Quality Management in Health Care最新文献

Background and objectives: We evaluated the prevalence of potential reinforcement of common unhealthy misinterpretations of bodily sensations in social media (YouTube videos) addressing elbow enthesopathy (eECRB, enthesopathy of the extensor carpi radialis brevis, tennis elbow).

Methods: We recorded video metric data on 139 unique YouTube videos when searching "lateral epicondylitis" and "tennis elbow." We designed a rubric to assess the level of potential reinforcement of unhelpful thinking in videos about eECRB. Informational quality was scored with an adapted version of the DISCERN instrument. We then assessed the factors associated with these scores.

Results: Sixty-five percent (91 of 139) of videos contained information reinforcing at least one common misconception regarding eECRB. Potential reinforcement of misconceptions was associated with longer video duration, higher likes per day, and higher likes per view. No factors were associated with information quality scores.

Conclusions: These findings of a high prevalence of potential reinforcement of misconceptions in YouTube videos, in combination with the known associations of misconceptions with greater discomfort and incapability, point to the potential of such videos to harm health. Producers of patient facing health material can add avoidance of reinforcement of unhelpful thinking along with readability, accuracy, and relevance as a guiding principle.

Background and objectives: Cataract surgery is common and low-risk. Preoperative evaluation and preoperative testing have not been shown to improve patient outcomes but do increase cost. Our process improvement aimed to reduce unnecessary preoperative primary care evaluation for cataract surgery.

Methods: We implemented a simple process involving a brief chart review and conversation with the patient to determine the appropriateness of preoperative primary care evaluations. After implementation of the screening tool, we reviewed 100 patient charts, 50 who underwent cataract surgery prior to and 50 after the intervention.

Results: The screening tool resulted in a decrease in primary care provider referrals from 100% to 4% and a decrease in primary care provider evaluation from 94% to 6%.

Conclusions: Implementation of a simple screening tool resulted in a dramatic decrease in unnecessary primary care preoperative testing.

Background and objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Background and objectives: 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.

Methods: 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.

Results: 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).

Conclusions: 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.

Background and objectives: Compliance rates for follow-up appointments are an issue for postoperative sleeve gastrectomy (SG) patients. Without consistent reinforcement and monitoring of patient progress, patients tend to gain the weight back, all of the medical improvements made are lost, and the ability to access patients for potential complications is denied. Patients need much reinforcement during their forever bariatric lifestyle, and the lack of consistent reminders may contribute to follow-up noncompliance and recidivism in SG patients. As time progresses, the follow-up appointment compliance rate decreases. Decreased follow-up can lead to a higher risk for complications such as asymptomatic esophagitis, and current recommendations suggest that esophagogastroduodenoscopy screening should occur 3 years postoperatively. After 1 year, the follow-up compliance decreases dramatically so that by the 3-year postoperative period, very few patients are being seen and scheduled for interventions such as an esophagogastroduodenoscopy. The objective of this quality improvement project was to evaluate the effectiveness of a patient educational handout on SG bariatric patient follow-up visit compliance.

Methods: A quasi-experimental design and retrospective chart review was chosen. An educational handout was developed. Preintervention retrospective chart review consisted of 441 SG patients expecting a follow-up in 12 to 48 months. Postintervention included 3 months of the handout intervention with data collection totaling 198 patients.

Results: Follow-up compliance for 4 year visits noted 0% preintervention/12.2% postintervention ( P = .008), for 3 year visits 13.4% preintervention/12% postintervention ( P = .846), for 2 year visits 26.3% preintervention/28.6% postintervention ( P = .755), for 18 months visits 26.3% preintervention/32.6% postintervention ( P = .365), and for 12 months visits 54.2% preintervention/34.1% postintervention ( P = .011).

Conclusions: In this quality improvement project, educational handouts did not have a statistical impact on follow-up compliance.

Background and objectives: Basal cell carcinoma (BCC) is the most common form of skin cancer, originating from basal cells in the skin's outer layer. It frequently arises from prolonged exposure to ultraviolet (UV) radiation from the sun or tanning beds. Although BCC rarely metastasizes, it can cause significant local tissue damage if left untreated. Early detection is essential to prevent extensive damage and potential disfigurement. The United States Preventive Services Task Force (USPSTF) currently remains uncertain about the benefits and potential harms of routine skin cancer screenings in asymptomatic individuals. This paper evaluates the accuracy of predicting BCC using patients' medical histories to address this uncertainty and support early detection efforts.

Methods: We analyzed the medical histories of 405,608 patients, including 7733 with BCC. We categorized 25,154 diagnoses into 16 body systems based on the hierarchy in the Systematized Nomenclature of Medicine (SNOMED) ontology. For each body system, we identified the most severe condition present. Logistic Least Absolute Shrinkage and Selection Operator (LASSO) regression was then employed to predict BCC, using demographic information, body systems, and pairwise and triple combinations of body systems, as well as missing value indicators. The dataset was split into 90% for training and 10% for validation. Model performance was evaluated using McFadden's R 2 , Percentage Deviance Explained (PDE), and cross-validated with the area under the receiver operating characteristic curve (AUC).

Results: Diagnoses related to the Integument system showed an 8-fold higher likelihood of being associated with BCC compared to diagnoses related to other systems. Older (age from 60 to 69) white individuals were more likely to receive a BCC diagnosis. After training the model, it achieved a McFadden's R 2 of 0.286, an AUC of 0.912, and a PDE of 28.390%, reflecting a high level of explained variance and prediction accuracy.

Conclusions: This study underscores the potential of LASSO Regression models to enhance early identification of BCC. Extant medical history of patients, available in electronic health records, can accurately predict the risk of BCC. Integrating such predictive models into clinical practice could significantly improve early detection and intervention.

Background and objectives: Lung cancer causes 130 000 deaths annually in the United States, with treatment costs averaging $150 000 per patient and a 5-year survival rate of 20.5%. Current screening criteria rely on smoking history and age, missing other risk factors. This study aimed to identify clinical risk factors and social determinants of health (SDoH) for enhanced risk assessment using electronic health record (EHR) data.

Methods: We analyzed 410 298 patient records from the All of Us Research Program, including 9375 lung cancer cases identified through SNOMED coding. Using Logistic LASSO regression, we developed predictive models based on diagnoses grouped by body systems and their interactions.

Results: Respiratory, cardiovascular, and immune systems showed three-fold greater association with lung cancer than other systems. Brain metastasis showed the strongest association (odds ratio 5.0, 95% CI: 4.2-5.8). The final model achieved an AUC of 0.82 (95% CI: 0.80-0.84) and 78% sensitivity in validation. Patients with documented social determinants showed 2.5-fold higher risk (95% CI: 2.1-2.9).

Conclusions: EHR-based prediction models effectively identify lung cancer risk using readily available medical history data. These findings support expanding current screening criteria beyond traditional risk factors.

Background and objectives: Health information technology (HIT) for patient-engagement can positively influence the quality and efficiency of health care delivery. Although this topic is of significant importance, it has not been fully addressed in the federally qualified health center (FQHC) context. This research investigates the relationship between the level of patient-engagement HIT and FQHC preventive health care quality outcomes.

Methods: Based on the Uniform Data System (UDS), this study employed multivariable regression analysis to investigate the association between the level of patient-engagement HIT and FQHC preventive health care quality outcomes. FQHCs were placed in 4 mutually exclusive groups based on the level of FQHC use of patient-engagement HIT.

Results: The results indicate that compared with the most comprehensive patient-engagement HIT at FQHCs, less comprehensive patient-engagement HIT was associated with lower rates of preventive care provision.

Conclusions: Comprehensive patient-engagement HIT across FQHCs may improve preventive health care quality outcomes. The results support policy incentives for FQHCs with less comprehensive levels of patient-engagement HIT to foster improved preventive care for their patients.