Using pharmacy information in a decision support system to improve efficient delivery of primary health care

Published: November 5, 2009
Category: Bibliography > Reports
Authors: Heurgren M, Johansson A, Kinder-Siemens K
Countries: Sweden
Language: null
Types: Care Management
Settings: Government

A study focusing on the Swedish national drug register. BMC Health Serv Res 0:A6.

Ensolution, Stockholm, Sweden

INTRODUCTION: The aim of the project was to apply the John Hopkins  ACG case-mix system, Rx-PM model, to the Swedish  National Drug Register (period 2006-2008). Its intention  was to analyze and compare results between different  county councils, and analyze if drug use in the population  can be employed to approximate the need for care, and as  a tool to adjust the capitation payment system in the  county councils. This paper focuses on the comparisons  between the different county councils. Practical examples  and usage of data are presented.

METHODS: The ACG-Rx system, based on the unique Rx-MG categories,  is an Rx-based risk adjustment tool (NDC, ATC, Read  code) that can be used as a predictive model and to understand  patterns of medication use. Pharmaceutical utilization  is a proxy for underlying morbidity. The John  Hopkins ACG case mix system, Rx-PM model, is a grouping  logic that uses drug utilization to measure the severity  of the underlying morbidity, the therapeutic goal of medication use, and the duration of treatment to present pharmacy  data in a new perspective that had not been available previously. The tool can be used for disease/case management, profiling  (population and provider) and forecasting pharmacy  and total costs for large groups. The analysis included  Sweden’s entire population (9 million persons) and their  usage of drugs (6.2 million patients annually). This  resulted in 29 million combinations of patients and used  ATC-codes for each year. Results have been grouped for  the periods 2006, 2007 and 2008. The analysis represents  an annual cost of 24-25 Billion SEK (approx. 25 Mill  Euro). The grouping went well in practice without any  coding issues.

RESULTS: The analysis model involves five steps: 1) Actual pharmacy  cost and predicted pharmacy cost per county council.  The purpose of this analysis is to determine the cost  level. 2) Actual costs per inhabitant and predicted cost per  inhabitant per county council/municipality. The purpose  of this analysis is to determine the differences in consumption.  3) Proportion of high-risk patients per municipality.  The purpose of this analysis is to determine how  specific outliers influence the results. 4) Standard Morbidity  Rates (SMRs) for major Rx-MGs per county council.  The purpose of this analysis is to determine if specific  groups and practices influence the results. 5) Comparisons  of specific Rx-MGs per county council. The purpose  of this analysis is to provide a detailed comparison on  practices and costs.

CONCLUSION:  The Rx-model works well for Swedish data. The analysis  showed significant differences between county councils as  well as on the municipal level. Measures generated from  the system could, therefore, be used in the Swedish benchmarking  model Open Comparisons. The model also provides functionality for predicting  change in total cost. Comparison between predicted costs  from the Rx-model and the actual costs showed a low variation  (1%). The model provides a large amount of data  for analysis and usage in practice, i.e., specific analysis for  measuring costs for high-risk patients. More analysis with  diagnosis and cost data on the county council level is still  needed to prove if Rx-MG can be a useful tool for resource  allocation in a capitation model. The combined models (Rx-PM + Dx-PM) with diagnoses  and pharmacy data are recommended for use. Pharmacy  data alone has a higher explanatory value than age and  gender, but it is still low in comparison with combined  models.

Capitation,Payment,Sweden,High Risk, Cost Burden Evaluation

Please log in/register to access.

Log in/Register

LinkedIn Facebook Twitter

© The Johns Hopkins University, The Johns Hopkins Hospital, and Johns Hopkins Health System.
All rights reserved. Terms of Use Privacy Statement

Back to top