Quality Management in Health Care最新文献

{"title":"Reducing CLABSI Rates in Adult ICUs: A Multi-Center Performance Improvement Project (2020-2021).","authors":"Mohammad K Mhawish, Abdulrahman A Algeer, Iyad S Alyateem, Anees S Alhenn, Ahmad I Alazzam","doi":"10.1097/QMH.0000000000000512","DOIUrl":"https://doi.org/10.1097/QMH.0000000000000512","url":null,"abstract":"<p><strong>Background and objective: </strong>Central Line-Associated Bloodstream Infection (CLABSI) remains a leading cause of death among critically ill patients. Implementing preventive measures and adhering to best practices is a crucial action to proactively prevent its occurrence. This project aimed to reduce the overall CLABSIs rate in adult medical/surgical Intensive Care Units (ICUs) of hospitals under the Ministry of Defense Health Services (MODHS) in Saudi Arabia. The baseline CLABSI rate was 2 cases per 1000 catheter days during the first quarter of 2020, while the target was to achieve a rate equal to or lower than 0.8 as reported by the American National Healthcare Safety Network (NHSN) in 2013.</p><p><strong>Methods: </strong>The initiative was carried out across 15 hospitals under the purview of MODHS. Data on CLABSI incidents were collected from the ICUs dedicated to adult medical and surgical care. The project utilized the Institute for Healthcare Improvement collaborative model to achieve breakthrough improvement in a short-term learning system that facilitated the collaboration of participating hospitals in the pursuit of enhancements in CLABSI rates. The project involved 3 cycles, each consisting of a learning session followed by an action period.</p><p><strong>Results: </strong>The data revealed a continuous improvement in the overall CLABSI rate within MODHS hospitals, progressing positively for 4 consecutive quarters and attaining a value of 0.3 during the third quarter of 2021. This signifies an impressive 85% reduction from the initial baseline of 2, and the rate remains below the project benchmark of 0.8.</p><p><strong>Conclusion: </strong>The project successfully employed collaborative learning cycles, fostering effective knowledge-sharing among teams and promoting active engagement. This approach proved instrumental in achieving learning objectives, identifying gaps, and determining appropriate courses of action. Key factors for the project's success included standardizing the change package, conducting regular training sessions, encouraging open discussions, and sharing experiences.</p>","PeriodicalId":20986,"journal":{"name":"Quality Management in Health Care","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Breast Cancer Remission.","authors":"Vladimir Cardenas, Yalin Li, Samika Shrestha, Hong Xue","doi":"10.1097/QMH.0000000000000513","DOIUrl":"https://doi.org/10.1097/QMH.0000000000000513","url":null,"abstract":"<p><strong>Background and objectives: </strong>This study aims to use electronic health records (EHR) and social determinants of health (SDOH) data to predict breast cancer remission. The emphasis is placed on utilizing easily accessible information to improve predictive models, facilitate the early detection of high-risk patients, and facilitate targeted interventions and personalized care strategies.</p><p><strong>Methods: </strong>This study identifies individuals who are unlikely to respond to standard treatment of breast cancer. The study identified 1621 patients with breast cancer by selecting patients who received tamoxifen in the All of Us Research Database. The dependent variable, remission, was defined using tamoxifen exposure as a proxy. Data preprocessing involved creating dummy variables for diseases, demographic, and socioeconomic factors and handling missing values to maintain data integrity. For the feature selection phase, we utilized the strong rule for feature elimination and then logistic least absolute shrinkage and selection operator regression with 5-fold cross-validation to reduce the number of predictors by retaining only those with coefficients with an absolute value greater than 0.01. We then trained machine learning models using logistic regression, random forest, naïve Bayes, and extreme gradient boost using area under the receiver operating curve (AUROC) metric to score model performance. This created race-neutral model performance. Finally, we analyzed model performance for race and ethnicity test populations including Non-Hispanic White, Non-Hispanic Black, Hispanic, and Other Race or Ethnicity. These generated race-specific model performance.</p><p><strong>Results: </strong>The model achieved an AUROC range between 0.68 and 0.75, with logistic regression and random forest trained on data without interaction terms demonstrating the best performance. Feature selection identified significant factors such as melanocytic nevus and bone disorders, highlighting the importance of these factors in predictive accuracy. Race-specific model performance was lower than race-neutral model performance for Non-Hispanic Blacks, and Other Race and Ethnicity Groups.</p><p><strong>Conclusions: </strong>In conclusion, our research demonstrates the feasibility of predicting breast cancer non-remission using EHR and SDOH data, achieving acceptable performance without complex predictors. Addressing the data quality limitations and refining remission indicators can further improve the models' utility for early treatment decisions, fostering improved patient outcomes and support throughout the cancer journey.</p>","PeriodicalId":20986,"journal":{"name":"Quality Management in Health Care","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"USPSTF Dismisses Predictive Medicine and Data Science.","authors":"Farrokh Alemi, Kyung Hee Lee","doi":"10.1097/QMH.0000000000000528","DOIUrl":"https://doi.org/10.1097/QMH.0000000000000528","url":null,"abstract":"","PeriodicalId":20986,"journal":{"name":"Quality Management in Health Care","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EHR-Based Risk Prediction for Kidney Cancer.","authors":"Kyung Hee Lee, Farrokh Alemi, Xia Wang","doi":"10.1097/QMH.0000000000000526","DOIUrl":"https://doi.org/10.1097/QMH.0000000000000526","url":null,"abstract":"<p><strong>Background and objectives: </strong>The U.S. Preventive Services Task Force (USPSTF) does not currently recommend routine screening for kidney cancer, even though approximately 14 390 people are expected to die from this disease in the United States in 2024. Individualized risk-based kidney cancer screening offers the potential to effectively detect cancer at an early stage and avoid unnecessarily screening the rest of the population who are at low risk. This study proposes electronic health records (EHR) risk evaluation for kidney cancer by examining a comprehensive set of medical history including diagnoses, comorbidities, viruses, and rare diseases.</p><p><strong>Methods: </strong>The relevant medical history for predicting kidney cancer occurrence was identified from the analysis of All of Us data in three steps. First, a Systematized Nomenclature of Medicine (SNOMED) code binary indicator variable in EHR was set for the presence of kidney cancer. Second, the relationship between this binary indicator of cancer and all prior health conditions was examined using the Strong Rule for Feature Elimination and Least Absolute Shrinkage and Selection Operator logistic regression methods of variable selection. Third, the accuracy of the model was reported using cross-validated McFadden's R2 and Area under the Receiver Operating Characteristic curve (AROC) values.</p><p><strong>Results: </strong>The analysis identified 133 out of an initial set of 25 683 clinical diagnoses (represented by SNOMED codes) that were predictive of kidney cancer. The model achieved a cross-validated McFadden's R2 of 0.195 and an AROC of 0.799. Most of the identified codes are consistent with the known risk factors for kidney cancer.</p><p><strong>Conclusions: </strong>It is possible to accurately predict the risk of kidney cancer from medical history using this method. Additional studies to establish high-dimensional predictive risk factors are needed to see if EHR personalized risk prediction can lead to cost-effective cancer screening and eventually better clinical outcomes.</p>","PeriodicalId":20986,"journal":{"name":"Quality Management in Health Care","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Liver Cancer Risk Using Comprehensive Medical History.","authors":"Tumen Sosorburam","doi":"10.1097/QMH.0000000000000521","DOIUrl":"https://doi.org/10.1097/QMH.0000000000000521","url":null,"abstract":"<p><strong>Background: </strong>Liver cancer mortality is rising faster than any other cancer, significantly impacting life expectancy due to its relatively young median age at diagnosis and high mortality rate. There are currently no consistently recommended screening tests for liver cancer in individuals with a high-risk profile or abnormalities in body systems other than liver disease with cirrhosis. This study aims to screen various body system diseases that might be associated with liver cancer risk.</p><p><strong>Method: </strong>The study utilized the All of Us database, including 410 361 US-based adults aged 18 and above, of whom 2171 had liver cancer. Least Absolute Shrinkage and Selection Operator regression and logistic regression were used to identify significant predictors and calculate odds ratios (ORs). All statistical analyses were conducted using R software.</p><p><strong>Results: </strong>Out of the total participants, 0.5% had liver cancer diagnoses. Male gender and white race were associated with an increased risk of liver cancer (OR = 1.2). Certain diseases were strongly linked to a higher risk of liver cancer, such as liver cirrhosis, chronic steatorrhea, and neoplasms of unknown behavior in the genitourinary organs, each with an OR greater than 8. Digestive disorders, including pancreatic disorders and chronic hepatitis B and C, were also associated with an increased risk of liver cancer (OR > 4).</p><p><strong>Conclusion: </strong>The predictive model has the potential to enhance liver cancer outcomes by effectively targeting at-risk populations and by advocating for early screening among those with high-risk bodily diseases or specific diseases, which could impact survival rates.</p>","PeriodicalId":20986,"journal":{"name":"Quality Management in Health Care","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Risk of Malignant CNS Tumors From Medical History Events.","authors":"Aaron J Hill","doi":"10.1097/QMH.0000000000000497","DOIUrl":"https://doi.org/10.1097/QMH.0000000000000497","url":null,"abstract":"<p><strong>Background and objectives: </strong>Malignant brain and other central nervous system tumors (MBT) are the second leading cause of cancer death among males aged 39 years and younger, and the leading cause of cancer death among males and females younger than 20. There are few widely accepted predictors and a lack of United States Preventive Services Taskforce recommendations for MBT. This study examined how medical history could be used to assess the risk of MBT.</p><p><strong>Methods: </strong>Using over 400,000 patients' medical histories, including nearly 1,800 with MBT, Logistic Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to predict MBT. More than 25,000 diagnoses were grouped into 16 body systems, plus pairwise and triple combinations, as well as indicators for missing values. Data were split into 80/20 training and validation sets with fit and accuracy assessed using McFadden's R2 and the area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>Diagnoses of the endocrine, nervous, and lymphatic systems consistently showed greater than three times more association with MBT. The best performing model at an AUC of 0.83 consisted of 14 body system diagnosis groups and pairwise interactions among groups, in addition to demographic, social determinant of health, death, and six missing diagnosis grouping indicators.</p><p><strong>Conclusions: </strong>This study demonstrated how large data models can predict MBT in patients using EHR data. With the lack of preventive screening guidelines and known risk factors associated with MBT, predictive models provide a universal, non-invasive, and inexpensive method of identifying at-risk patients.</p>","PeriodicalId":20986,"journal":{"name":"Quality Management in Health Care","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Coming Home Intervention to Enhance Safe Hospital-to-Home Health Transitions: Pilot Evaluation.","authors":"Alicia I Arbaje, Yea-Jen Hsu, Sylvan Greyson, Kathryn H Bowles, Margaret V McDonald, Sasha Vergez, Katie Harbison, Nicole Williams, Dawn Hohl, Kimberly Carl, Ayse P Gurses, Jill A Marsteller, Bruce Leff","doi":"10.1097/QMH.0000000000000519","DOIUrl":"https://doi.org/10.1097/QMH.0000000000000519","url":null,"abstract":"<p><strong>Background: </strong>Care transitions from hospital to skilled home health care (HH) often pose safety risks, especially for older adults. The Coming Home Intervention (CHI) was developed to enhance these transitions based on the Hospital-to-Home Health Transition Quality (H3TQ) index, a previously validated survey instrument assessing quality issues during hospital-to-HH transitions.</p><p><strong>Objectives: </strong>This study aimed to pilot CHI and evaluate its impact at 2 large HH agencies in Baltimore, MD, and New York, NY.</p><p><strong>Methods: </strong>The 2 participating HH agencies implement CHI by providing HH clinicians and patients tools for expectation setting, clarification of healthcare-related roles of family and HH personnel, clinical care guides to support information management, and the H3TQ for identification of quality/safety issues. Using a quasi-experimental, before-and-after difference-in-difference design, changes before and after CHI implementation were compared between intervention and comparison groups. Quality of hospital-to-HH transitions was rated by older adults/caregivers and HH clinicians using the H3TQ before and after CHI implementation. In total, 394 responses were from older adults/caregivers and 604 responses were from HH clinicians. Outcomes including identification of medication issues and 30-day emergency department use or rehospitalization were evaluated using the Outcome and Assessment Information Set with a difference-in-difference approach (n = 3,471 in the Baltimore site; n = 758 in the New York City site). Results were analyzed and reported separately for each HH agency.</p><p><strong>Results: </strong>CHI implementation in Baltimore was associated with a statistically non-significant, decreasing trend in 30-day emergency department use or rehospitalization (odds ratio = 0.68, 95% confidence interval = 0.45-1.03). After implementation, older adults/caregivers rated quality issues measured by H3TQ less favorably. In New York City, older adults/caregivers reported fewer quality issues (incidence rate ratio = 0.50, 95% confidence interval = 0.27-0.89) after implementation. Assessment of other measures did not show significant changes.</p><p><strong>Conclusion: </strong>The pilot implementation of CHI demonstrated potential to improve hospital-to-HH transition quality. Study findings can guide future CHI implementation in larger studies in a broader population of older adults receiving HH services after hospital discharge.</p>","PeriodicalId":20986,"journal":{"name":"Quality Management in Health Care","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-Related Barriers to Medication Adherence in Uveitis Patients Enrolled in NIH's All of Us Program.","authors":"Bhoomi Dave, Maria Carolina Ibanez Bruron, Wenqing Zhang, Paulina Liberman, Meghan K Berkenstock","doi":"10.1097/QMH.0000000000000510","DOIUrl":"https://doi.org/10.1097/QMH.0000000000000510","url":null,"abstract":"<p><strong>Background and objective: </strong>To investigate cost-related barriers to medication adherence in patients with uveitis.</p><p><strong>Methods: </strong>Non-interventional, retrospective study. The study examined the responses to cost-related medication adherence questions of 879 patients with uveitis who were enrolled in the National Institutes of Health All of Us Research Program database. To be eligible for inclusion, patients were required to have successfully completed at least one self-reported survey. Logistic regression analysis was employed to assess the relationship between race/ethnicity and medication adherence, controlling for relevant covariates.</p><p><strong>Results: </strong>Patients with an annual income of less than $75 000 were significantly more likely than those with an income above $150 000 to report difficulty affording medication, delaying filling prescriptions, skipping doses, taking less medication, and exploring alternative therapies to save money. Patients aged 60 years and above were more likely to report difficulty affording medication, as were those without health insurance.</p><p><strong>Conclusion: </strong>This study revealed that income and age are barriers to medication adherence. These findings have important implications for health care providers and policymakers, who should consider strategies to address these cost-related barriers to medication adherence.</p>","PeriodicalId":20986,"journal":{"name":"Quality Management in Health Care","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Potential of Patient Stories to Advance Birth Equity.","authors":"Anjali Walia, Fiona Miller, Linda Jones, Julie Harris-Taylor, Breezy Powell, Sarah B Garrett","doi":"10.1097/QMH.0000000000000504","DOIUrl":"https://doi.org/10.1097/QMH.0000000000000504","url":null,"abstract":"","PeriodicalId":20986,"journal":{"name":"Quality Management in Health Care","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143059407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peer Audit and Feedback: A Documentation-Focused Quality Improvement Project.","authors":"Michal I Glass, Kelly Powers, Laura M Magennis, Carmen L Shaw","doi":"10.1097/QMH.0000000000000496","DOIUrl":"https://doi.org/10.1097/QMH.0000000000000496","url":null,"abstract":"<p><strong>Background and objectives: </strong>Nurses' documentation of communication, including notification of critical laboratory results (CLR), is important to ensure safe, high-quality care. Evidence supports peer audit with feedback as a quality improvement (QI) intervention to improve documentation. Nursing compliance with CLR documentation requirements was below goal for several years in an intensive care unit. To address this problem, a peer audit and feedback intervention was implemented and evaluated.</p><p><strong>Methods: </strong>Compliance with CLR documentation requirements was evaluated pre- and postintervention, for a total of 12 months. The evaluation also included data from the peer audits and a survey to assess nurses' perceptions. The 5-month intervention was a timely peer audit and feedback of CLR events.</p><p><strong>Results: </strong>CLR documentation compliance improved from 6.4% to 9.6% (50% improvement), which was clinically meaningful but not statistically significant. Nurses had overall positive perceptions of the peer audit and feedback as a QI tool, perceiving it as nonpunitive and helpful for improving practice.</p><p><strong>Conclusion: </strong>Results support continued examination of peer audit and feedback to improve nursing documentation. Future projects should address the limited time for nurses to engage in QI projects.</p>","PeriodicalId":20986,"journal":{"name":"Quality Management in Health Care","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143047633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}