Laurie L. Novak

Mediating the intersections of organizational routines during the introduction of a health IT system.

Public interest in the quality and safety of health care has spurred examination of specific organizational routines believed to yield risk in health care work. Medication administration routines, in particular, have been the subject of numerous improvement projects involving information technology development, and other forms of research and regulation. This study draws from ethnographic observation to examine how the common routine of medication administration intersects with other organizational routines, and why understanding such intersections is important. We present three cases describing intersections between medication administration and other routines, including a pharmacy routine, medication administration on the next shift and management reporting. We found that each intersection had ostensive and performative dimensions; and furthermore, that IT-enabled changes to one routine led to unintended consequences in its intersection with others, resulting in misalignment of ostensive and performative aspects of the intersection. Our analysis focused on the activities of a group of nurses who provide technology use mediation (TUM) before and after the rollout of a new health IT system. This research offers new insights on the intersection of organizational routines, demonstrates the value of analyzing TUM activities to better understand the relationship between IT introduction and changes in routines, and has practical implications for the implementation of technology in complex practice settings.

Technical infrastructure implications of the patient work framework

In their response to our original paper, “Transforming Consumer Health Informatics through a Patient Work Framework: Connecting Patients to Context,” Marceglia and colleagues propose an architecture that integrates the patient work framework into a higher-order framework linking consumer health informatics (CHI) applications and professional health information systems (designated by the authors as the health-Information Technology (IT) ecosystem).1 The purpose of our letter is threefold. First, we detail how an expanded understanding of the patient work framework already conceptually encompasses the larger contexts in which CHI use must occur. Second, we assert that meaningful application of the patient work perspective yields implications not only for integration with professional health information systems but also with the larger information infrastructures within the community. Third, we propose modifications to Marceglia and colleagues’ architecture to explicitly represent a “shared space” between CHI applications and professional health information systems; this space contains collaborative work and collaborative informatics. Our original patient work framework was intended to serve as a foundation for CHI design by enabling the understanding of people, their daily contexts, and their daily activities. As such, we limited the scope of our discussion to the immediate home and community environments of the …

Evidence-based Health Informatics Frameworks for Applied Use.

Health Informatics frameworks have been created surrounding the implementation, optimization, adoption, use and evaluation of health information technology including electronic health record systems and medical devices. In this contribution, established health informatics frameworks are presented. Important considerations for each framework are its purpose, component parts, rigor of development, the level of testing and validation its undergone, and its limitations. In order to understand how to use a framework effectively, its often necessary to seek additional explanation via literature, documentation, and discussions with the developers.

Patient Work as a Maturing Approach Within HF/E Moving Beyond Traditional Self-Management Applications

Over the last five years, human factors and ergonomics (HF/E) applications in healthcare have expanded beyond the institution to encompass the homes and communities of patients. The patient work approach has been developed as a means of formalizing the theoretical and methodological foundations of applying HF/E within this new domain. Early applications of the patient work approach were confined to studies of relatively straightforward self-management tasks. As the patient work approach matures, however, its application is spreading to more complex subdomains of health management in home and community settings. This panel will focus on cutting edge application and methodological areas of patient work. Each panelist will introduce their work, present challenges related to expanding the patient work approach in a new direction, and engage with audience members to address these challenges and brainstorm ways in which the patient work approach can be further adapted to more intricate healthcare challenges.

Advancing In-Hospital Clinical Deterioration Prediction Models

Background Early warning systems lack robust evidence that they improve patients’ outcomes, possibly because of their limitation of predicting binary rather than time‐to‐event outcomes. Objectives To compare the prediction accuracy of 2 statistical modeling strategies (logistic regression and Cox proportional hazards regression) and 2 machine learning strategies (random forest and random survival forest) for in‐hospital cardiopulmonary arrest. Methods Retrospective cohort study with prediction model development from deidentified electronic health records at an urban academic medical center. Results The classification models (logistic regression and random forest) had statistical recall and precision similar to or greater than those of the time‐to‐event models (Cox proportional hazards regression and random survival forest). However, the time‐to‐event models provided predictions that could potentially better indicate to clinicians whether and when a patient is likely to experience cardiopulmonary arrest. Conclusions As early warning scoring systems are refined, they must use the best analytical methods that both model the underlying phenomenon and provide an understandable prediction.