Health Services Research and Managerial Epidemiology最新文献

Background: Unwarranted variation in healthcare utilization can only partly be explained by variation in the health care needs of the population, yet it is frequently found globally. This is the first cross-sectional study that systematically assessed geographic variation in the adherence to clinical recommendations in Switzerland. Specifically, we explored 1) the geographic variation of adherence to clinical recommendations across 24 health services at the sub-cantonal level, 2) assessed and mapped statistically significant spatial clusters, and 3) explored possible influencing factors for the observed geographic variation.

Methods: Exploratory spatial analysis using the Moran's I statistic on multivariable multilevel model residuals to systematically identify small area variation of adherence to clinical recommendations across 24 health services.

Results: Although there was no overall spatial pattern in adherence to clinical recommendations across all health care services, we identified health services that exhibited statistically significant spatial dependence in adherence. For these, we provided evidence about the locations of local clusters.

Interpretation: We identified regions in Switzerland in which specific recommended or discouraged health care services are utilized less or more than elsewhere. Future studies are needed to investigate the place-based social determinants of health responsible for the sub-cantonal variation in adherence to clinical recommendations in Switzerland and elsewhere over time.

Introduction: Existing research on postabortion emergency room visits is sparse and limited by methods which underestimate the incidence of adverse events following abortion. Postabortion emergency room (ER) use since Food and Drug Administration approval of chemical abortion in 2000 can identify trends in the relative morbidity burden of chemical versus surgical procedures.

Objective: To complete the first longitudinal cohort study of postabortion emergency room use following chemical and surgical abortions.

Methods: A population-based longitudinal cohort study of 423 000 confirmed induced abortions and 121,283 subsequent ER visits occurring within 30 days of the procedure, in the years 1999-2015, to Medicaid-eligible women over 13 years of age with at least one pregnancy outcome, in the 17 states which provided public funding for abortion.

Results: ER visits are at greater risk to occur following a chemical rather than a surgical abortion: all ER visits (OR 1.22, CL 1.19-1.24); miscoded spontaneous (OR 1.88, CL 1.81-1.96); and abortion-related (OR 1.53, CL 1.49-1.58). ER visit rates per 1000 abortions grew faster for chemical abortions, and by 2015, chemical versus surgical rates were 354.8 versus 357.9 for all ER visits; 31.5 versus 8.6 for miscoded spontaneous abortion visits; and 51.7 versus 22.0 for abortion-related visits. Abortion-related visits as a percent of total visits are twice as high for chemical abortions, reaching 14.6% by 2015. Miscoded spontaneous abortion visits as a percent of total visits are nearly 4 times as high for chemical abortions, reaching 8.9% of total visits and 60.9% of abortion-related visits by 2015.

Conclusion: The incidence and per-abortion rate of ER visits following any induced abortion are growing, but chemical abortion is consistently and progressively associated with more postabortion ER visit morbidity than surgical abortion. There is also a distinct trend of a growing number of women miscoded as receiving treatment for spontaneous abortion in the ER following a chemical abortion.

The Victoria Covid19 outbreak is well explained by the data represented in Figure 1. To August 1, 10,931 have tested positive for a coronavirus after more than 1,633,900 tests were performed. 116 people have died from coronavirus in Victoria. The number of infected, tests performed, their ratio, and the number of fatalities as communicated daily by ¹ are proposed vs. the number of days since May 31st.

Background: Atrial fibrillation (AF) in the elderly population is projected to increase over the next several decades. Catheter ablation shows promise as a treatment option and is becoming increasingly available. We examined 90-day hospital readmission for AF patients undergoing catheter ablation and utilized machine learning methods to explore the risk factors associated with these readmission trends.

Methods: Data from the 2013 Nationwide Readmissions Database on AF cases were used to predict 90-day readmissions for AF with catheter ablation. Multiple machine learning methods such as k-Nearest Neighbors, Decision Tree, and Support Vector Machine were employed to determine variable importance and build risk prediction models. Accuracy, precision, sensitivity, specificity, and area under the curve were compared for each model.

Results: The 90-day hospital readmission rate was 17.6%; the average age of the patients was 64.9 years; 62.9% of patients were male. Important variables in predicting 90-day hospital readmissions in patients with AF undergoing catheter ablation included the age of the patient, number of diagnoses on the patient's record, and the total number of discharges from a hospital. The k-Nearest Neighbor had the best performance with a prediction accuracy of 85%. This was closely followed by Decision Tree, but Support Vector Machine was less ideal.

Conclusions: Machine learning methods can produce accurate models in predicting hospital readmissions for patients with AF. The likelihood of readmission to the hospital increases as the patient age, total number of hospital discharges, and total number of patient diagnoses increase. Findings from this study can inform quality improvement in healthcare and in achieving patient-centered care.

The Covid-19 experience provides a natural experiment in personal and social ethics. Difficult decisions are routinely made to optimize lives and livelihoods. This commentary provides background and insight into the ethical and economic foundations underpinning dilemmas of this historic pandemic.

Objective: We surveyed patients who visited multiple outpatient specialty practices to understand what summary content was most helpful with the goal of optimizing meaningful outpatient clinical visit summary content.

Materials and methods: We constructed a survey instrument to measure delivery, use, and contents of clinical visit summaries. We surveyed patients who visited with at least 2 different outpatient medical specialties to understand preferences.

Results: Most patients in our sample valued the summary information they received, and retained it as healthcare documentation (84%) and/or quick reference in supporting self-care (70%). Patients most commonly reported that information on results of completed tests (91%) and treatment plan instructions (89%) were very helpful. Additionally, patients expressed the importance of online access to clinical visit summary information.

Discussion: Most patients used the clinical visit summary as healthcare documentation, and valued online availability of their summary information. Patients most often reported that information on results of recently completed tests and specific instructions on treatment plan were very helpful. Patients who sought further information after their visit most often looked to a provider and/or online.

Conclusions: Patients valued clinical visit summary accessibility and as a reference tool to summarize care and provide next steps. Optimal clinical visit summaries might collate and integrate assessments and recommendations from multiple specialties into coherent care plans for patients.

The purpose of this article is to perform a scientific analysis of the definitions associated with healthcare informatics and healthcare data analytics. Additionally, the authors attempt to redefine the scientific pursuit of healthcare informatics and healthcare data analytics. This commentary can assist the thinking of informaticians and data analysts working in healthcare management and practice. The authors also provide a brief insight on the possible future direction of informatics and analytics associated with healthcare.