Development and evaluation of software to support prescribing and drug supply management in the treatment of MDR-TB in Peru. Fraser H, Choi S, Jazayeri D, Kempton K, Bayona J Partners In Health & Harvard Medical School, Boston, USA Socios En Salud, Lima, Peru

INTRODUCTION: The PIH-EMR  A secure (SSL) web based electronic medical record using a relational database  Developed to support treatment of MDR-TB  Usable over low-speed Internet connections  Bilingual: English/Spanish  Extensive data analysis tools  Uses:  Clinical care, patient summaries, laboratory data  Monthly reports on patient outcomes  Drug supply management  Ordering and tracking laboratory results  Research studies

Medication data  Drug regimens must be accurately recorded and updated to ensure reliable estimates  Data entry may be from paper forms/charts or by medical staff or nurses  Checks are required to ensure that the data is accurate and complete  Data integrity checks eg. overlapping prescriptions  Cross checks with other records e.g. pharmacy

Evaluation studies Software was developed in-house in close collaboration with the medical and nursing staff in Lima, Peru. Evaluation was performed of two aspects of the system in use: 1) the accuracy of the analysis programs for predicting future drug requirements compared with actual usage. 2) The entry of medication regimens directly into the electronic medical record(EMR) by the nurses assessed by comparing one intervention and one control district

Drug regimen entry form Analysis of 1 months drug requirements from integration of all medication regimens (1) Prediction of drug requirements One months medication for MDR-TB

Predicted versus actual drug usage from drug regimens in PIH-EMR 1.We compared years 2002 and 2003 :  predicted usage from drug regimens (morbidity analysis)  actual usage in the warehouse (consumption analysis)  Results in table 1  Usage was also predicted from a 1 day snapshot of drug regimens on 1/1/2003 and compared to use calculated from actual drug regimen data in 2003  Results shown in table 2  Predicted use is affected by enrollment rate, time in treatment and changes in preferred medications

Comparison of EMR estimate and actual usage from warehouse DrugsEMR/Usage Cicloserina98.5% Ciprofloxacina96.1% Ethionamida99.8% Ac.Paraminosalicilico123.4% Capreomicina108.4% Amikacina98.3% Kanamicina106.1% Amox/Ac. Clav 500mg101.0% Ofloxacina102.1% Clofazimina 100 mg101.4% Rifabutina93.3% Claritromicina95.8% Levofloxacina126.7% Moxifloxacina101.2% Protionamida65.0% PAS sodium 60g 85.8% B % Mean100.5% Total of 6.5M doses with value of $4.5M

Patient enrollments Sep Mar (per 30 days) Days in treatment for all patients

ProductDosesPredicted/actual Amikacin (1 g) %103% Amox/Clav (500 mg) %103% Capreomycin (1 g) % 99% Ciprofloxin (500 mg) % Clarithromycin (500 mg) % 96% Clofazamine (50 mg) % 99% Cycloserine (250 mg) %102% Ethambutol (400 mg) %107% Ethionamide (250 mg) % 99% Isoniazid (100 mg) %112% Kanamycin (1 g) %104% Levofloxacin (500 mg) % Moxifloxacin (400 mg) %100% Ofloxacin (200 mg) % PAS (4 g) % PAS-MacLeod (3.3 g) % Pyrazinamide (500 mg) %100% Pyridoxine (300 mg) %101% Rifabutin (150 mg) %108% Rifampicin (300 mg) % 95% Streptomycin (1 g) %112% Ciprofloxin (500 mg) equiv % PAS-MacLeod (3.3 g) equiv % Mean difference102% Forecast of 2003 medication usage Estimated from: -snapshot of regimens on 1/1/2003, -expected time in treatment from previous 5 years’ data -enrollment rate of previous 60 days Combined fluoroquinolones Combined PAS

Medication order entry

(2) Direct order entry of medications  Nurses manage the medications for patients (once the pulmonologist has decided on the regimen)  Initially we identified problems with data accuracy in drug regimens and inefficient data flow  We developed: 1.a custom prescription form for the doctors 2.a web-based drug order entry system for nurses

Nurse order entry forms

Evaluation of impact of order entry system on drug data accuracy  Quality and timeliness of the drug regimen data in the EMR was surveyed in Nov. /Dec  90 charts in Callao – intervention site  77 charts Lima Este- control site  Data entry in Callao commenced 10 th Feb  Survey was repeated early April 2003  95 charts Callao (80 same as initial review)  102 charts Este (71 same as initial review)

Results of order entry system Date/SiteCallaoLima Este activecontrol December %*8.6%** April %*6.9%** *P= **P= 0.66, Wilcoxon signed-rank test Percentage of medication in errors in EMR per patient. Most errors were delays in updating regimens

Conclusions and Recommendations  Regimen data can be used to predict drug requirements, and hence improve drug procurement.  Comparing predicted and actual drug use allows errors or discrepancies in data to be detected (such as incorrect number of doses from a new form of PAS).  Predictions of future drug use requires knowledge of:  changes in enrollment rate  length of time in treatment  Changes in drug use for clinical or programmatic reasons  The web based EMR can permit order entry systems to be deployed in a developing country and improve the quality of drug regimen data.

ProductPredicted/actualGrouped Amikacin (1 g)103%103% Amox/Clav (500 mg)103%103% Capreomycin (1 g) 99% 99% Ciprofloxin (500 mg) 88% Clarithromycin (500 mg) 96% 96% Clofazamine (50 mg) 99% 99% Cycloserine (250 mg)102%102% Ethambutol (400 mg)107%107% Ethionamide (250 mg) 99% 99% Isoniazid (100 mg)112%112% Kanamycin (1 g)104%104% Levofloxacin (500 mg) 1025% Moxifloxacin (400 mg)100%100% Ofloxacin (200 mg)190% PAS (4 g)161% PAS-MacLeod (3.3 g) 91% Pyrazinamide (500 mg)100%100% Pyridoxine (300 mg)101%101% Rifabutin (150 mg)108%108% Rifampicin (300 mg) 95% 95% Streptomycin (1 g)112%112% Ciprofloxin (500 mg) equiv.101% PAS-MacLeod (3.3 g) equiv. 96% Mean difference102% Forecast of 2003 medication usage Estimated from: -snapshot of regimens on 1/1/2003, -expected time in treatment from previous 5 years’ data -enrollment rate of previous 60 days Combined fluoroquinolones Combined PAS