A decision support system for equipment allocation in a telemedicine referral network

Date
2014-05
Journal Title
Journal ISSN
Volume Title
Publisher
Southern African Institute for Industrial Engineering
Abstract
Telemedicine applications have had much success in strengthening health systems worldwide. Unfortunately, many systems are implemented without decisions based on proper needs assessments. In South Africa, this ‘technology push’ approach has led to a large amount of equipment standing dormant. It is proposed that the potential of telemedicine be measured prior to implementation, thus ‘pulling’ the technology towards a clinical need. A decision support system is developed that uses health informatics and computational intelligence to determine the need for telemedicine and to allocate equipment in a network of facilities to achieve the best cost benefit. The system facilitates the collection and storage of electronic health record (EHR) data in a data warehouse. A linear programming model is used with a genetic algorithm. The system was developed and tested for the South African public health sector, using data from 27 hospitals in the Western Cape Province. Results have shown that if telemedicine workstations with specific peripheral equipment, as determined by the algorithm, were implemented in the given period, an estimated R8.7 million in referral costs could have been saved for the 27 hospitals. Thus the case study provided evidence for the benefits of implementation in the chosen network of hospitals. This new application of health informatics could provide telemedicine management with a useful tool for making implementation decisions based on evidence. Future work would include the development of similar systems for other markets.
’n Verskeidenheid telegeneeskunde toepassings het reeds groot sukses behaal in die bevordering van gesondheidsdienste wêreldwyd. Ongelukkig word tegnologie dikwels geimplementeer sonder om besluitneming te baseer op behoorlike behoefte bepalings. In Suid-Afrika, het hierdie ‘tegnologie stoot’ benadering gelei tot ’n groot hoeveelhede ongebruikte toerusting. Daar word voorgestel dat die potensiaal van telemedisyne gemeet moet word, voor implementering, om sodoende tegnologie te ‘trek’ na kliniese behoefte. ’n Besluitneming ondersteuning stelsel is ontwikkel wat gebruik maak van gesondheidsorg informatika en rekenkundige intelligensie, om die behoefte vir tele-geneeskunde te bepaal en daarvolgens toerusting toe te ken aan ’n netwerk van gesondheidsorg fasiliteite, om die beste kostevoordeel te bereik. Die stelsel fasiliteer die versamel en berg van elektroniese mediese rekord data in ’n data stoor. ’n Lineêre programmering model word gebruik met ’n genetiese algoritme opgelos. Die stelsel is ontwikkel en getoets vir die Suid-Afrikaanse openbare gesondheidsektor, met behulp van data van 27 hospitale in die Wes-Kaap Provinsie. Resultate toon dat indien telemedisyne werkstasies met spesifieke aanvullende toerusting, soos bepaal deur die algoritme, beskikbaar was in die gegewe tydperk, ’n beraamde R8.7 miljoen gespaar kon word met betrekking tot pasiënt verwysingkoste. Die gevallestudie toon dus van die voordele van implementering in die gekose netwerk van hospitale. Hierdie nuwe toepassing van gesondheidsorg informatika kan dien as ’n nuttige hulpmiddel vir tele-geneeskunde besluitnemers in tele-geneeskunde om besluite gebaseer op konkrete bewyse. Toekomstige werk sal die ontwikkeling van soortgelyke stelsels vir ander markte insluit.
Description
CITATION: Treurnicht, M. J. & Van Dyk, L. 2014. A decision support system for equipment allocation in a telemedicine referral network. South African Journal of Industrial Engineering, 25(1):29-38, doi:10.7166/25-1-641.
The original publication is available at http://sajie.journals.ac.za
Keywords
Telemedicine, Health informatics, Computational intelligence, Electronic health record (EHR) data
Citation
Treurnicht, M. J. & Van Dyk, L. 2014. A decision support system for equipment allocation in a telemedicine referral network. South African Journal of Industrial Engineering, 25(1):29-38, doi:10.7166/25-1-641.