Call for Papers

We are entering a new age of management for health organizations and systems. Although healthcare expenditures are rising at a fast, steady pace, healthcare investments do not seem to always translate into better, more equitable health provision (Goh et al., 2016). Inequalities in care access and outcomes across socio-economic strata, patient groups and geographies are still plaguing developed as well as developing countries (World Health Organization, 2017). Demands for more personalized, patient-centered and affordable care are growing. Innovation in healthcare systems and services, driven partly by new technologies, is viewed as a solution to these challenges. Almost every day media report about novel technologies that are expected to produce massive transformations in health care. Examples include (i) wearable devices, medical apps, and telemedicine technologies which are seen as means to improve patients’ self-care and, ultimately, change patients’ unhealthy behavior; (ii) “big ticket” health technologies, such as robotic-assisted technologies or 3D printers, which physicians progressively incorporate in clinical practice; (iii) machine learning and big data on which hospitals more and more rely to support complex decision making and organizational processes.
 
However, technological innovation is both a panacea and a problem in healthcare. The adoption of many technological innovations into mainstream use often brings about challenges for different actors in the field. Adoption is often a complex endeavor for healthcare organizations and systems, which imposes higher costs for practitioners and policymakers, implies learning and unlearning dynamics, generates uncertainty and ambiguity for different stakeholders and may engender unexpected consequences. New technologies also usually result in higher costs for governments and healthcare organizations, which not only have to purchase them, but also create a favorable environment that facilitates their successful implementation into mainstream use by systems and organizations.
 
Moreover, improvements in medical technology often raise costs overall, as new conditions are identified and become treatable or a wider range of patients are eligible for treatment. Technologies also pose challenges for clinicians and other healthcare personnel, who have to learn new practices and acquire new skills (Tjora & Scambler, 2009). Hospital managers are increasingly asked to balance the delicate tension between old and new routines imposed by the adoption of new technologies, to ensure an affordable implementation and to provide users with the necessary skills and motivation to embed the technology in the organizational and wider health system fabric (Boonstra & Broekhuis, 2010).
 
An emerging and as yet poorly understood challenge is the data multiplication effect that new medical technologies bring about. Wearables, medical apps, eHealth applications all produce a massive amount of data. This both opens up opportunities as well as posing challenges (Murdoch & Detsky, 2013). Turning this data into valuable information and useable knowledge is becoming increasingly critical yet complex to achieve. The huge amount of data now available to clinicians – if left unstructured – is often perceive as a burden. From a patient point of view, medical apps, wearables and online medical portals have been viewed as a process of “democratization” of medical information, which is now more available and more comprehensible the patients (de Lusignan et al., 2014; Lu & Rui, 2018). While this can stimulate patient empowerment and self-management, and reduce the large information asymmetry between patients and physicians, it can also increase the risk of wrong diagnoses, as patients often lack knowledge and experience to contextualize medical information. This poses threats to patients’ trust relationship with doctors, with possible detrimental effects on patient outcomes.
 
The limited or negative impact of innovation can partly be explained by organizational idiosyncrasies inherent in healthcare. First, the sector is highly-regulated, where multitude logics influence actors’ beliefs, attitudes and behaviors. Another peculiarity is the strong effect of professional roles. On the one hand these provide identity to clinicians, along with a sense of belonging and affiliation to a larger community. At the same time, they represent a constraint insofar they are frequently not permeable to new technologies, especially those which have an impact across professional boundaries. Finally, we emphasize the multilevel, fragmented nature of decision making (Dattée & Barlow, 2017). Different actors at different level (national, regional, local, organizational, professional) are involved, at least to some extent, in decision making about the adoption and implementation of innovations, creating a decision-making environment that is often highly fragmented, with conflicting institutional logics.
 
While there is a growing body of research on aspects of healthcare innovation, the pace of technological evolution and the unfolding demographic and economic landscape, means that the organizational challenges are also evolving. In particular, there is a need to consider how the latest management thinking is applicable in the healthcare sector, given its unique features.
 
This sub-theme seeks to develop the literature by focusing on questions such as:

• How can technological innovation be properly designed and implemented in healthcare organizations and systems?
• How can we better understand the implementation of innovations across multiple organizational and systemic levels? How can change be diffused and adopted from one level to another?
• How can inter- and intra-organizational networks facilitate or hinder technological innovation and its implementation and adoption?
• How can internal (profession, culture, routines) and external (new technologies, reforms and new policies) contingencies be better combined in the adoption of new medical technology?
• (How) does the adoption of new technologies by organizations and systems contribute to resolve inequalities among professionals and patients? Can medical innovation exacerbate inequalities and how can this be prevented?
• How does the adoption of new medical technologies influence organizational commitment, clinicians’ motivations and job satisfaction in health organizations?
• How to manage and organize the delicate tension between new and old routines and technologies (e.g. laparoscopic vs robotic surgery) in health organizations? Can health organizations develop technological ambidexterity, and how can this be theorized and empirically tested in the context of health organizations and systems?
• Do new medical technological technologies build or disrupt the resources and capabilities portfolios of clinicians and other healthcare professionals? How can health organizations and systems design and implement interventions that can help bridge the capability gap?
• How is big data and machine learning technologies beginning to impact on health organizations? Will it help professionals and patients better discern and filter data or will it lead to new tensions and inequalities?
• What theoretical lenses are better able to understanding the development and implementation of new medical technology in health organizations and systems? What are the empirical challenges related to testing such theories?
   

References

• Boonstra, A., & Broekhuis, M. (2010): “Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions.” BMC Health Services Research, 10 (1), 231.
• Dattée, B., & Barlow, J. (2017): “Multilevel Organizational Adaptation: Scale Invariance in the Scottish Healthcare System.” Organization Science, 28 (2), 301–319.
• de Lusignan, S., Mold, F., Sheikh, A., Majeed, A., et al. (2014): “Patients’ online access to their electronic health records and linked online services: a systematic interpretative review.” BMJ Open, 4 (9), e006021.
• Goh, J., Pfeffer, J., & Zenios, S.A. (2016): “The Relationship Between Workplace Stressors and Mortality and Health Costs in the United States.” Management Science, 62 (2), 608–628.
• Lu, S.F., & Rui, H. (2018): “Can We Trust Online Physician Ratings? Evidence from Cardiac Surgeons in Florida.” Management Science, 64 (6), 2557–2573.
• Murdoch, T.B., & Detsky, A.S. (2013): “The Inevitable Application of Big Data to Health Care.” JAMA, 309 (13), 1351–1352.
• Tjora, A.H., & Scambler, G. (2009): “Square pegs in round holes: Information systems, hospitals and the significance of contextual awareness.” Social Science & Medicine, 68 (3), 519–525.
• World Health Organization (2017): 10 facts on health inequities and their causes, http://www.who.int/features/factfiles/health_inequities/en/.

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