Patricia Kipnis

Biographic Data

ID5536846
NAMEPatricia Kipnis
GIVEN NAMESPatricia
FAMILY NAMEKipnis
SIGNATUREKIPNIS P
AFFILIATIONSKaiser Permanente
ORCID0000-0003-4572-0178
VERIFIEDYes
TOTAL WORKS9
TOTAL CITATIONS0
AUTHOR COUNT9
EDITOR COUNT0
FIRST PUBLICATION YEAR2008
LATEST PUBLICATION YEAR2023
H-INDEX0

  • Prediction of In-hospital Mortality Among Intensive Care Unit Patients Using Modified Daily Laboratory-based Acute Physiology Score, Version 2

    Rachel Kohn, Gary E Weissman et al.ARTICLEMedical Care2023References: 31

    BACKGROUND: Mortality prediction for intensive care unit (ICU) patients frequently relies on single ICU admission acuity measures without accounting for subsequent clinical changes. OBJECTIVE: Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Score, version 2 (LAPS2) to predict in-hospital mortality among ICU patients. RESEARCH DESIGN: Retrospective cohort study. PATIENTS: ICU patien…

  • Evaluation of Vaccination Strategies to Compare Efficient and Equitable Vaccine Allocation by Race and Ethnicity Across Time

    Open AccessPatricia Kipnis, Lauren Soltesz et al.ARTICLEJAMA Health Forum2021

    In this simulation modeling study of adults from a large integrated health care delivery system, risk-based strategies were associated with the largest estimated reductions in COVID-19 hospitalizations, deaths, and household transmissions compared with the CDC proxy and age-based strategies, with a higher proportion of Hispanic and Black patients were estimated to be vaccinated early in the process compared with the CDC strategy

  • The Impact of Pharmacy-specific Predictors on the Performance of 30-Day Readmission Risk Prediction Models

    Samuel Kabue, J Greene et al.ARTICLEMedical Care2019References: 26

    RESEARCH OBJECTIVE: Pharmacists are an expensive and limited resource in the hospital and outpatient setting. A pharmacist can spend up to 25% of their day planning. Time spent planning is time not spent delivering an intervention. A readmission risk adjustment model has potential to be used as a universal outcome-based prioritization tool to help pharmacists plan their interventions more efficiently. Pharmacy-specific predictors have not been us…

  • Nonelective Rehospitalizations and Postdischarge Mortality

    Open AccessGabriel J Escobar, Arona I Ragins et al.ARTICLEMedical Care2015References: 35

    BACKGROUND: Hospital discharge planning has been hampered by the lack of predictive models. OBJECTIVE: To develop predictive models for nonelective rehospitalization and postdischarge mortality suitable for use in commercially available electronic medical records (EMRs). DESIGN: Retrospective cohort study using split validation. SETTING: Integrated health care delivery system serving 3.9 million members. PARTICIPANTS: A total of 360,036 surviving…

  • Accuracy of Hospital Standardized Mortality Rates

    Patricia Kipnis, Vincent Liu et al.ARTICLEMedical Care2014References: 8

    BACKGROUND: Risk-adjusted mortality rates are commonly used in quality report cards to compare hospital performance. The risk adjustment depends on models that are assessed for goodness-of-fit using various discrimination and calibration measures. However, the relationship between model fit and the accuracy of hospital comparisons is not well characterized. OBJECTIVES: To evaluate the impact of imperfect model calibration (miscalibration) on the …

  • Risk-adjusting Hospital Mortality Using a Comprehensive Electronic Record in an Integrated Health Care Delivery System

    Gabriel J Escobar, Marla N Gardner et al.ARTICLEMedical Care2013References: 32

    OBJECTIVE: Using a comprehensive inpatient electronic medical record, we sought to develop a risk-adjustment methodology applicable to all hospitalized patients. Further, we assessed the impact of specific data elements on model discrimination, explanatory power, calibration, integrated discrimination improvement, net reclassification improvement, performance across different hospital units, and hospital rankings. DESIGN: Retrospective cohort stu…

  • Effect of Choice of Estimation Method on Inter-Hospital Mortality Rate Comparisons

    Patricia Kipnis, Gabriel J Escobar et al.ARTICLEMedical Care2010References: 13

    OBJECTIVE: To evaluate and compare the use of 6 different methods for calculating expected mortality rates and standardized mortality ratios (SMRs) when performing interhospital mortality rate comparisons. DESIGN: Retrospective cohort study using actual and simulated hospitalization data to evaluate the use of (1) fixed-effects, (2) Generalized Linear Mixed Model, and (3) Bayesian (Markov Chain Monte Carlo-based) random-effects models on both agg…

  • Length of Stay Predictions

    Vincent Liu, Patricia Kipnis et al.ARTICLEMedical Care2010References: 27

    BACKGROUND: Length of stay (LOS) is a common measure of hospital resource utilization. Most methods for risk-adjusting LOS are limited by the use of only administrative data. Recent studies suggest that adding automated clinical data to these models improves performance. OBJECTIVES: To evaluate the utility of adding "point of admission" automated laboratory and comorbidity measures-the Laboratory Acute Physiology Score (LAPS) and Comorbidity Poin…

  • Risk-Adjusting Hospital Inpatient Mortality Using Automated Inpatient, Outpatient, and Laboratory Databases

    Gabriel J Escobar, J Greene et al.ARTICLEMedical Care2008References: 31

    OBJECTIVES: To develop a risk-adjustment methodology that maximizes the use of automated physiology and diagnosis data from the time period preceding hospitalization. DESIGN: : Retrospective cohort study using split-validation and logistic regression. SETTING: Seventeen hospitals in a large integrated health care delivery system. SUBJECTS: Patients (n = 259,699) hospitalized between January 2002 and June 2005. MAIN OUTCOME MEASURES: Inpatient and…