1 / 17

Development & Evaluation of MDR-TB Software in Peru

This software aids MDR-TB treatment in Peru by managing prescriptions & drug supply. Development, evaluation results & impact analysis are discussed.

carlislel
Download Presentation

Development & Evaluation of MDR-TB Software in Peru

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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

  3. 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

  4. Evaluation studies • Software was developed in-housein 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

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

  6. Predicted versus actual drug usage from drug regimens in PIH-EMR • 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

  7. Comparison of EMR estimate and actual usage from warehouse 2002-2003 Drugs EMR/Usage Cicloserina 98.5% Ciprofloxacina 96.1% Ethionamida 99.8% Ac.Paraminosalicilico 123.4% Capreomicina 108.4% Amikacina 98.3% Kanamicina 106.1% Amox/Ac. Clav 500mg 101.0% Ofloxacina 102.1% Clofazimina 100 mg 101.4% Rifabutina 93.3% Claritromicina 95.8% Levofloxacina 126.7% Moxifloxacina 101.2% Protionamida 65.0% PAS sodium 60g 85.8% B-6 105.8% Mean 100.5% Total of 6.5M doseswith value of $4.5M

  8. Patient enrollments Sep. 1996 - Mar. 2004 (per 30 days) Days in treatment for all patients 1996 - 2003

  9. Forecast of 2003 medication usage Product Doses Predicted/actual Amikacin (1 g) 3858 103% 103% Amox/Clav (500 mg) 546317 103% 103% Capreomycin (1 g) 59503 99% 99% Ciprofloxin (500 mg) 550687 88% Clarithromycin (500 mg) 38577 96% 96% Clofazamine (50 mg) 902180 99% 99% Cycloserine (250 mg) 703511 102% 102% Ethambutol (400 mg) 244045 107% 107% Ethionamide (250 mg) 572377 99% 99% Isoniazid (100 mg) 91347 112% 112% Kanamycin (1 g) 115366 104% 104% Levofloxacin (500 mg) 728 1025% Moxifloxacin (400 mg) 30752 100% 100% Ofloxacin (200 mg) 249656 190% PAS (4 g) 45801 161% PAS-MacLeod (3.3 g) 680911 91% Pyrazinamide (500 mg) 335038 100% 100% Pyridoxine (300 mg) 143783 101% 101% Rifabutin (150 mg) 18197 108% 108% Rifampicin (300 mg) 23656 95% 95% Streptomycin (1 g) 29133 112% 112% Ciprofloxin (500 mg) equiv. 718579 101% PAS-MacLeod (3.3 g) equiv. 768304 96% Mean difference 102% 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

  10. Medication order entry

  11. (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: • a custom prescription form for the doctors • a web-based drug order entry system for nurses

  12. Nurse order entry forms

  13. 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. 2002 • 90 charts in Callao – intervention site • 77 charts Lima Este- control site • Data entry in Callao commenced 10th Feb. 2003 • Survey was repeated early April 2003 • 95 charts Callao (80 same as initial review) • 102 charts Este (71 same as initial review)

  14. Results of order entry system Percentage of medication in errors in EMR per patient. Date/Site Callao Lima Esteactive control December 02 17.4%* 8.6%** April 03 3.1%* 6.9%** *P= 0.0075 **P= 0.66, Wilcoxon signed-rank test Most errors were delays in updating regimens

  15. 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.

  16. Forecast of 2003 medication usage Product Predicted/actual Grouped 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 difference 102% 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

More Related