Randomized controlled trial demonstrates novel tools to assess patient outcomes of Indigenous cultural safety training.

BACKGROUND: Health care routinely fails Indigenous peoples and anti-Indigenous racism is common in clinical encounters. Clinical training programs aimed to enhance Indigenous cultural safety (ICS) rely on learner reported impact assessment even though clinician self-assessment is poorly correlated with observational or patient outcome reporting. We aimed to compare the clinical impacts of intensive and brief ICS training to control, and to assess the feasibility of ICS training evaluation tools, including unannounced Indigenous standardized patient (UISP) visits. METHOD: Using a prospective parallel group three-arm randomized controlled trial design and masked standardized patients, we compared the clinical impacts of the intensive interactive, professionally facilitated, 8- to10-h Sanyas ICS training; a brief 1-h anti-bias training adapted to address anti-Indigenous bias; and control continuing medical education time-attention matched to the intensive training. Participants included 58 non-Indigenous staff physicians, resident physicians and nurse practitioners from family practice clinics, and one emergency department across four teaching hospitals in Toronto, Canada. Main outcome measures were the quality of care provided during UISP visits including adjusted odds that clinician would be recommended by the UISP to a friend or family member; mean item scores on patient experience of care measure; and clinical practice guideline adherence for NSAID renewal and pain assessment. RESULTS: Clinicians in the intensive or brief ICS groups had higher adjusted odds of being highly recommended to friends and family by standardized patients (OR 6.88, 95% CI 1.17 to 40.45 and OR 7.78, 95% CI 1.05 to 58.03, respectively). Adjusted mean item patient experience scores were 46% (95% CI 12% to 80%) and 40% (95% CI 2% to 78%) higher for clinicians enrolled in the intensive and brief training programs, respectively, compared to control. Small sample size precluded detection of training impacts on clinical practice guideline adherence; 100% of UISP visits were undetected by participating clinicians. CONCLUSIONS: Patient-oriented evaluation design and tools including UISPs were demonstrated as feasible and effective. Results show potential impact of cultural safety training on patient recommendation of clinician and improved patient experience. A larger trial to further ascertain impact on clinical practice is needed. TRIAL REGISTRATION: Clinicaltrials.org NCT05890144. Retrospectively registered on June 5, 2023.

Nursing turnover in a large, rural health system.

PURPOSE: Nursing turnover is a leading cause of inefficiency in health care delivery. Few studies have examined turnover among nurses who work in rural areas. METHODS: We accessed human resources data that tracked hiring and terminations from a large health system operating in South Dakota, North Dakota, and Minnesota between January 2016 and December 2017. Our study sample included 7,634 registered nurses, 1,765 of whom worked in a rural community. Within the health system, there were 27 affiliated hospitals, 17 of which were designated critical access hospitals. We estimated nursing turnover rates overall and stratified turnover rates by available demographic and occupational characteristics, including whether the nurse worked in a community with an affiliated acute care hospital or critical access hospital. FINDINGS: Overall, 19% of nurses left their position between January 2016 and December 2017. Turnover rates were associated with state, nurse gender and age, and occupational tenure, but were similar in urban and rural areas. Of note, turnover rates were significantly higher in communities without an affiliated acute care hospital or critical access hospital. CONCLUSION: Between 2016 and 2017, nearly 1 in 5 nurses working in this health system left their position. Turnover rates differed based on nurse demographics and selected occupational characteristics, including tenure. We also found higher turnover rates among nurses who worked in communities without an affiliated hospital, which points to a potential but unexplored benefit of hospitals in rural areas.

Forecasting HIV-1 Genetic Cluster Growth in Illinois,United States.

BACKGROUND: HIV intervention activities directed toward both those most likely to transmit and their HIV-negative partners have the potential to substantially disrupt HIV transmission. Using HIV sequence data to construct molecular transmission clusters can reveal individuals whose viruses are connected. The utility of various cluster prioritization schemes measuring cluster growth have been demonstrated using surveillance data in New York City and across the United States, by the Centers for Disease Control and Prevention (CDC). METHODS: We examined clustering and cluster growth prioritization schemes using Illinois HIV sequence data that include cases from Chicago, a large urban center with high HIV prevalence, to compare their ability to predict future cluster growth. RESULTS: We found that past cluster growth was a far better predictor of future cluster growth than cluster membership alone but found no substantive difference between the schemes used by CDC and the relative cluster growth scheme previously used in New York City (NYC). Focusing on individuals selected simultaneously by both the CDC and the NYC schemes did not provide additional improvements. CONCLUSION: Growth-based prioritization schemes can easily be automated in HIV surveillance tools and can be used by health departments to identify and respond to clusters where HIV transmission may be actively occurring.

Predicting unplanned medical visits among patients with diabetes: translation from machine learning to clinical implementation.

BACKGROUND: Diabetes is a medical and economic burden in the United States. In this study, a machine learning predictive model was developed to predict unplanned medical visits among patients with diabetes, and findings were used to design a clinical intervention in the sponsoring healthcare organization. This study presents a case study of how predictive analytics can inform clinical actions, and describes practical factors that must be incorporated in order to translate research into clinical practice. METHODS: Data were drawn from electronic medical records (EMRs) from a large healthcare organization in the Northern Plains region of the US, from adult (≥ 18 years old) patients with type 1 or type 2 diabetes who received care at least once during the 3-year period. A variety of machine-learning classification models were run using standard EMR variables as predictors (age, body mass index (BMI), systolic blood pressure (BP), diastolic BP, low-density lipoprotein, high-density lipoprotein (HDL), glycohemoglobin (A1C), smoking status, number of diagnoses and number of prescriptions). The best-performing model after cross-validation testing was analyzed to identify strongest predictors. RESULTS: The best-performing model was a linear-basis support vector machine, which achieved a balanced accuracy (average of sensitivity and specificity) of 65.7%. This model outperformed a conventional logistic regression by 0.4 percentage points. A sensitivity analysis identified BP and HDL as the strongest predictors, such that disrupting these variables with random noise decreased the model's overall balanced accuracy by 1.3 and 1.4 percentage points, respectively. These recommendations, along with stakeholder engagement, behavioral economics strategies, and implementation science principles helped to inform the design of a clinical intervention targeting behavioral changes. CONCLUSION: Our machine-learning predictive model more accurately predicted unplanned medical visits among patients with diabetes, relative to conventional models. Post-hoc analysis of the model was used for hypothesis generation, namely that HDL and BP are the strongest contributors to unplanned medical visits among patients with diabetes. These findings were translated into a clinical intervention now being piloted at the sponsoring healthcare organization. In this way, this predictive model can be used in moving from prediction to implementation and improved diabetes care management in clinical settings.

Neural Tube Defects in Pregnancies Among Women With Diagnosed HIV Infection - 15 Jurisdictions, 2013-2017.

In May 2018, a study of birth defects in infants born to women with diagnosed human immunodeficiency virus (HIV) infection in Botswana reported an eightfold increased risk for neural tube defects (NTDs) among births with periconceptional exposure to antiretroviral therapy (ART) that included the integrase inhibitor dolutegravir (DTG) compared with other ART regimens (1). The World Health Organization* (WHO) and the U.S. Department of Health and Human Services† (HHS) promptly issued interim guidance limiting the initiation of DTG during early pregnancy and in women of childbearing age with HIV who desire pregnancy or are sexually active and not using effective contraception. On the basis of additional data, WHO now recommends DTG as a preferred treatment option for all populations, including women of childbearing age and pregnant women. Similarly, the U.S. recommendations currently state that DTG is a preferred antiretroviral drug throughout pregnancy (with provider-patient counseling) and as an alternative antiretroviral drug in women who are trying to conceive.§ Since 1981 and 1994, CDC has supported separate surveillance programs for HIV/acquired immunodeficiency syndrome (AIDS) (2) and birth defects (3) in state health departments. These two surveillance programs can inform public health programs and policy, linkage to care, and research activities. Because birth defects surveillance programs do not collect HIV status, and HIV surveillance programs do not routinely collect data on occurrence of birth defects, the related data have not been used by CDC to characterize birth defects in births to women with HIV. Data from these two programs were linked to estimate overall prevalence of NTDs and prevalence of NTDs in HIV-exposed pregnancies during 2013-2017 for 15 participating jurisdictions. Prevalence of NTDs in pregnancies among women with diagnosed HIV infection was 7.0 per 10,000 live births, similar to that among the general population in these 15 jurisdictions, and the U.S. estimate based on data from 24 states. Successful linking of data from birth defects and HIV/AIDS surveillance programs for pregnancies among women with diagnosed HIV infection suggests that similar data linkages might be used to characterize possible associations between maternal diseases or maternal use of medications, such as integrase strand transfer inhibitors used to manage HIV, and pregnancy outcomes. Although no difference in NTD prevalence in HIV-exposed pregnancies was found, data on the use of integrase strand transfer inhibitors in pregnancy are needed to understand the safety and risks of these drugs during pregnancy.