Moritz Esdar1, Jens Hüsers2, Jan-Patrick Weiß3, Jens Rauch4, Ursula Hübner5. 1. Health Informatics Research Group, University of Applied Sciences Osnabrück, Faculty of Business Management and Social Sciences, Caprivistr. 30A, D-49076 Osnabrück, Germany. Electronic address: m.esdar@hs-osnabrueck.de. 2. Health Informatics Research Group, University of Applied Sciences Osnabrück, Faculty of Business Management and Social Sciences, Caprivistr. 30A, D-49076 Osnabrück, Germany. Electronic address: j.huesers@hs-osnabrueck.de. 3. Health Informatics Research Group, University of Applied Sciences Osnabrück, Faculty of Business Management and Social Sciences, Caprivistr. 30A, D-49076 Osnabrück, Germany. Electronic address: j.p.weiss@hs-osnabrueck.de. 4. Health Informatics Research Group, University of Applied Sciences Osnabrück, Faculty of Business Management and Social Sciences, Caprivistr. 30A, D-49076 Osnabrück, Germany. Electronic address: j.rauch@hs-osnabrueck.de. 5. Health Informatics Research Group, University of Applied Sciences Osnabrück, Faculty of Business Management and Social Sciences, Caprivistr. 30A, D-49076 Osnabrück, Germany. Electronic address: u.huebner@hs-osnabrueck.de.
Abstract
BACKGROUND: While aiming for the same goal of building a national eHealth Infrastructure, Germany and the United States pursued different strategic approaches - particularly regarding the role of promoting the adoption and usage of hospital Electronic Health Records (EHR). OBJECTIVE: To measure and model the diffusion dynamics of EHRs in German hospital care and to contrast the results with the developments in the US. MATERIALS AND METHODS: All acute care hospitals that were members of the German statutory health system were surveyed during the period 2007-2017 for EHR adoption. Bass models were computed based on the German data and the corresponding data of the American Hospital Association (AHA) from non-federal hospitals in order to model and explain the diffusion of innovation. RESULTS: While the diffusion dynamics observed in the US resembled the typical s-shaped curve with high imitation effects (q = 0.583) but with a relatively low innovation effect (p = 0.025), EHR diffusion in Germany stagnated with adoption rates of approx. 50% (imitation effect q = -0.544) despite a higher innovation effect (p = 0.303). DISCUSSION: These findings correlate with different governmental strategies in the US and Germany of financially supporting EHR adoption. Imitation only seems to work if there are financial incentives, e.g. those of the HITECH Act in the US. They are lacking in Germany, where the government left health IT adoption strategies solely to the free market and the consensus among all of the stakeholders. CONCLUSION: Bass diffusion models proved to be useful for distinguishing the diffusion dynamics in German and US non-federal hospitals. When applying the Bass model, the imitation parameter needs a broader interpretation beyond the network effects, including driving forces such as incentives and regulations, as was demonstrated by this study.
BACKGROUND: While aiming for the same goal of building a national eHealth Infrastructure, Germany and the United States pursued different strategic approaches - particularly regarding the role of promoting the adoption and usage of hospital Electronic Health Records (EHR). OBJECTIVE: To measure and model the diffusion dynamics of EHRs in German hospital care and to contrast the results with the developments in the US. MATERIALS AND METHODS: All acute care hospitals that were members of the German statutory health system were surveyed during the period 2007-2017 for EHR adoption. Bass models were computed based on the German data and the corresponding data of the American Hospital Association (AHA) from non-federal hospitals in order to model and explain the diffusion of innovation. RESULTS: While the diffusion dynamics observed in the US resembled the typical s-shaped curve with high imitation effects (q = 0.583) but with a relatively low innovation effect (p = 0.025), EHR diffusion in Germany stagnated with adoption rates of approx. 50% (imitation effect q = -0.544) despite a higher innovation effect (p = 0.303). DISCUSSION: These findings correlate with different governmental strategies in the US and Germany of financially supporting EHR adoption. Imitation only seems to work if there are financial incentives, e.g. those of the HITECH Act in the US. They are lacking in Germany, where the government left health IT adoption strategies solely to the free market and the consensus among all of the stakeholders. CONCLUSION: Bass diffusion models proved to be useful for distinguishing the diffusion dynamics in German and US non-federal hospitals. When applying the Bass model, the imitation parameter needs a broader interpretation beyond the network effects, including driving forces such as incentives and regulations, as was demonstrated by this study.
Authors: Elske Ammenwerth; Georg Duftschmid; Zaid Al-Hamdan; Hala Bawadi; Ngai T Cheung; Kyung-Hee Cho; Guillermo Goldfarb; Kemal H Gülkesen; Nissim Harel; Michio Kimura; Önder Kırca; Hiroshi Kondoh; Sabine Koch; Hadas Lewy; Dara Mize; Sari Palojoki; Hyeoun-Ae Park; Christopher Pearce; Fernan G B de Quirós; Kaija Saranto; Christoph Seidel; Vivian Vimarlund; Martin C Were; Johanna Westbrook; Chung P Wong; Reinhold Haux; Christoph U Lehmann Journal: Methods Inf Med Date: 2020-11-18 Impact factor: 2.176