Ying P Tabak
Biographic Data
| ID | 5535397 |
|---|---|
| NAME | Ying P Tabak |
| GIVEN NAMES | Ying P |
| FAMILY NAME | Tabak |
| SIGNATURE | TABAK Y P |
| AFFILIATIONS | Cardinal Health (United States) |
| ORCID | 0000-0002-5248-716X |
| VERIFIED | No |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2005 |
| LATEST PUBLICATION YEAR | 2017 |
| H-INDEX | 0 |
Predicting Readmission at Early Hospitalization Using Electronic Clinical Data
BACKGROUND: Identifying patients at high risk for readmission early during hospitalization may aid efforts in reducing readmissions. We sought to develop an early readmission risk predictive model using automated clinical data available at hospital admission. METHODS: We developed an early readmission risk model using a derivation cohort and validated the model with a validation cohort. We used a published Acute Laboratory Risk of Mortality Score…
Using Enriched Observational Data to Develop and Validate Age-specific Mortality Risk Adjustment Models for Hospitalized Pediatric Patients
BACKGROUND: Growth and development in early childhood are associated with rapid physiological changes. We sought to develop and validate age-specific mortality risk adjustment models for hospitalized pediatric patients using objective physiological variables on admission in addition to administrative variables. METHODS: Age-specific laboratory and vital sign variables were crafted for neonates (up to 30 d old), infants/toddlers (1-23 mo), and chi…
Development and Validation of a Mortality Risk-Adjustment Model for Patients Hospitalized for Exacerbations of Chronic Obstructive Pulmonary Disease
BACKGROUND: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a leading cause of hospitalization and death. We sought to develop and validate a mortality risk-adjustment model to enhance hospital performance measurement and to support comparative effectiveness research. METHODS: Using a derivation cohort of 69,299 AECOPD admissions in 2005-2006 across 172 hospitals, we developed a logistic regression model with age, sex, lab…
An Automated Model to Identify Heart Failure Patients at Risk for 30-Day Readmission or Death Using Electronic Medical Record Data
BACKGROUND: A real-time electronic predictive model that identifies hospitalized heart failure (HF) patients at high risk for readmission or death may be valuable to clinicians and hospitals who care for these patients. METHODS: An automated predictive model for 30-day readmission and death was derived and validated from clinical and nonclinical risk factors present on admission in 1372 HF hospitalizations to a major urban hospital between Januar…
Using Automated Clinical Data for Risk Adjustment
BACKGROUND: Clinically plausible risk-adjustment methods are needed to implement pay-for-performance protocols. Because billing data lacks clinical precision, may be gamed, and chart abstraction is costly, we sought to develop predictive models for mortality that maximally used automated laboratory data and intentionally minimized the use of administrative data (Laboratory Models). We also evaluated the additional value of vital signs and altered…
Should Do-Not-Resuscitate Status be Included as a Mortality Risk Adjustor
BACKGROUND: The practice of ordering "Do-Not-Resuscitate" (DNR) varies across hospitals. No research has explored how the DNR variation would affect cross-institutional performance reporting when DNR status is used as a risk adjustor. OBJECTIVE: We sought to assess the impact of DNR variation on performance reporting. RESEARCH DESIGN: We used retrospective clinical data abstracted from chart review for our analysis. SUBJECTS: We studied a total o…
No prominent works on this page.
Should Do-Not-Resuscitate Status be Included as a Mortality Risk Adjustor
BACKGROUND: The practice of ordering "Do-Not-Resuscitate" (DNR) varies across hospitals. No research has explored how the DNR variation would affect cross-institutional performance reporting when DNR status is used as a risk adjustor. OBJECTIVE: We sought to assess the impact of DNR variation on performance reporting. RESEARCH DESIGN: We used retrospective clinical data abstracted from chart review for our analysis. SUBJECTS: We studied a total o…
Using Automated Clinical Data for Risk Adjustment
BACKGROUND: Clinically plausible risk-adjustment methods are needed to implement pay-for-performance protocols. Because billing data lacks clinical precision, may be gamed, and chart abstraction is costly, we sought to develop predictive models for mortality that maximally used automated laboratory data and intentionally minimized the use of administrative data (Laboratory Models). We also evaluated the additional value of vital signs and altered…
An Automated Model to Identify Heart Failure Patients at Risk for 30-Day Readmission or Death Using Electronic Medical Record Data
BACKGROUND: A real-time electronic predictive model that identifies hospitalized heart failure (HF) patients at high risk for readmission or death may be valuable to clinicians and hospitals who care for these patients. METHODS: An automated predictive model for 30-day readmission and death was derived and validated from clinical and nonclinical risk factors present on admission in 1372 HF hospitalizations to a major urban hospital between Januar…
Using Enriched Observational Data to Develop and Validate Age-specific Mortality Risk Adjustment Models for Hospitalized Pediatric Patients
BACKGROUND: Growth and development in early childhood are associated with rapid physiological changes. We sought to develop and validate age-specific mortality risk adjustment models for hospitalized pediatric patients using objective physiological variables on admission in addition to administrative variables. METHODS: Age-specific laboratory and vital sign variables were crafted for neonates (up to 30 d old), infants/toddlers (1-23 mo), and chi…
Development and Validation of a Mortality Risk-Adjustment Model for Patients Hospitalized for Exacerbations of Chronic Obstructive Pulmonary Disease
BACKGROUND: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a leading cause of hospitalization and death. We sought to develop and validate a mortality risk-adjustment model to enhance hospital performance measurement and to support comparative effectiveness research. METHODS: Using a derivation cohort of 69,299 AECOPD admissions in 2005-2006 across 172 hospitals, we developed a logistic regression model with age, sex, lab…
Predicting Readmission at Early Hospitalization Using Electronic Clinical Data
BACKGROUND: Identifying patients at high risk for readmission early during hospitalization may aid efforts in reducing readmissions. We sought to develop an early readmission risk predictive model using automated clinical data available at hospital admission. METHODS: We developed an early readmission risk model using a derivation cohort and validated the model with a validation cohort. We used a published Acute Laboratory Risk of Mortality Score…
Internal Medicine (6 works) · Internal Medicine (6 works) · Medicine (6 works) · Emergency Medicine (5 works) · Emergency Medicine (5 works) · Sepsis Diagnosis and Treatment (5 works) · Statistics (5 works) · Intensive care medicine (3 works) · Statistic (3 works) · Acute Myocardial Infarction Research (2 works)