Thick Data Analytics (TDA): An Iterative and Inductive Framework for Algorithmic Improvement.

A gap remains between developing risk prediction models and deploying models to support real-world decision making, especially in high-stakes situations. Human-experts' reasoning abilities remain critical in identifying potential improvements and ensuring safety. We propose a (TDA) framework for eliciting and combining expert-human insight into the evaluation of models.

Autism Diagnosis Among US Children and Adults, 2011-2022.

Importance: An improved understanding of autism spectrum disorder (ASD) prevalence over time and across the lifespan can inform health care service delivery for the growing population of autistic children and adults.

Objective: To describe trends in the prevalence of ASD diagnoses using electronic records data from a large network of health systems in the US.

Design, Setting, And Participants: This cross-sectional study examined annual diagnosis rates in health records of patients in US health systems from January 1, 2011, to December 31, 2022.

Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural Language Processing and Machine Learning.

Introduction: Rapid identification of individuals developing a psychotic spectrum disorder (PSD) is crucial because untreated psychosis is associated with poor outcomes and decreased treatment response. Lack of recognition of early psychotic symptoms often delays diagnosis, further worsening these outcomes.

Methods: The proposed study is a cross-sectional, retrospective analysis of electronic health record data including clinician documentation and patient-clinician secure messages for patients aged 15-29 years with ≥ 1 primary care encounter between 2017 and 2019 within 2 Kaiser Permanente regions.

Effectiveness of an Artificial Intelligence-Enabled Intervention for Detecting Clinical Deterioration.

Importance: Inpatient clinical deterioration is associated with substantial morbidity and mortality but may be easily missed by clinicians. Early warning scores have been developed to alert clinicians to patients at high risk of clinical deterioration, but there is limited evidence for their effectiveness.

Objective: To evaluate the effectiveness of an artificial intelligence deterioration model-enabled intervention to reduce the risk of escalations in care among hospitalized patients using a study design that facilitates stronger causal inference.

Human Immunodeficiency Virus Status, Tenofovir Exposure, and the Risk of Poor Coronavirus Disease 19 Outcomes: Real-World Analysis From 6 United States Cohorts Before Vaccine Rollout.

Background: People with human immunodeficiency virus (HIV) (PWH) may be at increased risk for severe coronavirus disease 2019 (COVID-19) outcomes. We examined HIV status and COVID-19 severity, and whether tenofovir, used by PWH for HIV treatment and people without HIV (PWoH) for HIV prevention, was associated with protection.

Methods: Within 6 cohorts of PWH and PWoH in the United States, we compared the 90-day risk of any hospitalization, COVID-19 hospitalization, and mechanical ventilation or death by HIV status and by prior exposure to tenofovir, among those with severe acute respiratory syndrome coronavirus 2 infection between 1 March and 30 November 2020.

Rib Fracture Frailty Index: A risk stratification tool for geriatric patients with multiple rib fractures.

Background: Rib fractures are consequential injuries for geriatric patients (age, ≥65 years). Although age and injury patterns drive many rib fracture management decisions, the impact of frailty-which baseline conditions affect rib fracture-specific outcomes-remains unclear for geriatric patients. We aimed to develop and validate the Rib Fracture Frailty (RFF) Index, a practical risk stratification tool specific for geriatric patients with rib fractures.

Developing machine learning models to personalize care levels among emergency room patients for hospital admission.

Objective: To develop prediction models for intensive care unit (ICU) vs non-ICU level-of-care need within 24 hours of inpatient admission for emergency department (ED) patients using electronic health record data.

Materials And Methods: Using records of 41 654 ED visits to a tertiary academic center from 2015 to 2019, we tested 4 algorithms-feed-forward neural networks, regularized regression, random forests, and gradient-boosted trees-to predict ICU vs non-ICU level-of-care within 24 hours and at the 24th hour following admission. Simple-feature models included patient demographics, Emergency Severity Index (ESI), and vital sign summary.

Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study.

Objectives: To determine the associations between a care coordination intervention (the Transitions Program) targeted to patients after hospital discharge and 30 day readmission and mortality in a large, integrated healthcare system.

Design: Observational study.

Setting: 21 hospitals operated by Kaiser Permanente Northern California.

Predicting preventable hospital readmissions with causal machine learning.

Objective: To assess both the feasibility and potential impact of predicting preventable hospital readmissions using causal machine learning applied to data from the implementation of a readmissions prevention intervention (the Transitions Program).

Data Sources: Electronic health records maintained by Kaiser Permanente Northern California (KPNC).

Study Design: Retrospective causal forest analysis of postdischarge outcomes among KPNC inpatients.

Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data.

Importance: Accurate prediction of outcomes among patients in intensive care units (ICUs) is important for clinical research and monitoring care quality. Most existing prediction models do not take full advantage of the electronic health record, using only the single worst value of laboratory tests and vital signs and largely ignoring information present in free-text notes. Whether capturing more of the available data and applying machine learning and natural language processing (NLP) can improve and automate the prediction of outcomes among patients in the ICU remains unknown.

Geographic and Patient Characteristics Associated With Election of Prophylactic Mastectomy in Young Breast Cancer Patients With Early Disease.

Objectives: To examine the association between geography and utilization rates of contralateral prophylactic mastectomy (CPM) relative to patient-level factors in patients with early stage breast cancer.

Materials And Methods: Using the Truven Health MarketScan Commercial Database, a descriptive analysis of geographic variation in genetic testing and CPM rates of 38,108 women ages 18 to 64 years treated with surgery for invasive unilateral breast cancer between 2010 and 2012 was conducted. Multivariate hierarchical analysis was used to examine the relationship between CPM likelihood and patient characteristics, with metropolitan statistical area (MSA) serving as a random effect.

Geographic Variation in Postoperative Imaging for Low-Risk Breast Cancer.

The objective of this study was to examine the presence and magnitude of US geographic variation in use rates of both recommended and high-cost imaging in young patients with early-stage breast cancer during the 18 month period after surgical treatment of their primary tumor. Using the Truven Health MarketScan Commercial Database, a descriptive analysis was conducted of geographic variation in annual rates of dedicated breast imaging and high-cost body imaging of 36,045 women aged 18 to 64 years treated with surgery for invasive unilateral breast cancer between 2010 and 2012. Multivariate hierarchical analysis examined the relationship between likelihood of imaging and patient characteristics, with metropolitan statistical area (MSA) serving as a random effect.

Accurate and interpretable intensive care risk adjustment for fused clinical data with generalized additive models.

Risk adjustment models for intensive care outcomes have yet to realize the full potential of data unlocked by the increasing adoption of EHRs. In particular, they fail to fully leverage the information present in longitudinal, structured clinical data - including laboratory test results and vital signs - nor can they infer patient state from unstructured clinical narratives without lengthy manual abstraction. A fully electronic ICU risk model fusing these two types of data sources may yield improved accuracy and more personalized risk estimates, and in obviating manual abstraction, could also be used for real-time decision-making.

Variation in Quality of Urgent Health Care Provided During Commercial Virtual Visits.

Importance: Commercial virtual visits are an increasingly popular model of health care for the management of common acute illnesses. In commercial virtual visits, patients access a website to be connected synchronously-via videoconference, telephone, or webchat-to a physician with whom they have no prior relationship. To date, whether the care delivered through those websites is similar or quality varies among the sites has not been assessed.

Receipt of Intravenous Iron and Clinical Outcomes among Hemodialysis Patients Hospitalized for Infection.

Background And Objectives: Anemia guidelines for CKD recommend withholding intravenous iron in the setting of active infection, although no data specifically support this recommendation. This study aimed to examine the association between intravenous iron and clinical outcomes among hemodialysis patients hospitalized for infection.

Design, Setting, Participants, & Measurements: This was a retrospective observational cohort study using data from the US Renal Data System of 22,820 adult Medicare beneficiaries on in-center hemodialysis who had received intravenous iron in the 14 days preceding their first hospitalization for bacterial infection in 2010.

Efficient and sparse feature selection for biomedical text classification via the elastic net: Application to ICU risk stratification from nursing notes.

Background And Significance: Sparsity is often a desirable property of statistical models, and various feature selection methods exist so as to yield sparser and interpretable models. However, their application to biomedical text classification, particularly to mortality risk stratification among intensive care unit (ICU) patients, has not been thoroughly studied.

Objective: To develop and characterize sparse classifiers based on the free text of nursing notes in order to predict ICU mortality risk and to discover text features most strongly associated with mortality.

Thirty-day hospital readmissions in systemic lupus erythematosus: predictors and hospital- and state-level variation.

Objective: Systemic lupus erythematosus (SLE) has one of the highest hospital readmission rates among chronic conditions. This study was undertaken to identify patient-level, hospital-level, and geographic predictors of 30-day hospital readmissions associated with SLE.

Methods: Using hospital discharge databases from 5 geographically dispersed states, we studied all-cause readmission of SLE patients between 2008 and 2009.

N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit.

Background: Existing risk adjustment models for intensive care unit (ICU) outcomes rely on manual abstraction of patient-level predictors from medical charts. Developing an automated method for abstracting these data from free text might reduce cost and data collection times.

Objective: To develop a support vector machine (SVM) classifier capable of identifying a range of procedures and diagnoses in ICU clinical notes for use in risk adjustment.

Practical approaches to dose selection for first-in-human clinical trials with novel biopharmaceuticals.

Recent advances in our understanding of disease biology, biomarkers, new therapeutic targets, and innovative modalities have each fueled a dramatic expansion in the development of novel human therapeutics. Many are biotechnology-derived biologics possessing high selectivity and affinity for their intended target; as such they often pose challenges in the development path to approval. One challenge is the selection of the first-in-human (FIH) dose.