, the review included studies focusing on US health systems that screen and refer for housing instability. Major scholarly databases, including PubMed and Scopus, were queried. Screening and response program characteristics, methodologies, and outcomes were characterized.
Results: Thirty studies published between 2003 and 2023 were included in this study. Included studies were primarily cross-sectional (26.7%) or quality improvement (20.0%), among 9 other designs. Screening programs were predominantly implemented in academic hospital systems (46.7%) and in the Northeast (63.3%). Of the 25 adult population studies, 68.0% were in outpatient settings, and of the 23 studies providing detailed information on their process, 52.2% used electronic health record entry. Of the 22 studies that describe their screening tool, 15 used institution-specific tools, and only 4 of the remaining 7 studies used identical tools. Of the 20 studies that described their response to positive screenings, 13 provided patients with a paper or electronic referral to a collaborating community partner, while only 6 aided the patient in connecting with community resources.
Conclusion: This study found significant variability in screening and response programs for housing instability among US health care providers. A lack of standardized definitions and methodologies hampers effective comparison and implementation of these programs. Future research should focus on standardizing screening methods and measurement of interventions and outcomes to address housing instability.
{"title":"Housing Instability Screening and Referral Programs: A Scoping Review.","authors":"Shravan Asthana, Luis Gago, Joshua Garcia, Molly Beestrum, Teresa Pollack, Lori Post, Cynthia Barnard, Mita Sanghavi Goel","doi":"10.1016/j.jcjq.2024.08.007","DOIUrl":"https://doi.org/10.1016/j.jcjq.2024.08.007","url":null,"abstract":"<p><strong>Background: </strong>Housing instability in the United States is a critical social determinant of health, influencing health outcomes and health care utilization. This scoping review aimed to analyze literature on US health system screening and response programs addressing housing instability, highlighting methodologies, geographic and demographic variations, and policy implications.</p><p><strong>Methods: </strong>Adhering to PRISMA-ScR guidelines, the review included studies focusing on US health systems that screen and refer for housing instability. Major scholarly databases, including PubMed and Scopus, were queried. Screening and response program characteristics, methodologies, and outcomes were characterized.</p><p><strong>Results: </strong>Thirty studies published between 2003 and 2023 were included in this study. Included studies were primarily cross-sectional (26.7%) or quality improvement (20.0%), among 9 other designs. Screening programs were predominantly implemented in academic hospital systems (46.7%) and in the Northeast (63.3%). Of the 25 adult population studies, 68.0% were in outpatient settings, and of the 23 studies providing detailed information on their process, 52.2% used electronic health record entry. Of the 22 studies that describe their screening tool, 15 used institution-specific tools, and only 4 of the remaining 7 studies used identical tools. Of the 20 studies that described their response to positive screenings, 13 provided patients with a paper or electronic referral to a collaborating community partner, while only 6 aided the patient in connecting with community resources.</p><p><strong>Conclusion: </strong>This study found significant variability in screening and response programs for housing instability among US health care providers. A lack of standardized definitions and methodologies hampers effective comparison and implementation of these programs. Future research should focus on standardizing screening methods and measurement of interventions and outcomes to address housing instability.</p>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142400262","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 : 2024-08-24DOI: 10.1016/j.jcjq.2024.08.006
Christine D. Franciscovich MSN, CRNP, NNP-BC (is the Patient Safety and Improvement Advanced Practice Provider, Children's Hospital of Philadelphia.), Anna Bieniek BS, PharmD, MS (is the Pharmacy Regulatory Compliance, Quality Assurance, and Medication Safety Program Manager, Children's Hospital of Philadelphia.), Katie Dunn BSN, RN, CPN (is a Certified Pediatric Nurse, Children's Hospital of Philadelphia.), Ursula Nawab MD (formerly Senior Medical Director of Patient Safety, Children's Hospital of Philadelphia, is Chief Patient Safety and Quality Officer, Johns Hopkins All Children's Hospital. Please address correspondence to Christine D. Franciscovich)
Background
Automated dispensing cabinets (ADCs) are used to store and dispense medications at the point of care. Medications accessed from an ADC before pharmacist order verification are removed using override functionality. Bypassing pharmacist verification can lead to medication errors; therefore, The Joint Commission considers overrides acceptable only in limited scenarios. During an 18-month period, the override rate in our perianesthesia care unit (PACU) was 17%, with oral midazolam accounting for roughly 40% of overrides. A multidisciplinary quality improvement (QI) project was initiated with a goal to reduce overrides by 10% (17% to 15%) by December 31, 2021.
Methods
Key drivers for reducing overrides included timely medication order entry, nursing practice to wait for verification, and timely pharmacist medication order verification. Interventions related to the latter two drivers included nursing education, individual interviews, and a workflow change involving nurse-to-pharmacy communication prior to medication overrides. Interventions were implemented in three Plan-Do-Study-Act cycles beginning in July 2021. Outcome metrics were average monthly percentage of total medication overrides and overrides for oral midazolam, which were analyzed using statistical process control charts.
Results
Following interventions, the average monthly percentage of total medication overrides decreased from 17% to 8% in July 2021, and further to 4% in February 2022. Oral midazolam overrides decreased from 22% to 9% in July 2021, and further to 3% in February 2022.
Conclusion
Both total and oral midazolam overrides were reduced by changing nursing and pharmacy workflow. Reducing ADC overrides is a complex process balancing operational flow and safety efforts.
{"title":"Reducing Automated Dispensing Cabinet Overrides in the Perianesthesia Care Unit: A Quality Improvement Project","authors":"Christine D. Franciscovich MSN, CRNP, NNP-BC (is the Patient Safety and Improvement Advanced Practice Provider, Children's Hospital of Philadelphia.), Anna Bieniek BS, PharmD, MS (is the Pharmacy Regulatory Compliance, Quality Assurance, and Medication Safety Program Manager, Children's Hospital of Philadelphia.), Katie Dunn BSN, RN, CPN (is a Certified Pediatric Nurse, Children's Hospital of Philadelphia.), Ursula Nawab MD (formerly Senior Medical Director of Patient Safety, Children's Hospital of Philadelphia, is Chief Patient Safety and Quality Officer, Johns Hopkins All Children's Hospital. Please address correspondence to Christine D. Franciscovich)","doi":"10.1016/j.jcjq.2024.08.006","DOIUrl":"10.1016/j.jcjq.2024.08.006","url":null,"abstract":"<div><h3>Background</h3><div>Automated dispensing cabinets (ADCs) are used to store and dispense medications at the point of care. Medications accessed from an ADC before pharmacist order verification are removed using override functionality. Bypassing pharmacist verification can lead to medication errors; therefore, The Joint Commission considers overrides acceptable only in limited scenarios. During an 18-month period, the override rate in our perianesthesia care unit (PACU) was 17%, with oral midazolam accounting for roughly 40% of overrides. A multidisciplinary quality improvement (QI) project was initiated with a goal to reduce overrides by 10% (17% to 15%) by December 31, 2021.</div></div><div><h3>Methods</h3><div>Key drivers for reducing overrides included timely medication order entry, nursing practice to wait for verification, and timely pharmacist medication order verification. Interventions related to the latter two drivers included nursing education, individual interviews, and a workflow change involving nurse-to-pharmacy communication prior to medication overrides. Interventions were implemented in three Plan-Do-Study-Act cycles beginning in July 2021. Outcome metrics were average monthly percentage of total medication overrides and overrides for oral midazolam, which were analyzed using statistical process control charts.</div></div><div><h3>Results</h3><div>Following interventions, the average monthly percentage of total medication overrides decreased from 17% to 8% in July 2021, and further to 4% in February 2022. Oral midazolam overrides decreased from 22% to 9% in July 2021, and further to 3% in February 2022.</div></div><div><h3>Conclusion</h3><div>Both total and oral midazolam overrides were reduced by changing nursing and pharmacy workflow. Reducing ADC overrides is a complex process balancing operational flow and safety efforts.</div></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"50 12","pages":"Pages 867-876"},"PeriodicalIF":2.3,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390713","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 : 2024-08-24DOI: 10.1016/j.jcjq.2024.08.005
Sonali Shambhu MPH (formerly Senior Researcher, Elevance Health Public Policy Institute, is Senior Researcher, Pfizer.) , Aliza S. Gordon MPH (is Director, Health Services Research, Elevance Health Public Policy Institute.) , Ying Liu PhD (formerly Senior Researcher, Elevance Health Public Policy Institute, is Senior Manager, CORDS Oncology, Bristol Myers Squibb.), Maximilian Pany PhD (is Researcher, Elevance Health, Medicare Clinical Operations, and MD Candidate, Harvard Medical School.), William V. Padula PhD (is Assistant Professor, Department of Pharmaceutical and Health Economnics, Schaeffer Center, University of Southern California.), Peter J. Pronovost MD, PhD (is Chief Quality and Clinical Transformation Officer, University Hospitals Cleveland Medical Center.), Eugene Hsu MD, MBA (is Chief Medical Officer and Regional Vice President, Elevance Health, Medicare Clinical Operations, and Adjunct Faculty, Stanford University School of Medicine. Please address correspondence to Aliza S. Gordon)
Objective
To assess the additional health care utilization, cost, and mortality resulting from three surgical site infections (SSIs): mediastinitis/SSI after coronary artery bypass graft, SSI after bariatric surgery for obesity, and SSI after certain orthopedic procedures.
Methods
This retrospective observational cohort study used commercial and Medicare Advantage/Supplement claims from 2016 to 2021. Patients with one of three SSIs were compared to a 1:1 propensity score-matched group of patients with the same surgeries but without SSI on outcomes up to one year postdischarge.
Results
The total sample size was 4,620. Compared to their matched cohorts, the three SSI cohorts had longer mean index inpatient length of stay (LOS; adjusted days difference ranged from 1.73 to 6.27 days, all p < 0.001) and higher 30-day readmission rates (adjusted odds ratio ranged from 2.83 to 25.07, all p ≤ 0.001). The SSI cohort for orthopedic procedures had higher 12-month mortality (hazard ratio 1.56, p = 0.01), though other cohorts did not have significant differences. Total medical costs were higher in all three SSI cohorts vs. matched comparison cohorts for the index episode and 6 months and 1 year postdischarge. Average adjusted 1-year total medical cost differences ranged from $40,606 to $68,101 per person, depending on the cohort (p < 0.001), with out-of-pocket cost differences ranging from $330 to $860 (p < 0.05).
Conclusion
Patients with SSIs experienced higher LOS, readmission rates, and total medical costs, and higher mortality for some populations, compared to their matched comparison cohorts during the first year postdischarge. Identifying strategies to reduce SSIs is important both for patient outcomes and affordability of care.
{"title":"The Burden of Health Care Utilization, Cost, and Mortality Associated with Select Surgical Site Infections","authors":"Sonali Shambhu MPH (formerly Senior Researcher, Elevance Health Public Policy Institute, is Senior Researcher, Pfizer.) , Aliza S. Gordon MPH (is Director, Health Services Research, Elevance Health Public Policy Institute.) , Ying Liu PhD (formerly Senior Researcher, Elevance Health Public Policy Institute, is Senior Manager, CORDS Oncology, Bristol Myers Squibb.), Maximilian Pany PhD (is Researcher, Elevance Health, Medicare Clinical Operations, and MD Candidate, Harvard Medical School.), William V. Padula PhD (is Assistant Professor, Department of Pharmaceutical and Health Economnics, Schaeffer Center, University of Southern California.), Peter J. Pronovost MD, PhD (is Chief Quality and Clinical Transformation Officer, University Hospitals Cleveland Medical Center.), Eugene Hsu MD, MBA (is Chief Medical Officer and Regional Vice President, Elevance Health, Medicare Clinical Operations, and Adjunct Faculty, Stanford University School of Medicine. Please address correspondence to Aliza S. Gordon)","doi":"10.1016/j.jcjq.2024.08.005","DOIUrl":"10.1016/j.jcjq.2024.08.005","url":null,"abstract":"<div><h3>Objective</h3><div>To assess the additional health care utilization, cost, and mortality resulting from three surgical site infections (SSIs): mediastinitis/SSI after coronary artery bypass graft, SSI after bariatric surgery for obesity, and SSI after certain orthopedic procedures.</div></div><div><h3>Methods</h3><div>This retrospective observational cohort study used commercial and Medicare Advantage/Supplement claims from 2016 to 2021. Patients with one of three SSIs were compared to a 1:1 propensity score-matched group of patients with the same surgeries but without SSI on outcomes up to one year postdischarge.</div></div><div><h3>Results</h3><div>The total sample size was 4,620. Compared to their matched cohorts, the three SSI cohorts had longer mean index inpatient length of stay (LOS; adjusted days difference ranged from 1.73 to 6.27 days, all <em>p</em> < 0.001) and higher 30-day readmission rates (adjusted odds ratio ranged from 2.83 to 25.07, all <em>p</em> ≤ 0.001). The SSI cohort for orthopedic procedures had higher 12-month mortality (hazard ratio 1.56, <em>p</em> = 0.01), though other cohorts did not have significant differences. Total medical costs were higher in all three SSI cohorts vs. matched comparison cohorts for the index episode and 6 months and 1 year postdischarge. Average adjusted 1-year total medical cost differences ranged from $40,606 to $68,101 per person, depending on the cohort (<em>p</em> < 0.001), with out-of-pocket cost differences ranging from $330 to $860 (<em>p</em> < 0.05).</div></div><div><h3>Conclusion</h3><div>Patients with SSIs experienced higher LOS, readmission rates, and total medical costs, and higher mortality for some populations, compared to their matched comparison cohorts during the first year postdischarge. Identifying strategies to reduce SSIs is important both for patient outcomes and affordability of care.</div></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"50 12","pages":"Pages 857-866"},"PeriodicalIF":2.3,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-13DOI: 10.1016/j.jcjq.2024.08.002
Jennifer A. Schweiger PharmD (formerly Pharmacist Resident, Children's Hospital Colorado, Aurora, Colorado, is Pediatric Infectious Diseases and Antimicrobial Stewardship Clinical Pharmacist, Atrium Health Levine Children's Hospital, Charlotte, North Carolina.), Nicole M. Poole MD, MPH (is Assistant Professor, Division of Infectious Diseases, Department of Pediatrics, University of Colorado School of Medicine, and Associate Medical Director, Antimicrobial Stewardship Program, Children's Hospital Colorado.), Sarah K. Parker MD (is Professor, Division of Infectious Diseases, Department of Pediatrics, University of Colorado School of Medicine, and Medical Director, Antimicrobial Stewardship Program, Children's Hospital Colorado.), John S. Kim MD (is Associate Professor, Division of Cardiology, Department of Pediatrics, University of Colorado School of Medicine, and Associate Medical Director, Cardiac Intensive Care Unit, Children's Hospital Colorado.), Christine E. MacBrayne PharmD, MSCS (is Clinical Pharmacy Manager, Department of Pharmacy, Children's Hospital Colorado)
{"title":"Preserving Resources: The Vital Role of Antimicrobial Stewardship Programs in Mitigating Antimicrobial Shortages","authors":"Jennifer A. Schweiger PharmD (formerly Pharmacist Resident, Children's Hospital Colorado, Aurora, Colorado, is Pediatric Infectious Diseases and Antimicrobial Stewardship Clinical Pharmacist, Atrium Health Levine Children's Hospital, Charlotte, North Carolina.), Nicole M. Poole MD, MPH (is Assistant Professor, Division of Infectious Diseases, Department of Pediatrics, University of Colorado School of Medicine, and Associate Medical Director, Antimicrobial Stewardship Program, Children's Hospital Colorado.), Sarah K. Parker MD (is Professor, Division of Infectious Diseases, Department of Pediatrics, University of Colorado School of Medicine, and Medical Director, Antimicrobial Stewardship Program, Children's Hospital Colorado.), John S. Kim MD (is Associate Professor, Division of Cardiology, Department of Pediatrics, University of Colorado School of Medicine, and Associate Medical Director, Cardiac Intensive Care Unit, Children's Hospital Colorado.), Christine E. MacBrayne PharmD, MSCS (is Clinical Pharmacy Manager, Department of Pharmacy, Children's Hospital Colorado)","doi":"10.1016/j.jcjq.2024.08.002","DOIUrl":"10.1016/j.jcjq.2024.08.002","url":null,"abstract":"","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"50 12","pages":"Pages 893-896"},"PeriodicalIF":2.3,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142466009","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 : 2024-08-10DOI: 10.1016/j.jcjq.2024.08.003
Myrna Katalina Serna MD, MPH (is Assistant Professor and Physician Research Scientist, Division of General Medicine, University of Texas Medical Branch, Galveston, Texas.), Katrina Grace Sadang MD, MPH (is Resident, Department of Family Medicine, LifeLong Medical Care, Richmond, California.), Hanna B. Vollbrecht MD (is Fellow, Section of Pulmonary and Critical Care, University of Chicago Medicine.), Catherine Yoon MS (is Senior Statistical Programmer/Analyst, Hospital Medicine Unit, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston.), Julie Fiskio (is Senior Programmer/Analyst, Hospital Medicine Unit, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital.), Joshua R. Lakin MD (is Attending Physician, Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, and Assistant Professor of Medicine, Harvard Medical School.), Anuj K. Dalal MD (is Internist, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, and Associate Professor of Medicine, Harvard Medical School.), Jeffrey L. Schnipper MD, MPH (is Research Director, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, and Professor of Medicine, Harvard Medical School. Please address correspondence to Myrna Katalina Serna)
Background
Determining which patients benefit from a serious illness conversation (SIC) is challenging. The authors sought to determine whether Epic's Risk of Readmission Score (RRS), could be combined with a simple, validated, one-question mortality prognostic screen (the surprise question: Would you be surprised if the patient died in the next 12 months?) to identify hospitalized patients with SIC needs.
Methods
In this retrospective study, the authors randomly selected encounters for patients ≥ 18 years of age to a general medicine service from January 2019 to October 2021 who had an RRS > 28%. Two adjudicators independently performed chart reviews for each encounter to answer the surprise question to create two distinct prognostic groups (yes vs. no). Fisher's exact test was used to assess for statistically significant differences in standardized documentation of SICs between groups.
Results
Out of 2,879 encounters, 202 patient encounters were randomly selected. Adjudicators answered “no” to the surprise question for 156 (77.2%) patients. Patients for whom adjudicators answered “no” were generally older with higher comorbidity and more often had standardized documentation of a SIC (14 [9.0%] vs. 0.[0.0%], p = 0.042) compared to patients for whom adjudicators answered “yes.”
Conclusion
Approximately three quarters of patients with a high RRS were predicted to have a lifespan of less than a year. Although these patients were significantly more likely to have a SIC, rates of SICs were extremely low. Combining available electronic health record (EHR) data with a simple one-question screening tool may help identify hospitalized patients who require a SIC in quality improvement initiatives.
背景：确定哪些患者可以从重症疾病谈话（SIC）中获益是一项挑战。作者试图确定 Epic 的再入院风险评分（RRS）是否能与简单、有效、一问式死亡率预后筛查（惊喜问题：如果患者在未来 12 个月内死亡，您会感到惊讶吗？如果病人在未来 12 个月内死亡，您会感到惊讶吗？），以识别有 SIC 需求的住院病人：在这项回顾性研究中，作者随机选取了 2019 年 1 月至 2021 年 10 月期间在全科医学服务机构就诊的年龄≥18 岁、RRS > 28% 的患者。两名评审员独立对每个病例进行病历审查，回答突发性问题，以创建两个不同的预后组（是与否）。采用费雪精确检验来评估各组之间 SIC 标准化记录的统计学差异：在 2,879 个病例中，随机抽取了 202 个病例。有 156 名患者（77.2%）的意外问题回答 "否"。与裁决者回答 "是 "的患者相比，裁决者回答 "否 "的患者一般年龄较大，合并症较多，且有标准化 SIC 文档的患者较多（14 [9.0%] vs. 0.[0.0%], p = 0.042）：RRS较高的患者中，约有四分之三预计寿命不足一年。虽然这些患者发生SIC的几率明显更高，但SIC的发生率极低。将现有的电子健康记录（EHR）数据与简单的一问一答筛查工具相结合，可能有助于在质量改进措施中识别需要SIC的住院患者。
{"title":"Identification of Hospitalized Patients Who May Benefit from a Serious Illness Conversation Using the Readmission Risk Score Combined with the Surprise Question","authors":"Myrna Katalina Serna MD, MPH (is Assistant Professor and Physician Research Scientist, Division of General Medicine, University of Texas Medical Branch, Galveston, Texas.), Katrina Grace Sadang MD, MPH (is Resident, Department of Family Medicine, LifeLong Medical Care, Richmond, California.), Hanna B. Vollbrecht MD (is Fellow, Section of Pulmonary and Critical Care, University of Chicago Medicine.), Catherine Yoon MS (is Senior Statistical Programmer/Analyst, Hospital Medicine Unit, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston.), Julie Fiskio (is Senior Programmer/Analyst, Hospital Medicine Unit, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital.), Joshua R. Lakin MD (is Attending Physician, Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, and Assistant Professor of Medicine, Harvard Medical School.), Anuj K. Dalal MD (is Internist, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, and Associate Professor of Medicine, Harvard Medical School.), Jeffrey L. Schnipper MD, MPH (is Research Director, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, and Professor of Medicine, Harvard Medical School. Please address correspondence to Myrna Katalina Serna)","doi":"10.1016/j.jcjq.2024.08.003","DOIUrl":"10.1016/j.jcjq.2024.08.003","url":null,"abstract":"<div><h3>Background</h3><div>Determining which patients benefit from a serious illness conversation (SIC) is challenging. The authors sought to determine whether Epic's Risk of Readmission Score (RRS), could be combined with a simple, validated, one-question mortality prognostic screen (the surprise question: Would you be surprised if the patient died in the next 12 months?) to identify hospitalized patients with SIC needs.</div></div><div><h3>Methods</h3><div>In this retrospective study, the authors randomly selected encounters for patients ≥ 18 years of age to a general medicine service from January 2019 to October 2021 who had an RRS > 28%. Two adjudicators independently performed chart reviews for each encounter to answer the surprise question to create two distinct prognostic groups (yes vs. no). Fisher's exact test was used to assess for statistically significant differences in standardized documentation of SICs between groups.</div></div><div><h3>Results</h3><div>Out of 2,879 encounters, 202 patient encounters were randomly selected. Adjudicators answered “no” to the surprise question for 156 (77.2%) patients. Patients for whom adjudicators answered “no” were generally older with higher comorbidity and more often had standardized documentation of a SIC (14 [9.0%] vs. 0.[0.0%], <em>p</em> = 0.042) compared to patients for whom adjudicators answered “yes.”</div></div><div><h3>Conclusion</h3><div>Approximately three quarters of patients with a high RRS were predicted to have a lifespan of less than a year. Although these patients were significantly more likely to have a SIC, rates of SICs were extremely low. Combining available electronic health record (EHR) data with a simple one-question screening tool may help identify hospitalized patients who require a SIC in quality improvement initiatives.</div></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"50 12","pages":"Pages 842-848"},"PeriodicalIF":2.3,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142287755","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 : 2024-08-06DOI: 10.1016/j.jcjq.2024.08.001
Jeanene Johnson MPH, BSN (is Quality Improvement Advisor, Quality Improvement Department, Stanford Medicine Children's Health, Palo Alto, California.), Conner Brown BS (is Data Scientist, Stanford Medicine Children's Health.), Grace Lee MD, MPH (is Professor, Department of Pediatrics, Stanford University School of Medicine, and Chief Quality Officer, Stanford Medicine Children's Health.), Keith Morse MD, MBA (is Clinical Associate Professor, Department of Pediatrics, Stanford University School of Medicine, and Medical Director of Clinical Informatics, Stanford Medicine Children's Health)
Background
Using the data collected through incident reporting systems is challenging, as it is a large volume of primarily qualitative information. Large language models (LLMs), such as ChatGPT, provide novel capabilities in text summarization and labeling that could support safety data trending and early identification of opportunities to prevent patient harm. This study assessed the capability of a proprietary LLM (GPT-3.5) to automatically label a cross-sectional sample of real-world obstetric incident reports.
Methods
A sample of 370 incident reports submitted to inpatient obstetric units between December 2022 and May 2023 was extracted. Human-annotated labels were assigned by a clinician reviewer and considered gold standard. The LLM was prompted to label incident reports relying solely on its pretrained knowledge and information included in the prompt. Primary outcomes assessed were sensitivity, specificity, positive predictive value, and negative predictive value. A secondary outcome assessed the human-perceived quality of the model's justification for the label(s) applied.
Results
The LLM demonstrated the ability to label incident reports with high sensitivity and specificity. The model applied a total of 79 labels compared to the reviewer's 49 labels. Overall sensitivity for the model was 85.7%, and specificity was 97.9%. Positive and negative predictive values were 53.2% and 99.6%, respectively. For 60.8% of labels, the reviewer approved of the model's justification for applying the label.
Conclusion
The proprietary LLM demonstrated the ability to label obstetric incident reports with high sensitivity and specificity. LLMs offer the potential to enable more efficient use of data from incident reporting systems.
{"title":"Accuracy of a Proprietary Large Language Model in Labeling Obstetric Incident Reports","authors":"Jeanene Johnson MPH, BSN (is Quality Improvement Advisor, Quality Improvement Department, Stanford Medicine Children's Health, Palo Alto, California.), Conner Brown BS (is Data Scientist, Stanford Medicine Children's Health.), Grace Lee MD, MPH (is Professor, Department of Pediatrics, Stanford University School of Medicine, and Chief Quality Officer, Stanford Medicine Children's Health.), Keith Morse MD, MBA (is Clinical Associate Professor, Department of Pediatrics, Stanford University School of Medicine, and Medical Director of Clinical Informatics, Stanford Medicine Children's Health)","doi":"10.1016/j.jcjq.2024.08.001","DOIUrl":"10.1016/j.jcjq.2024.08.001","url":null,"abstract":"<div><h3>Background</h3><div>Using the data collected through incident reporting systems is challenging, as it is a large volume of primarily qualitative information. Large language models (LLMs), such as ChatGPT, provide novel capabilities in text summarization and labeling that could support safety data trending and early identification of opportunities to prevent patient harm. This study assessed the capability of a proprietary LLM (GPT-3.5) to automatically label a cross-sectional sample of real-world obstetric incident reports.</div></div><div><h3>Methods</h3><div>A sample of 370 incident reports submitted to inpatient obstetric units between December 2022 and May 2023 was extracted. Human-annotated labels were assigned by a clinician reviewer and considered gold standard. The LLM was prompted to label incident reports relying solely on its pretrained knowledge and information included in the prompt. Primary outcomes assessed were sensitivity, specificity, positive predictive value, and negative predictive value. A secondary outcome assessed the human-perceived quality of the model's justification for the label(s) applied.</div></div><div><h3>Results</h3><div>The LLM demonstrated the ability to label incident reports with high sensitivity and specificity. The model applied a total of 79 labels compared to the reviewer's 49 labels. Overall sensitivity for the model was 85.7%, and specificity was 97.9%. Positive and negative predictive values were 53.2% and 99.6%, respectively. For 60.8% of labels, the reviewer approved of the model's justification for applying the label.</div></div><div><h3>Conclusion</h3><div>The proprietary LLM demonstrated the ability to label obstetric incident reports with high sensitivity and specificity. LLMs offer the potential to enable more efficient use of data from incident reporting systems.</div></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"50 12","pages":"Pages 877-881"},"PeriodicalIF":2.3,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142287754","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 : 2024-08-03DOI: 10.1016/j.jcjq.2024.07.010
Della M. Lin MS, MD, FASA (is Anesthesiologist and Clinical Professor, Department of Surgery, John A Burns, School of Medicine, University of Hawaii.), Meghan B. Lane-Fall MD, MSHP (is David E. Longnecker Associate Professor of Anesthesiology and Critical Care and Associate Professor of Epidemiology, Perelman School of Medicine, University of Pennsylvania.), Joshua A. Lea DNP, MBA, CRNA (is Nurse Anesthetist, Massachusetts General Hospital, Boston.), Lynn J. Reede DNP, MBA, CRNA, FNAP (is Associate Clinical Professor and Doctor of Nursing Practice Program Director, Northeastern University.), Brandon D. Gomes DNP, CRNA (is Nurse Anesthetist, Southcoast Health, Charlton Memorial Hospital, Fall River, Massachusetts.), Yuwei Xia MD (is Anesthesia Resident, Jefferson Einstein Hospital, Philadelphia.), Jennifer A. Rock-Klotz MBA (is Manager of Analytics and Research Services, American Society of Anesthesiologists, Schaumburg, Illinois.), Thomas R. Miller PhD, MBA (is Director of Analytics and Research Services and Director, Center for Anesthesia Workforce Studies, American Society of Anesthesiologists. Please address correspondence to Della M. Lin)
Background
Workplace violence in health care has gained attention with its rising incidence and its impact on patient safety and clinician well-being. Legal and regulatory organizational requirements related to workplace violence are broadening, including updated Joint Commission standards. Although workplace violence surveys have been administered across health care settings, the few that have focused on the perioperative environment have predominantly been single-profession surveys.
Methods
This cross-sectional, prospective survey focused on perioperative care was conducted by the Anesthesia Patient Safety Foundation using simultaneous convenience sampling across professional societies representing anesthesiologist assistants, certified registered nurse anesthetists, physicians, and registered nurses. Descriptive statistics were used to summarize responses, and multivariable regression was used to model the odds of experiencing or witnessing physical or nonphysical workplace violence. Open-text entries were analyzed using thematic analysis.
Results
Of 4,662 survey respondents, 3,645 (78.2%) reported some form of workplace violence: 1,446 (31.0%) experienced physical workplace violence, 1,718 (36.9%) witnessed physical workplace violence, and 3,226 (69.2%) experienced nonphysical workplace violence. Fewer than half (49.8%) of the respondents experiencing physical workplace violence and fewer than one third (31.4%) of the respondents experiencing nonphysical workplace violence felt that the “situation was addressed and resolved to their satisfaction.”
Conclusion
Workplace violence is commonplace and reported by all perioperative professionals. There is a pressing need for actions at multiple levels to respond to and eventually eliminate perioperative workplace violence, preventing harm to both patients and staff.
{"title":"Workplace Violence Pervasiveness in the Perioperative Environment: A Multiprofessional Survey","authors":"Della M. Lin MS, MD, FASA (is Anesthesiologist and Clinical Professor, Department of Surgery, John A Burns, School of Medicine, University of Hawaii.), Meghan B. Lane-Fall MD, MSHP (is David E. Longnecker Associate Professor of Anesthesiology and Critical Care and Associate Professor of Epidemiology, Perelman School of Medicine, University of Pennsylvania.), Joshua A. Lea DNP, MBA, CRNA (is Nurse Anesthetist, Massachusetts General Hospital, Boston.), Lynn J. Reede DNP, MBA, CRNA, FNAP (is Associate Clinical Professor and Doctor of Nursing Practice Program Director, Northeastern University.), Brandon D. Gomes DNP, CRNA (is Nurse Anesthetist, Southcoast Health, Charlton Memorial Hospital, Fall River, Massachusetts.), Yuwei Xia MD (is Anesthesia Resident, Jefferson Einstein Hospital, Philadelphia.), Jennifer A. Rock-Klotz MBA (is Manager of Analytics and Research Services, American Society of Anesthesiologists, Schaumburg, Illinois.), Thomas R. Miller PhD, MBA (is Director of Analytics and Research Services and Director, Center for Anesthesia Workforce Studies, American Society of Anesthesiologists. Please address correspondence to Della M. Lin)","doi":"10.1016/j.jcjq.2024.07.010","DOIUrl":"10.1016/j.jcjq.2024.07.010","url":null,"abstract":"<div><h3>Background</h3><div>Workplace violence in health care has gained attention with its rising incidence and its impact on patient safety and clinician well-being. Legal and regulatory organizational requirements related to workplace violence are broadening, including updated Joint Commission standards. Although workplace violence surveys have been administered across health care settings, the few that have focused on the perioperative environment have predominantly been single-profession surveys.</div></div><div><h3>Methods</h3><div>This cross-sectional, prospective survey focused on perioperative care was conducted by the Anesthesia Patient Safety Foundation using simultaneous convenience sampling across professional societies representing anesthesiologist assistants, certified registered nurse anesthetists, physicians, and registered nurses. Descriptive statistics were used to summarize responses, and multivariable regression was used to model the odds of experiencing or witnessing physical or nonphysical workplace violence. Open-text entries were analyzed using thematic analysis.</div></div><div><h3>Results</h3><div>Of 4,662 survey respondents, 3,645 (78.2%) reported some form of workplace violence: 1,446 (31.0%) experienced physical workplace violence, 1,718 (36.9%) witnessed physical workplace violence, and 3,226 (69.2%) experienced nonphysical workplace violence. Fewer than half (49.8%) of the respondents experiencing physical workplace violence and fewer than one third (31.4%) of the respondents experiencing nonphysical workplace violence felt that the “situation was addressed and resolved to their satisfaction.”</div></div><div><h3>Conclusion</h3><div>Workplace violence is commonplace and reported by all perioperative professionals. There is a pressing need for actions at multiple levels to respond to and eventually eliminate perioperative workplace violence, preventing harm to both patients and staff.</div></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"50 11","pages":"Pages 764-774"},"PeriodicalIF":2.3,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142287757","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 : 2024-08-01DOI: 10.1016/j.jcjq.2024.03.004
Background
Communication failures are among the most common causes of harmful medical errors. At one Comprehensive Cancer Center, patient handoffs varied among services. The authors describe the implementation and results of an organization-wide project to improve handoffs and implement an evidence-based handoff tool across all inpatient services.
Methods
The research team created a task force composed of members from 22 hospital services—advanced practice providers (APPs), trainees, some faculty members, electronic health record (EHR) staff, education and training specialists, and nocturnal providers. Over two years, the task force expanded to include consulting services and Anesthesiology. Factors contributing to ineffective handoffs were identified and organized into categories. The EHR I-PASS tool was used to standardize handoff documentation. Training was provided to staff on its use, and compliance was monitored using a customized dashboard. I-PASS champions in each service were responsible for the rollout of I-PASS in their respective services. The data were reported quarterly to the Quality Assessment and Performance Improvement (QAPI) governing committee. Provider handoff perception was assessed through the biennial Institution-wide safety culture survey.
Results
All fellows, residents, APPs, and physician assistants were trained in the use of I-PASS, either online or in person. Adherence to the I-PASS written tool improved from 41.6% in 2019 to 70.5% in 2022 (p < 0.05), with improvements seen in most services. The frequency of updating I-PASS elements and the action list in the handoff tool also increased over time. The handoff favorability score on the safety culture survey improved from 38% in 2018 to 59% in 2022.
Conclusion
The implementation approach developed by the Provider Handoff Task Force led to increased use of the I-PASS EHR tool and improved safety culture survey handoff favorability.
{"title":"Enhancing Implementation of the I-PASS Handoff Tool Using a Provider Handoff Task Force at a Comprehensive Cancer Center","authors":"","doi":"10.1016/j.jcjq.2024.03.004","DOIUrl":"10.1016/j.jcjq.2024.03.004","url":null,"abstract":"<div><h3>Background</h3><p>Communication failures are among the most common causes of harmful medical errors. At one Comprehensive Cancer Center, patient handoffs varied among services. The authors describe the implementation and results of an organization-wide project to improve handoffs and implement an evidence-based handoff tool across all inpatient services.</p></div><div><h3>Methods</h3><p>The research team created a task force composed of members from 22 hospital services—advanced practice providers (APPs), trainees, some faculty members, electronic health record (EHR) staff, education and training specialists, and nocturnal providers. Over two years, the task force expanded to include consulting services and Anesthesiology. Factors contributing to ineffective handoffs were identified and organized into categories. The EHR I-PASS tool was used to standardize handoff documentation. Training was provided to staff on its use, and compliance was monitored using a customized dashboard. I-PASS champions in each service were responsible for the rollout of I-PASS in their respective services. The data were reported quarterly to the Quality Assessment and Performance Improvement (QAPI) governing committee. Provider handoff perception was assessed through the biennial Institution-wide safety culture survey.</p></div><div><h3>Results</h3><p>All fellows, residents, APPs, and physician assistants were trained in the use of I-PASS, either online or in person. Adherence to the I-PASS written tool improved from 41.6% in 2019 to 70.5% in 2022 (<em>p</em> < 0.05), with improvements seen in most services. The frequency of updating I-PASS elements and the action list in the handoff tool also increased over time. The handoff favorability score on the safety culture survey improved from 38% in 2018 to 59% in 2022.</p></div><div><h3>Conclusion</h3><p>The implementation approach developed by the Provider Handoff Task Force led to increased use of the I-PASS EHR tool and improved safety culture survey handoff favorability.</p></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"50 8","pages":"Pages 560-568"},"PeriodicalIF":2.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1553725024000734/pdfft?md5=2308c60ab04c20b7faee74dd13dee6aa&pid=1-s2.0-S1553725024000734-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140860828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.jcjq.2024.03.014
Background
A single dose of dexamethasone is routinely given during general anesthesia for postoperative nausea and vomiting (PONV) prophylaxis, although the exact dosage and timing of administration may vary between practitioners. The authors aimed to standardize the dosage and timing of this medication when given to adult patients undergoing general anesthesia for elective surgery.
Methods
Baseline data for 7,483 preintervention cases were analyzed. The researchers attempted to use a standard dose of 8 to 10 mg induction of anesthesia, which, based on a literature review, was effective for PONV prophylaxis, had a similar safety profile as a 4 to 5 mg dose (including in diabetic patients), and may confer additional benefits such as improved prophylaxis and quality of recovery. The interventions included standardizing the medication concentration vials, altering electronic health record quick-select button options, simplifying the intraoperative charting process, and educating the anesthesia providers. The research team then tracked compliance with the standard of care for 2,167 cases after the interventions.
Results
Overall compliance with the standard of care increased from 21.2% preintervention to 53.7% postintervention. The number of patients not receiving dexamethasone was reduced from 29.7% to 19.4%. Patients receiving a compliant dose at a noncompliant time increased from 16.3% to 23.8%. Postanesthesia care unit antiemetic administration also decreased after the interventions.
Conclusion
This study showed improvements in compliance with the dosage of medication with the interventions. However, compliance with the timing of administration remains challenging.
{"title":"Standardizing the Dosage and Timing of Dexamethasone for Postoperative Nausea and Vomiting Prophylaxis at a Safety-Net Hospital System","authors":"","doi":"10.1016/j.jcjq.2024.03.014","DOIUrl":"10.1016/j.jcjq.2024.03.014","url":null,"abstract":"<div><h3>Background</h3><p>A single dose of dexamethasone is routinely given during general anesthesia for postoperative nausea and vomiting (PONV) prophylaxis, although the exact dosage and timing of administration may vary between practitioners. The authors aimed to standardize the dosage and timing of this medication when given to adult patients undergoing general anesthesia for elective surgery.</p></div><div><h3>Methods</h3><p>Baseline data for 7,483 preintervention cases were analyzed. The researchers attempted to use a standard dose of 8 to 10 mg induction of anesthesia, which, based on a literature review, was effective for PONV prophylaxis, had a similar safety profile as a 4 to 5 mg dose (including in diabetic patients), and may confer additional benefits such as improved prophylaxis and quality of recovery. The interventions included standardizing the medication concentration vials, altering electronic health record quick-select button options, simplifying the intraoperative charting process, and educating the anesthesia providers. The research team then tracked compliance with the standard of care for 2,167 cases after the interventions.</p></div><div><h3>Results</h3><p>Overall compliance with the standard of care increased from 21.2% preintervention to 53.7% postintervention. The number of patients not receiving dexamethasone was reduced from 29.7% to 19.4%. Patients receiving a compliant dose at a noncompliant time increased from 16.3% to 23.8%. Postanesthesia care unit antiemetic administration also decreased after the interventions.</p></div><div><h3>Conclusion</h3><p>This study showed improvements in compliance with the dosage of medication with the interventions. However, compliance with the timing of administration remains challenging.</p></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"50 8","pages":"Pages 601-605"},"PeriodicalIF":2.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1553725024000989/pdfft?md5=3d73540b49a5ee104118d56bf2122222&pid=1-s2.0-S1553725024000989-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140793611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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