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Enhanced clinical pharmacy service targeting tools: risk‐predictive algorithms
Author(s) -
El Hajji Feras W. D.,
Scullin Claire,
Scott Michael G.,
McElnay James C.
Publication year - 2015
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/jep.12276
Subject(s) - medicine , pharmacy , staffing , algorithm , prioritization , emergency medicine , clinical pharmacy , medical emergency , intensive care medicine , family medicine , computer science , nursing , management science , economics
Rationale, aims and objectives This study aimed to determine the value of using a mix of clinical pharmacy data and routine hospital admission spell data in the development of predictive algorithms. Exploration of risk factors in hospitalized patients, together with the targeting strategies devised, will enable the prioritization of clinical pharmacy services to optimize patient outcomes. Methods Predictive algorithms were developed using a number of detailed steps using a 75% sample of integrated medicines management ( IMM ) patients, and validated using the remaining 25%. IMM patients receive targeted clinical pharmacy input throughout their hospital stay. The algorithms were applied to the validation sample, and predicted risk probability was generated for each patient from the coefficients. Risk threshold for the algorithms were determined by identifying the cut‐off points of risk scores at which the algorithm would have the highest discriminative performance. Clinical pharmacy staffing levels were obtained from the pharmacy department staffing database. Results Numbers of previous emergency admissions and admission medicines together with age‐adjusted co‐morbidity and diuretic receipt formed a 12‐month post‐discharge and/or readmission risk algorithm. Age‐adjusted co‐morbidity proved to be the best index to predict mortality. Increased numbers of clinical pharmacy staff at ward level was correlated with a reduction in risk‐adjusted mortality index ( RAMI ). Conclusions Algorithms created were valid in predicting risk of in‐hospital and post‐discharge mortality and risk of hospital readmission 3, 6 and 12 months post‐discharge. The provision of ward‐based clinical pharmacy services is a key component to reducing RAMI and enabling the full benefits of pharmacy input to patient care to be realized.