1. Inthira Kanchanaphibool Faculty of Pharmacy, Silpakorn University, Thailand 1

2. Common e-Datasets Needed for Medicines Use Research Demographic Age, gender Diagnosis and procedure* ICD-10, ICD-10-TM (Thai modification), ICD-9-CM Prescribing drugs* Generic name / dosage form / strength / amount / price per unit / dosage regimen Laboratory results Financial information Expense of prescription drugs Health insurance scheme 2

3. Strengths National level Incentives of health insurance payments to hospitals OPD cases: Fee-for-service scheme – direct paid to hospitals by items of dispensing data submitted Capitation scheme – not required dispensing data IPD cases: All schemes Paid by DRGs (diagnosis-related groups) data submitted Individual hospital level Completeness of dispensing database Improvement in hospital information system 3

4. Challenges National level Lack of national standards of prescription datasets among hospitals Individual hospital level Large hospitals No standard drug classification system Unable to identify the exact trade names One code for all brands Two codes for one original and one any local made brands Small hospitals.....more problems….e.g. Separate databases (diagnosis and prescription) Completeness of transaction data (no backup system) 4

5. Examples SIM201E = simvastatin 20 mg tablet (one code for all brands) Jan 1999 – Mar 2000 code = Zocor R 20 mg (original brand) cost per unit = 42.00 Baht/tab Apr 2000 – Dec 2003 code = Zimmex R 20 mg and others (local made brand) cost per unit = 2.47 Baht/tab 5 Average no. of visits by days (to check for completeness of the transaction data)

6. Experience in a Thai Hospital Context Tertiary care hospital Advanced e-hospital information system Dispensing drug data Generate drug code and assign the pharmacological classification by a responsible pharmacist at that time (lack of standard classification system) 2 drug codes for 1 generic name (one for original brand and one for any local made brand) Diagnosis data Standard diagnosis code: ICD-10, ICD-9-CM Recorded by trained clerical coder, not physicians (lack of completeness and correctness) 6

7. Approaches for Medicines Use Research Step 1: Consider the applicable diseases or drugs to research Particular drug items or drug groups regardless of diseases e.g. COX-II inhibitors, thaiazolidinediones, etc. Specific drugs used for only few specific diseases e.g. diabetes, glaucoma, some types of cancer Step 2:Set up the most valid criteria to identify the eligible patients using selected drug and diagnosis codes Step 3: Clean the transaction data Discard the incorrect and missing data Discard the ineligible records Step 4: (If needed) Sampling some qualified records to verify with the hard copy of medical records to Assess the accuracy of the data Learn about the possible errors in the data Step 5: Analyze the treated data to answer the research questions 7

8. A Case Study of Research on 5-year Medicines Use in Diabetes Step 1: Almost all of antidiabetic drugs – specific for diabetes Step 2: Set up the criteria to recruit diabetes patients List patients who received all oral antidiabetic drugs (21 codes) and insulins (11 codes) No need to use diagnosis code (ICD-10) Step 3: Clean the transaction data Discard the patients with irregular visits throughout the 5-year period 8 Step 4:No need to sampling records to verify with the hard copy of medical records Step 5: Analyze the treated data Equality in access to diabetic care Quality of diabetic care Compared to CPGs (Antiplatelet therapy for prevention of CVD) Tracer for patient safety (Recived Insulin and glucose injection) Cost of care

9. 9 % of pateints (>40 yr) with antipletlet therapy to prevent CVD

10. Conclusion Accuracy of the eligible data depends on context of the individual hospitals Need to understand the nature (limitations) of the data kept in the prescription database Even in the absence of a national drug classification system, prescription data is still useful to assess medicines utilization in some certain diseases 10