Predicting the impact of hospital health information technology adoption on patient satisfaction

Abstract

<p><em>Objectives:</em> To develop and explore the predictability of patient perceptions of satisfaction through the hospital adoption of health information technology (HIT) in order to help understand the benefits of increased HIT investment.</p> <p><em>Data and Methods:</em> The solution proposed is based on an adaptive neuro-fuzzy inference system (ANFIS), which integrates artificial neural networks and fuzzy logic and can handle certain complex problems that include fuzziness in human perception, and non-normal and non­ linear data. Two surveys were combined to develop the model. Hospital HIT adoption capability and use indicators in the Canadian province of Ontario were used as inputs, while patient satisfaction indicators of healthcare services in hospitals were used as outputs.</p> <p><em>Results:</em> Seven different types of models were trained and tested for each of four patient satisfaction dimensions. The accuracy of each predictive model was evaluated through statistical performance measures, including root mean square error (RMSE), and adjusted coefficient of determination<em> R² Adjusted</em>. The impact of HIT adoption on patient satisfaction was obtained for different HIT adoption scenarios using ANFIS simulations.</p> <p><em>Conclusions:</em> The results revealed that ANFIS simulations provide good accuracy and reliability for predicting the impact of health information technology adoption on patient satisfaction in hospitals. These simulations can therefore be helpful as decision support mechanisms to assist government and policy makers in understanding and predicting the effects of successful implementation and use of HIT in hospitals.</p>