A method for analyzing inpatient care variability through physicians' orders

OBJECTIVE Administrators assess care variability through chart review or cost variability to inform care standardization efforts. Chart review is costly and cost variability is imprecise. This study explores the potential of physician orders as an alternative measure of care variability. MATERIALS & METHODS The authors constructed an order variability metric from adult Vanderbilt University Hospital patients treated between 2013 and 2016. The study compared how well a cost variability model predicts variability in the length of stay compared to an order variability model. Both models adjusted for covariates such as severity of illness, comorbidities, and hospital transfers. RESULTS The order variability model significantly minimized the Akaike information criterion (superior outcome) compared to the cost variability model. This result also held when excluding patients who received intensive care. CONCLUSION Order variability can potentially typify care variability better than cost variability. Order variability is a scalable metric, calculable during the course of care.

[1]  R. Horn,et al.  Severity of Illness Within DRGs: Homogeneity Study , 1986, Medical care.

[2]  W. Knaus,et al.  APACHE II: a severity of disease classification system. , 1985 .

[3]  Tania D Strout,et al.  Impact of a computerized order set on adherence to Centers for Disease Control guidelines for the treatment of victims of sexual assault. , 2013, The Journal of emergency medicine.

[4]  J. Welton,et al.  Hospital nursing costs, billing, and reimbursement. , 2006, Nursing economic$.

[5]  M. Cabana,et al.  Why don't physicians follow clinical practice guidelines? A framework for improvement. , 1999, JAMA.

[6]  P. Austin,et al.  Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community , 2010, Canadian Medical Association Journal.

[7]  Amanda H. Salanitro,et al.  Risk prediction models for hospital readmission: a systematic review. , 2011, JAMA.

[8]  Kevin B. Johnson,et al.  The Impact of Peer Management on Test-Ordering Behavior , 2004, Annals of Internal Medicine.

[9]  N. Laport,et al.  Variability of nursing care by APR-DRG and by severity of illness in a sample of nine Belgian hospitals , 2013, BMC Nursing.

[10]  J. Klima,et al.  Patterns of Inpatient Care for Newly Diagnosed Immune Thrombocytopenia in US Children’s Hospitals , 2013, Pediatrics.

[11]  D. Bates,et al.  Effects of computerized physician order entry on prescribing practices. , 2000, Archives of internal medicine.

[12]  L. McMahon,et al.  Variation in Resource Use Within Diagnosis-related Groups: The Effect of Severity of Illness and Physician Practice , 1986, Medical care.

[13]  R. Resar,et al.  Standardization as a mechanism to improve safety in health care. , 2004, Joint Commission journal on quality and safety.

[14]  J. P. Kichak,et al.  Computerized physician order entry: helpful or harmful? , 2003, Journal of the American Medical Informatics Association : JAMIA.

[15]  Kristian B Filion,et al.  The use of the transition cost accounting system in health services research , 2007, Cost effectiveness and resource allocation : C/E.

[16]  Julia Adler-Milstein,et al.  Electronic Health Record Adoption In US Hospitals: Progress Continues, But Challenges Persist. , 2015, Health affairs.

[17]  George Hripcsak,et al.  Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk , 2017, J. Biomed. Informatics.

[18]  C. Steiner,et al.  Comorbidity measures for use with administrative data. , 1998, Medical care.

[19]  Lynne Moore,et al.  Impact of socio-economic status on unplanned readmission following injury: A multicenter cohort study. , 2016, Injury.

[20]  Son Doan,et al.  Application of information technology: MedEx: a medication information extraction system for clinical narratives , 2010, J. Am. Medical Informatics Assoc..

[21]  George Hripcsak,et al.  Research Paper: Knowledge-based Approaches to the Maintenance of a Large Controlled Medical Terminology , 1994, J. Am. Medical Informatics Assoc..

[22]  Anke J. E. de Veer,et al.  Factors influencing the implementation of clinical guidelines for health care professionals: A systematic meta-review , 2008, BMC Medical Informatics Decis. Mak..

[23]  Patricia M Davidson,et al.  What are the factors in risk prediction models for rehospitalisation for adults with chronic heart failure? , 2012, Australian critical care : official journal of the Confederation of Australian Critical Care Nurses.

[24]  Clare Davies,et al.  GIS Usability: Recommendations Based on the User's View , 1994, Int. J. Geogr. Inf. Sci..

[25]  J. Cimino Desiderata for Controlled Medical Vocabularies in the Twenty-First Century , 1998, Methods of Information in Medicine.

[26]  D. Bates,et al.  Relationship between medication errors and adverse drug events , 1995, Journal of General Internal Medicine.

[27]  Kazuyoshi Imaizumi,et al.  Comparison of severity scoring systems A‐DROP and CURB‐65 for community‐acquired pneumonia , 2008, Respirology.

[28]  Jonas Schreyögg,et al.  Cost accounting to determine prices: How well do prices reflect costs in the German DRG-system? , 2006, Health care management science.

[29]  Matthew Wheatley,et al.  Protocol-driven emergency department observation units offer savings, shorter stays, and reduced admissions. , 2013, Health affairs.

[30]  I. Abbass,et al.  Variability in the Initial Costs of Care and One-Year Outcomes of Observation Services , 2015, The western journal of emergency medicine.

[31]  Michael E Chernew,et al.  Changes in Low-Value Services in Year 1 of the Medicare Pioneer Accountable Care Organization Program. , 2015, JAMA internal medicine.

[32]  P. Groenewegen,et al.  Green space, urbanity, and health: how strong is the relation? , 2006, Journal of Epidemiology and Community Health.

[33]  Lucy A Savitz,et al.  How Intermountain trimmed health care costs through robust quality improvement efforts. , 2011, Health affairs.

[34]  Jacques Donzé,et al.  Causes and patterns of readmissions in patients with common comorbidities: retrospective cohort study , 2013, BMJ.

[35]  R. Horn,et al.  Severity of illness within DRGs: impact on prospective payment. , 1985, American journal of public health.

[36]  B J McNeil,et al.  Modified DRGs as Evidence for Variability in Patient Severity , 1988, Medical care.

[37]  Jonathan M Mansbach,et al.  Variability in inpatient management of children hospitalized with bronchiolitis. , 2015, Academic pediatrics.

[38]  Robert B. Fetter,et al.  Variation in resource use within diagnosis-related groups: The severity issue , 1984, Health care financing review.

[39]  Christian O. Jacke,et al.  The adherence paradox: guideline deviations contribute to the increased 5-year survival of breast cancer patients , 2015, BMC Cancer.

[40]  D W Young,et al.  The ratio of costs to charges: how good a basis for estimating costs? , 1995, Inquiry : a journal of medical care organization, provision and financing.