Responsible innovation with digital platforms: Cases in India and Canada

2023 | Information Systems Journal | Citations: 6

Authors: Ahuja, Suchit; Chan, Yolande E.; Krishnamurthy, Rashmi

Abstract: Marginalized communities globally encounter grand challenges such as lack of acc ... Expand

Abstract: Marginalized communities globally encounter grand challenges such as lack of access to education, healthcare, and sustained livelihoods. Several initiatives to address these complex, global problems have resulted in fragmented solutions. Recognizing this, there have been several calls for the study of responsible innovation (RI) to address grand challenges. Digital platforms such as AirBnB, Uber and so forth have now become commonplace and are known to generate economic value but also face criticism for being exploitative and exclusive. Only a handful of studies show how similar platforms can innovate responsibly to serve marginalized communities by generating simultaneous economic and social value. To address this gap, our study examines the cases of two platforms that orchestrated ecosystems consisting of individuals from marginalized communities, government agencies, and other entities to provide physical, digital and societal solutions based on principles of RI. We contribute to the RI and IS literatures to show how RI solutions can be fostered through digital platforms to address grand challenges. The article provides empirical evidence of all four dimensions of the RI framework—anticipation, reflexivity, inclusion, and responsiveness - and their operationalization through digital platforms. This research lays the foundation for future studies at the intersection of RI and digital platforms literature. The study also provides practice insights on developing digital platform solutions for marginalized communities to address grand challenges and is useful to policymakers to formulate appropriate interventions. It pushes the theoretical and practice boundaries of our understanding of RI and digital platforms. Collapse

Topics: digital platform mobile application social media innovation management business model

Methods: qualitative interview theory development case study triangulation archival research

Theories: theory of economic development

Managing compliance with privacy regulations through translation guardrails: A health information exchange case study

2023 | Information and Organization | Citations: 0

Authors: Anderson, Chad; Baskerville, Richard; Kaul, Mala

Abstract: Information privacy is increasingly important in our digitally connected world, ... Expand

Abstract: Information privacy is increasingly important in our digitally connected world, particularly in healthcare, and privacy regulations are ramping up to promote appropriate privacy practices. As a digital platform that enables healthcare providers to exchange protected health information (PHI), a health information exchange (HIE) is governed by health information privacy regula­ tions. The challenge for HIEs is to operate in a way that will maximize information exchange while maintaining compliance with regulations that may constrain the sharing of PHI. Regula­ tions impose a measure of universality through compliance requirements, while being flexible to allow adaptation to the local context. However, our longitudinal case study into the privacy policies of an HIE, demonstrates that the journey of privacy ideas from their original formulation in regulations, to their ultimate enactment in an organizational setting, is accompanied by translations, such that the final implementation may vary extensively from its original form. Such variability often results in interpretations that differ from what the regulators intended. Conse­ quently, translation guardrails are necessary to protect against problematic translations of reg­ ulatory ideas which could lead to compliance issues and loss of platform participation. Our findings offer two contributions. First, we contribute to the compliance literature by explaining how guardrails can balance the use of permission and obligation schemas which are necessary to translate regulations into effective organizational policies for the success of HIEs and other in­ formation exchange platforms. Second, we contribute to extending translation theory by explaining how pragmatic reasoning schemas function as the mechanism through which trans­ lation of regulations occurs. Collapse

Topics: privacy information privacy law data security information exchange legal context

Methods: qualitative interview longitudinal research case study longitudinal case study literature study

Theories: organizational behavior theory

Blockchain innovation for consent self-management in health information exchanges

2023 | Decision Support Systems | Citations: 0

Authors: Anderson, Chad; Carvalho, Arthur; Kaul, Mala; Merhout, Jeffrey W.

Abstract: With the increasing digitalization of health data, patients need to make informe ... Expand

Abstract: With the increasing digitalization of health data, patients need to make informed decisions about the online sharing of their protected health information. This necessitates a robust technical infrastructure that enables patients to self-manage consent and the trusted exchange of this information across sharing entities. Unfortu­ nately, current health information exchange systems in the U.S. are limited in both these regards. While there is recent work on digital patient consent management, there is limited work that provides effective solutions for patient self-management of consent. Further, interoperability issues in the way health information exchanges are currently architected and differences in regulations across localities exacerbate the challenges of consent man­ agement. In this research, we survey potential patients' willingness to self-manage healthcare-related consent. Having established the desire for consent self-management, we propose a solution that enables the seamless sharing of patient consent across different healthcare providers and health information exchanges. Specifically, we use a rigorous design science approach to create a blockchain-based, self-managed patient consent system and we evaluate the design through an instantiated prototype. The results of our study should be useful to researchers in healthcare information management as well as to practitioners designing consent management systems. Our research contributes to design science research with an innovative, rigorously evaluated, design principles-based artifact that addresses a critical problem of sharing protected health information. Collapse

Topics: blockchain mobile application information exchange privacy Ethereum

Methods: survey design artifact design science design process design principle

Theories: evidence-based design

Does Analytics Help Resolve Equivocality in the Healthcare Context? Contrasting the Effects of Analyzability and Differentiation

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Adjerid, Idris; Angst, Corey; Devaraj, Sarv; Berente, Nicholas

Abstract: Organizations are increasingly using data analytics to help make decisions and ... Expand

Abstract: Organizations are increasingly using data analytics to help make decisions and drive positive outcomes. But organizational scholarship has warned us that the sort of information processing associated with analytic capabilities, while effective for uncertainty reduction, may be less effective in equivocal contexts. Equivocality is evident when tasks are not easily analyzable (task analyzability) or when organizational departments are highly differentiated (differentiation). We hypothesize that analytics will be less effective in driving positive outcomes when equivocality is high because of low task analyzability. However, when an organization is more differentiated, resulting in high equivocality, we anticipate that analytics will be more effective in driving positive outcomes. To test this theory, we studied how clinical healthcare analytics influenced experiential quality (akin to patient satisfaction) in over 3,000 hospitals across nine years. Our results show that analytics capabilities, on average, do improve outcomes in terms of patient experiential quality, suggesting that analytics can reduce uncertainty, but we also found evidence for the moderating role of equivocality. Specifically, as task analyzability decreases (i.e., increasing equivocality), clinical healthcare analytics becomes less effective in improving experiential quality. However, when equivocality is high because of differentiation, there is a positive relationship between clinical healthcare analytics and experiential quality but only in larger hospitals. From a managerial perspective, this study has implications for boundary conditions of data analytics in organizations. Collapse

Topics: healthcare analytics technology adoption electronic health record health information system artificial intelligence

Methods: theory development post-hoc analysis experimental group longitudinal research machine learning

Theories: organizational information processing theory

At What Price? Exploring the Potential and Challenges of Differentially Private Machine Learning for Healthcare

2023 | HICSS | Citations: 1

Authors: Aslan, Aycan; Matschak, Tizian; Greve, Maike; Trang, Simon; Kolbe, Lutz

Abstract: The increased generation of data has become one of the main drivers of technolog ... Expand

Abstract: The increased generation of data has become one of the main drivers of technological innovation in healthcare. This applies in particular to the adoption of Machine Learning models that are used to generate value from the growing available healthcare data. However, the increased processing of sensitive healthcare data comes with challenges in terms of data privacy. Differential privacy, the method of adding randomness to the data to increase privacy, has gained popularity in the last few years as a possible solution. However, while the addition of randomness increases privacy, it also reduces overall model performance, generating a privacy-utility trade-off. Examining this trade-off, we contribute to the literature by providing an empirical paper that experimentally evaluates two prominent and innovative methods of differentially private Machine Learning on medical image and text data to deepen the understanding of the existing potential and challenges of such methods for the healthcare domain. Collapse

Topics: privacy differential privacy healthcare data knowledge base data protection

Methods: experiment computational algorithm machine learning blood pressure PyTorch

Quality Indicators of Online Cancer Communities from the Perspective of Cancer-Affected People

2023 | HICSS | Citations: 0

Authors: Badreddine, Basma; Blount, Yvette; Amrollahi, Alireza

Abstract: Online cancer communities established by non-profit cancer organisations help ca ... Expand

Abstract: Online cancer communities established by non-profit cancer organisations help cancer-affected people seeking information and facing emotional and psychological challenges to communicate and interact with a network of people suffering from similar issues. However, the quality features of online cancer communities from the perception of users of the information are under-researched. Using the factors credibility, content, and design, this study adopted a multi-theory perspective by integrating the Information Quality (IQ) assessment framework, the source credibility model, and the two-factor theory for website design to develop a framework to examine the quality indicators of online cancer communities. Data collected via semi-structured interviews with 14 participants showed that dimensions underlying content and credibility factors are crucial for using the online community. All dimensions underlying the design factor were essential factors, except for the visualisation of the website and privacy and data protection that constituted motivating factors for using the online community. Collapse

Topics: website design data quality privacy video conference user-generated content

Methods: qualitative interview literature study qualitative content analysis personal interview

Theories: two-factor theory

Value Implications of Sourcing Electronic Health Records: The Role of Physician Practice Integration

2023 | Information Systems Research | Citations: 0

Authors: Bardhan, Indranil R.; Bao, Chenzhang; Ayabakan, Sezgin

Abstract: Should hospitals source electronic health records (EHR) systems from a single ve ... Expand

Abstract: Should hospitals source electronic health records (EHR) systems from a single vendor or multiple vendors to deliver high-value care? We study hospitals’ EHR sourcing strategies based on their degree of integration with physician practices and its impact on the value of healthcare delivered. We propose a novel framework to define healthcare value as the extent to which a hospital effectively expends clinical resources to deliver services that improve patient outcomes. Drawing on modular systems and transaction cost economics theories, we propose a moderated-mediation model that explores the pathways through which EHR sourcing strategies can create value in healthcare. We test our research hypotheses on a large, longitudinal sample of U.S. hospitals and observe that hospitals with EHR configurations closer to single sourcing strategies exhibit greater health information sharing compared with hospitals with multisourced EHR systems. Furthermore, we find that hospital-physician practice integration moderates the impact of single sourcing on health information sharing, which in turn, improves value. Specifically, tighter integration between hospitals and physician practices can create greater value if it is aligned with hospitals’ EHR sourcing strategies. As the healthcare industry moves toward value-based payment reform, our findings provide a useful roadmap to practitioners and policy makers to improve the performance of hospitals and healthcare providers. History: Rajiv Kohli, Senior Editor; Sunil Wattal, Associate Editor. Funding: I.R. Bardhan thanks the Foster Parker Centennial Professorship and the Dean’s Research Excellence Grant at the McCombs School of Business at UT Austin for generous financial support. C. Bao thanks the Spears Fellowship at Oklahoma State University for financial support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1183. Collapse

Topics: information exchange healthcare data health information system accounting database system

Methods: longitudinal research survey robustness check econometric modeling design artifact

Theories: transaction cost economics general systems theory institutional theory

Optimizing Substance Use Treatment Selection Using Reinforcement Learning

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Baucum, Matt; Khojandi, Anahita; Myers, Carole; Kessler, Larry

Abstract: Substance use disorder (SUD) exacts a substantial economic and social cost in th ... Expand

Abstract: Substance use disorder (SUD) exacts a substantial economic and social cost in the United States, and it is crucial for SUD treatment providers to match patients with feasible, effective, and affordable treatment plans. The availability of large SUD patient datasets allows for machine learning techniques to predict patient-level SUD outcomes, yet there has been almost no research on whether machine learning can be used to optimize or personalize which treatment plans SUD patients receive. We use contextual bandits (a reinforcement learning technique) to optimally map patients to SUD treatment plans, based on dozens of patient-level and geographic covariates. We also use near-optimal policies to incorporate treatments’ time-intensiveness and cost into our recommendations, to aid treatment providers and policymakers in allocating treatment resources. Our personalized treatment recommendation policies are estimated to yield higher remission rates than observed in our original dataset, and they suggest clinical insights to inform future research on data-driven SUD treatment matching. Collapse

Topics: electronic health record individual characteristic healthcare data missing data

Methods: hazard function machine learning random forest classification descriptive statistic propensity score method

Human-machine collaboration for feature selection and integration to improve congestive Heart failure risk prediction

2023 | Decision Support Systems | Citations: 0

Authors: Ben-Assuli, Ofir; Heart, Tsipi; Klempfner, Robert; Padman, Rema

Abstract: The issue of harnessing machine learning (ML) algorithms for the prediction of a ... Expand

Abstract: The issue of harnessing machine learning (ML) algorithms for the prediction of adverse medical events is important considering the prevalence of vast repositories of patient-level medical data, and the quest to reduce healthcare costs while elevating the quality of care. Of particular interest is identifying a set of features that can better predict the risk of unplanned readmissions of chronically ill patients within 30 days post discharge, among them patients with Congestive Heart Failure (CHF). While numerous studies have employed ML algorithms to identify sets of significant features, only a handful have compared features extracted by various methods, and even fewer have compared the predictive power of feature sets selected by human experts to those selected by ML. Hence, this research aimed to elicit a comprehensive feature set selected by four ML algorithms and compare its predictive performance to that of a feature set selected by experts. We then evaluated a merged list of the two feature sets, constructing a human-machine collaborative set to predict the likelihood of 30-day unplanned readmission of CHF patients using data on 10,763 CHF patients hospitalized during the years 2010–2017. Our models achieved Area under the ROC Curve (AUC) above 0.8 and an Accuracy of ~0.89, comparable to the best models in the extant literature. Moreover, the Human-Machine Collaborative model significantly outperformed (best AUC 0.836) other human-only selected models as well as the machine-selected set. This study contributes to a better understanding of the power of using ML for patient risk stratification with special attention to the benefits of human-machine collaboration, even when these two entities work separately or in parallel, for improved clinical decision making. Collapse

Topics: healthcare data decision making big data missing data data warehouse

Methods: machine learning computational algorithm XGBoost descriptive statistic random forest classification

User Privacy, Surveillance and Public Health during COVID-19 – An Examination of Twitterverse

2023 | Information Systems Frontiers | Citations: 0

Authors: Bhatt, Paras; Vemprala, Naga; Valecha, Rohit; Hariharan, Govind; Rao, H. Raghav

Abstract: Online users frequently rely on social networking platforms to transmit public c ... Expand

Abstract: Online users frequently rely on social networking platforms to transmit public concerns and raise awareness about societal issues. With many government organizations actively employing social media data in recent times, the need for processing public concerns on social media has become a critical topic of interest across academic scholars and practitioners. However, the growing volume of social media data makes it difficult to process all the issues under a single umbrella, causing to overlook the main topic of interest within communication technologies, such as privacy. For example, during the COVID-19 pandemic, arguments on privacy and health issues exploded on Twitter, with several threads centered on contact tracking, health data gathering, and its usage by government agencies. To address the challenges of rising data volumes and to understand the importance of privacy concerns, particularly among users seeking greater privacy protection during this pandemic, we conduct a focused empirical analysis of user tweets about privacy. In this two-part research, our first study reveals three macro privacy issues of discussion distilled from the Twitter corpus, subsequently subdivided into 12 user privacy categories. The second study builds on the findings of the first study, focusing on the primary difficulties highlighted in the macro privacy subjects—contact tracing and digital surveillance. Using a document clustering approach, we present implications for the focal privacy topics that policymakers, agencies, and governments should consider for offering better privacy protections and help the community rebuild. Collapse

Topics: privacy Twitter social media pandemic information privacy concern

Methods: topic model qualitative content analysis cluster analysis hierarchical clustering Gensim