Artif Intell Med - Predicting the impact of hospital health information technology adoption on patient satisfaction.

Tópicos

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Resumo

JECTIVES: To develop and explore the predictability of patient perceptions of satisfaction through the hospital adoption of health information technology (HIT), leading to a better understanding of the benefits of increased HIT investment.DATA AND METHODS: The solution proposed is based on comparing the predictive capability of artificial neural networks (ANNs) with the adaptive neuro-fuzzy inference system (ANFIS). The latter 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. Secondary data from 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 acute hospitals were used as outputs.RESULTS: Eight 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 R(2)(Adjusted). For all four patient satisfaction indicators, the performance of ANFIS was found to be more effective (R(Adjusted)(2)=0.99) when compared with the results of ANN modeling in predicting the impact of HIT adoption on patient satisfaction (R(Adjusted)(2)=0.86-0.88).CONCLUSIONS: The impact of HIT adoption on patient satisfaction was obtained for different HIT adoption scenarios using ANFIS simulations. The results through simulation scenarios revealed that full implementation of HIT in hospitals can lead to significant improvement in patient satisfaction. We conclude that the proposed ANFIS modeling technique can be used as a decision support mechanism to assist government and policy makers in predicting patient satisfaction resulting from the implementation of HIT in hospitals.

Resumo Limpo

jectiv develop explor predict patient percept satisfact hospit adopt health inform technolog hit lead better understand benefit increas hit investmentdata method solut propos base compar predict capabl artifici neural network ann adapt neurofuzzi infer system anfi latter integr artifici neural network fuzzi logic can handl certain complex problem includ fuzzi human percept nonnorm nonlinear data secondari data two survey combin develop model hospit hit adopt capabl use indic canadian provinc ontario use input patient satisfact indic healthcar servic acut hospit use outputsresult eight differ type model train test four patient satisfact dimens accuraci predict model evalu statist perform measur includ root mean squar error rmse adjust coeffici determin radjust four patient satisfact indic perform anfi found effect radjust compar result ann model predict impact hit adopt patient satisfact radjustedconclus impact hit adopt patient satisfact obtain differ hit adopt scenario use anfi simul result simul scenario reveal full implement hit hospit can lead signific improv patient satisfact conclud propos anfi model techniqu can use decis support mechan assist govern polici maker predict patient satisfact result implement hit hospit

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