A DECISION SUPPORT TOOL FOR MEASURING THE OUTCOME OF CLINICAL IT WITH PROCESS-RELATED MATURITY MODELS

Abstract

The use of information technology (IT) in healthcare has a critical effect in enhancing the efficiency, cost-effectiveness, quality, and safety of medical care delivery(Gomes and Romão 2018). Identifying and containing costs of information technology (IT) have been the scope of chief executives in healthcare organizations for many years. The investments in healthcare information systems (HIS) have been driven by two key factors: The ever-increasing burden from chronic disease with costs growing significantly faster and the recognition of the need for greatly improved quality and safety in health delivery(Gomes and Romão 2018). To transparently measure the value for money spent on clinical IT, key performance indicators have been developed and refined. The complexity - especially in the case of heterogeneous IT landscapes - has led to continuous attempts to reach this objective at reasonable efforts. This work is based on papers that address the issue of IT systems maturity in the healthcare sector. Most of the research found are bibliographic works and the lack of empirical works pushed us to set up a simple and general mathematical framework, avoiding high levels of complexity, in order to measure the maturity level of a hospital system composed of several departments. Our approach is built in two steps. First, we set up a model under a deterministic environment, over several periods according to the demand and priority of each department. In this step, we assume that the demand of the departments is known in advance, which makes it easier for the manager to decide whether to satisfy it. The information technology team has a finite number of resources (The IT team's resource is limited). In a second step, we assume that the manager does not have visibility on the demand of each department that can follow a stochastic distribution. The manager will therefore have to find the optimal way to satisfy the maturity level of each department at each period according to the different scenarios. The results show that in both cases we mentioned earlier, the manager manages to find the optimal solution. The model allows customization according to the interests and requirements of a specific hospital or across several hospitals. Another advantage is that a fitted model offers the possibility to carry out a more detailed survey.