Human Factors Predicting Failure and Success in Hospital Information System Implementations in sub-Saharan Africa

Frank Verbeke

Department of Medical InformaticsVrije Universiteit Brussel

School of Public HealthUniversity of Rwanda

Cotonou 2015

Introduction

Hospital Information Systems (HIS) implementation has gained momentum in sub-Saharan Africa

Experiences with OpenClinic GA from 19 hospitals in Rwanda, Burundi, DR Congo, Congo-Brazzaville, Gabon and Mali

Many succeeded, some failed Failure & success factors?

Cotonou 2015

Materials & methods

19 HIS implementation projects analyzed Pre-implementation assets identification

Classification

Problem documentation in implementation logbooks Classification

Forwarded solutions and work-arounds & results obtained 297 health facility staff interviewed

Iterate through all projects Score global project success (0 – 5 based on 6 questions) Score impact on the project of each identified asset & problem

(0 – 5)

Cotonou 2015

10 functional categories of risk factors and facilitators

1. Infrastructure

2. Patient administration

3. Financial information management

4. Reason for encounter and diagnostic coding

5. Medical record management

6. Lab information management

7. Medical imaging

8. Reporting and statistics

9. Systems integration

10. Project management issues and human factors

Cotonou 2015

Human, cultural and environmental factors

14 potential failure & 15 success factors

Irrational Hard to solve Evaluation of predictive value?

Cotonou 2015

Predictive value of human factors

For each project Average of 14 asset item scores = project opportunity level Average of 15 problem item scores = project risk level

Correlations between asset item scores & global project success score (prediction

of implementation success) between problem item scores & global project success score

(prediction of implementation failure) With p-value for the F-test on the regression with a confidence level

of 95% r < 0.6: low predictive value 0.6 <= r < 0.75: moderate predictive value r >= 0.75 high predictive value

Cotonou 2015

Results: implementation failure factors

High predictive value Resignation to poor health

service quality Psychological factors Organizational culture and silo

mentality Unrealistic implementation time

frames Poor technical assistance Insufficient training Discontinued follow-up

Perceived complexity Low measured user

satisfaction

Moderate predictive value Unclear goals Absence of a project

champion Resistance to change

and power shifts

Cotonou 2015

Results: implementation success factors

High predictive value Clear communication Realistic timing Business process

reengineering ability Stakeholder consenus Holistic approach Quick wins implementation Adequate technical assistance High user satisfaction

Moderate predictive value Broad staff enrollment Progressive change

management Sufficient and continued

training Perceived user

friendliness

Cotonou 2015

Risk level & opportunity level – project success regressions

Cotonou 2015

Conclusions

High predictive value of most of the forwarded failure & success factors

Surprisingly: low or no statistical evidence for predictive value of the following factors: Insufficiently ICT skilled staff before implementation Incentives Availability of clinician & ICT-staff intermediaries Consideration of prevailing practice