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

IntroductionProblem Statement: Multi-drug resistant tuberculosis (MDR-TB) is a major problem in many developing countries, and treatment requires complex and expensive drug regimens. Optimal drug regimens improve outcomes and reduce costs. Ensuring that drugs are ordered correctly at the lowest price, and avoiding stock-outs, requires excellent record keeping and data analysis. Objectives: To develop and evaluate a web-based electronic medical record system (EMR) to assist in drug prescribing and drug supply management. Setting: A national community-based treatment program for MDR-TB in Peru. Over 100 public health centers, mainly in Lima, participated, starting in February, 1999 with 75 patients. Study Population: 1590 patients who received treatment for MDR-TB from February 1, 1999 until November 2003. 370 patient records in two districts were studied for the drug data entry study.

The PIH-EMR A secure (SSL) web based electronic medical record for

MDR-TB using a relational database 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

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 monthsdrug requirements from integration of all medication regimens

Prediction of drug requirements (morbidity)One months medication for MDR-TB

Predicted versus actual drug usage from drug regimens in PIH-EMR

1. We compared the predicted usage from drug regimens to actual usage in the warehouse Warehouse usage requires to be averaged over at

least 4 months due to variability

2. Usage was also estimated from a 1 day snapshot of drug regimens compared to 3 month integration of regimen data (1)

Predicted use is affected by enrollment rate, time in treatment and changes in preferred medications

Regimen estimate vs. usage for 2002-2003

101%

98%

96%

100%

123%

108%

98%

106%

101%

102%

101%

93%

96%

127%

101%

86%

106%

0% 20% 40% 60% 80% 100% 120% 140%

Mean

Cicloserine

Ciprofloxacin

Ethionamide

PASER 4gm

Capreomicin

Amikacin

Kanamicin

Amox/ Clav

Ofloxacin

Clofazimine

Rifabutin

Claritromicin

Levofloxacin

Moxifloxacin

PAS sodium 60g

B-6

1592 patients, 24 months data, over 6 Million doses included

Note discrepancies for 2 types of PAS cancel

*

*

*

3 month estimates from one day snap-shot of regimens (1 Jan 02 – 31 Mar 02)

0%

20%

40%

60%

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100%

120%

140%

MDR-TB Patient enrollments Sep. 1996 - Mar. 2004 (per 30 days)

0

10

20

30

40

50

60

70

80

90

Time patients remain in treatment (Aug-03)

0

0.1

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0.7

0.8

0.9

1

0 200 400 600 800 1000 1200 1400

Days

Pro

po

rtio

n i

n t

he

rap

y

Changes in total quinolone usage 2002 – 2003Overall quinolone use is stable but substitutions complicate assessment

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1/1/

2002

2/1/

2002

3/1/

2002

4/1/

2002

5/1/

2002

6/1/

2002

7/1/

2002

8/1/

2002

9/1/

2002

10/1

/200

2

11/1

/200

2

12/1

/200

2

1/1/

2003

2/1/

2003

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2003

4/1/

2003

5/1/

2003

6/1/

2003

7/1/

2003

8/1/

2003

9/1/

2003

10/1

/200

3

11/1

/200

3

12/1

/200

3

Moxif loxacin

Oflaxacin

Levofloxacin

Ciprofloxacin

Collection of accurate 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

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

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 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)

Drug data accuracy: results:

Percentage of medication in errors in EMR per patient. Most errors were delays in updating regimens.

Date/Site Callao Lima Este active control

December 02 17.4%* 8.6%**

April 03 3.1%* 6.9%**

*P= 0.0075 **P= 0.66,

Wilcoxon signed-rank test

Conclusions: The web based EMR can permit order entry in a

developing country and improve the quality of drug regimen data.

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