A GPT-based EHR modeling system for unsupervised novel disease detection.

Objective: To develop an Artificial Intelligence (AI)-based anomaly detection model as a complement of an "astute physician" in detecting novel disease cases in a hospital and preventing emerging outbreaks.

Methods: Data included hospitalized patients (n = 120,714) at a safety-net hospital in Massachusetts. A novel Generative Pre-trained Transformer (GPT)-based clinical anomaly detection system was designed and further trained using Empirical Risk Minimization (ERM), which can model a hospitalized patient's Electronic Health Records (EHR) and detect atypical patients.

A Novel Machine Learning Algorithm for Creating Risk-Adjusted Payment Formulas.

Importance: Models predicting health care spending and other outcomes from administrative records are widely used to manage and pay for health care, despite well-documented deficiencies. New methods are needed that can incorporate more than 70 000 diagnoses without creating undesirable coding incentives.

Objective: To develop a machine learning (ML) algorithm, building on Diagnostic Item (DXI) categories and Diagnostic Cost Group (DCG) methods, that automates development of clinically credible and transparent predictive models for policymakers and clinicians.

Metabolic Disease and The Risk of Post-COVID Conditions: A Retrospective Cohort Study.

Objective: To examine the influence of having a baseline metabolic disorder (diabetes, hypertension, and/or obesity) on the risk of developing new clinical sequelae potentially related to SARS-CoV-2 in a large sample of commercially insured adults in the US.

Design Setting And Participants: Deidentified data were collected from the IBM/Watson MarketScan Commercial Claims and Encounters (CCAE) Databases and Medicare Supplemental and Coordination of Benefits (MDCR) Databases from 2019 to 2021. A total of 839,344 adults aged 18 and above with continuous enrollment in the health plan were included in the analyses.

Target trial emulation for comparative effectiveness research with observational data: Promise and challenges for studying medications for opioid use disorder.

Medications for opioid use disorder (MOUD) increase retention in care and decrease mortality during active treatment; however, information about the comparative effectiveness of different forms of MOUD is sparse. Observational comparative effectiveness studies are subject to many types of bias; a robust framework to minimize bias would improve the quality of comparative effectiveness evidence. This paper discusses the use of target trial emulation as a framework to conduct comparative effectiveness studies of MOUD with administrative data.

Food Pantry Referral and Utilization in a Pediatric Primary Care Clinic.

Introduction: This study aimed to characterize progression from screening for food insecurity risk to on-site food pantry referral to food pantry utilization in pediatric primary care.

Methods: This retrospective study included 14,280 patients aged 0-21 years with ≥1 pediatric primary care visit from March 2018 to February 2020. Analyses were conducted in 2020-2022 using multivariable regression to examine patient-level demographic, clinical, and socioeconomic characteristics and systems-related factors associated with progression from screening positive for food insecurity risk to food pantry referral to completing ≥1 food pantry visit.

Value-Based Purchasing Design And Effect: A Systematic Review And Analysis.

During the past two decades in the United States, all major payer types-commercial, Medicare, Medicaid, and multipayer coalitions-have introduced value-based purchasing (VBP) contracts to reward providers for improving health care quality while reducing spending. This systematic review qualitatively characterized the financial and nonfinancial features of VBP programs and examined how such features combine to create a level of program intensity that relates to desired quality and spending outcomes. Higher-intensity VBP programs are more frequently associated with desired quality processes, utilization measures, and spending reductions than lower-intensity programs.

Development and Assessment of a New Framework for Disease Surveillance, Prediction, and Risk Adjustment: The Diagnostic Items Classification System.

Importance: Current disease risk-adjustment formulas in the US rely on diagnostic classification frameworks that predate the .

Objective: To develop an -based classification framework for predicting diverse health care payment, quality, and performance outcomes.

Design Setting And Participants: Physician teams mapped all diagnoses into 3 types of diagnostic items (DXIs): main effect DXIs that specify diseases; modifiers, such as laterality, timing, and acuity; and scaled variables, such as body mass index, gestational age, and birth weight.

Risk Factors for Admission Within a Hospital-Based COVID-19 Home Monitoring Program.

Background: Despite increasing vaccination rates, coronavirus disease 2019 (COVID-19) continues to overwhelm heath systems worldwide. Few studies follow outpatients diagnosed with COVID-19 to understand risks for subsequent admissions. We sought to identify hospital admission risk factors in individuals with COVID-19 to guide outpatient follow-up and prioritization for novel therapeutics.

Pediatric surgical site infections in 287 hospitals in the United States, 2015-2018.

Among 287 US hospitals reporting data between 2015 and 2018, annual pediatric surgical site infection (SSI) rates ranged from 0% for gallbladder to 10.4% for colon surgeries. Colon, spinal fusion, and small-bowel SSI rates did not decrease with greater surgical volumes in contrast to appendix and ventricular-shunt SSI rates.

Development and validation of predictive models for COVID-19 outcomes in a safety-net hospital population.

Objective: To develop predictive models of coronavirus disease 2019 (COVID-19) outcomes, elucidate the influence of socioeconomic factors, and assess algorithmic racial fairness using a racially diverse patient population with high social needs.

Materials And Methods: Data included 7,102 patients with positive (RT-PCR) severe acute respiratory syndrome coronavirus 2 test at a safety-net system in Massachusetts. Linear and nonlinear classification methods were applied.

Assessment of Federal Value-Based Incentive Programs and In-Hospital Clostridioides difficile Infection Rates.

Importance: Health care facility-onset Clostridioides difficile infection (HO-CDI) rates reported to the US Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN) became a target quality metric for 2 Centers for Medicare & Medicaid Services (CMS) value-based incentive programs (VBIPs) in October 2016. The association of VBIPs with HO-CDI rates is unknown.

Objective: To examine the association between VBIP implementation and HO-CDI rates.

Projected Long-Term Impact of the Coronavirus Disease 2019 (COVID-19) Pandemic on Hepatitis C Outcomes in the United States: A Modeling Study.

Background: The coronavirus disease 2019 (COVID-19) pandemic disrupted access to and uptake of hepatitis C virus (HCV) care services in the United States. It is unknown how substantially the pandemic will impact long-term HCV-related outcomes.

Methods: We used a microsimulation to estimate the 10-year impact of COVID-19 disruptions in healthcare delivery on HCV outcomes including identified infections, linkage to care, treatment initiation and completion, cirrhosis, and liver-related death.

Time to SARS-CoV-2 PCR Clearance in Immunocompromising Conditions: Is Test-Based Removal From Isolation Necessary in Severely Immunocompromised Individuals?

To determine the association between immunosuppression and time to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) polymerase chain reaction (PCR) clearance, we studied 3758 adults retested following initial SARS-CoV-2 infection. Cox proportional hazards models demonstrated delayed PCR clearance with older age, multiple comorbidities, and solid organ transplant but not by degree of immunocompromise. These findings challenge current retesting practices.

Trends in Well-Child Visits With Out-of-Pocket Costs in the US Before and After the Affordable Care Act.

This cross-sectional study uses national claims data to assess trends in well-child care visits with out-of-pocket costs before and after passage of the Affordable Care Act.

Principles of Care for Young Adults With Co-Occurring Psychiatric and Substance Use Disorders.

Over 50% of young adults (defined as individuals aged 18-25 years) with substance use disorders (SUDs) have at least 1 co-occurring psychiatric disorder, and the presence of co-occurring disorders worsens SUD outcomes. Treatment of both co-occurring psychiatric disorders and SUDs in young adults is imperative for optimal treatment, yet many barriers exist to achieving this goal. We present a series of evidence-informed principles of care for young adults with co-occurring psychiatric disorders derived by a workgroup of experts convened by Boston Medical Center's Grayken Center for Addiction.

Health Care-Associated Infections Among Critically Ill Children in the US, 2013-2018.

Importance: Central catheter-associated bloodstream infections (CLABSIs) and catheter-associated urinary tract infections (CAUTIs) increase morbidity, mortality, and health care costs in pediatric patients.

Objective: To examine changes over time in CLABSI and CAUTI rates between 2013 and 2018 in neonatal intensive care units (NICUs) and pediatric intensive care units (PICUs) using prospective surveillance data from community hospitals, children's hospitals, and pediatric units within general hospitals.

Design, Setting, And Participants: This time series study included 176 US hospitals reporting pediatric health care-associated infection surveillance data to the National Healthcare Safety Network from January 1, 2013, to June 30, 2018.