R P Sundarraj

Healthcare organizations (HCO) are under pressure to adopt emerging solutions with a view to improving the quality and efficiency of their operations, patient care and clinical decisions. In the literature, while recommendations have been put forth to follow an organizational approach to the adoption and implementation of analytics, there is a paucity of research into what specifically constitutes healthcare-analytics (HA), as well the antecedents that can explain and predict its adoption-decisions. In this paper, we fill this gap, by first proposing a typology for HA and an adoption-decision model that integrates the TOE framework with DeLone and McLean IS Success Model. We use a case study for the initial validation of the typology and the model. Our study reveals that the HCO studied has the same types of the data proposed in our typology, although the usage of the data and current/future adoption of analytics depends on many factors beyond simply technology.

Purpose: While the adoption of health-analytics (HA) is expanding, not every healthcare organization understands the factors impacting its readiness for HA. An assessment of HA-readiness helps guide organizational strategy and the realization of business value. Past research on HA has not included a comprehensive set of readiness-factors and assessment methods. This study’s objective is to design artifacts to assess the HA-readiness of hospitals. Design/methodology/approach: The information-systems (IS) theory and methodology entail the iterative Elaborated Action Design Research (EADR)method, combined with cross-sectional field studies involving 14 healthcare organizations and 27 participants. The researchers determine factors and leverage multi-criteria decision-making techniques to assess HA-readiness. Findings: The artifacts emerging from this research include: (1) a map of readiness factors, (2) multi-criteria decision-making techniques that assess the readiness levels on the factors, the varying levels of factor-importance and the inter-factor relationships and (3) an instantiated system. The in-situ evaluation shows how these artifacts can provide insights and strategic direction to an organization through collective knowledge from stakeholders. Originality/value: This study finds new factors influencing HA-readiness, validates the well-known and details their industry-specific nuances. The methods used in this research yield a well-rounded HA readiness-assessment (HARA) approach and offer practical insights to hospitals.

In India, private hospitals are at the cusp of adopting health-analytics (HA) technology to manage their organizational performance through data-driven decision-making. Past studies have analyzed the applications and benefits of HA. Our study builds on this descriptive base to investigate the patterns of HA adoption and the institutional factors which impact adoption. We conducted a cross-sectional field study that involved ten Indian private healthcare organizations and four health-ecosystem partners and analyzed the case study data using an institutional theory lens. Our cases reveal that the breadth and depth of HA adoption varies and falls into three distinct patterns: far-sighted, conservative, and niche. We assess how these patterns are influenced by the two key dimensions of institutional environments (material-resource environment and institutional environment). We highlight distinctive factors (management support for building organizational trust on data-driven decisions and IT-Medical practitioner collaboration) that exert important contextual influences on HA adoption. Our study identifies areas of commonalty for HA adoption across national healthcare settings as well as contextual aspects representative of the burgeoning Indian healthcare field.

Healthcare organizations (HCO) adopt emerging technology solutions such as Healthcare Analytics (HA) to improve the quality and efficiency of their operations, patient care, and clinical decisions. While their intent is to adopt HA, many of them are unsure of their organizational readiness for that adoption. There is a paucity of research on HA pre-adoption readiness assessment techniques and tools, which can be leveraged by hospitals, to make well-informed decisions. In this paper, we fill this gap, by proposing a prototype design of an instrument based on the design science approach. We base the constructs used in our artifact on our findings from the multi-case study analysis of three hospitals, which we also discuss briefly. The key contribution of this research is the process that we followed in integrating the case study approach with DSRM in the area of HA adoption.