Strengths and challenges of data for medicines use research: Electronic hospital prescribing data

1. Strengths and challenges of data for medicines use research: Electronic hospital prescribing data InthiraKanchanaphibool Faculty of Pharmacy, Silpakorn University, Thailand

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

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

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)

5. Examples SIM201E = simvastatin 20 mg tablet (one code for all brands) • Jan 1999 – Mar 2000code = ZocorR 20 mg(original brand)cost per unit = 42.00 Baht/tab • Apr 2000 – Dec 2003code = ZimmexR 20mg and others (local made brand) cost per unit = 2.47Baht/tab 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)

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

8. A Case Study of Research on 5-year Medicines Use in Diabetes • 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 • 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

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