A Sense of Coherence Approach to Improving Patient Experience Using Information Infrastructure Modeling: Design Science Research
Bibliographic record
Abstract
BACKGROUND: Health care provider organizations are complex and dynamic environments. Consequently, how the physical and social environment of such organizations interact with an individual is a primary driver of an individual's experience. Increasingly, the capabilities required for them to successfully interact with those within their care are critically dependent on the information infrastructure they have in place, which enables people, both patients and staff, to work optimally together to deliver their clinical and operational objectives. OBJECTIVE: This study aims to design a framework to address the challenge of how to assemble information systems in health care to support an improved sense of coherence for patients, as well as potentially innovate patients' experiences, by connecting and orchestrating the synergy among people, processes, and systems. METHODS: It is necessary to understand the needs of health care providers and patients to address this challenge at a level relevant to information process design and technology development. This paper describes the design science research method used to combine the sense of coherence, which is a core concept within the Antonosky salutogenic approach to health and well-being, with an established information infrastructure maturity framework, demonstrating the coalescence of 2 distinct conceptual perspectives on care delivery. This paper provides an approach to defining a positive and supportive health care experience and linking this to the capabilities of an information- and technology-enabled environment. RESULTS: This research delivers a methodology for describing the patient experience in a form relevant to information infrastructure design, articulating a pathway from information infrastructure to patient experience. It proposes that patient experience can be viewed pragmatically in terms of the established sense of coherence concept, with its ability to identify and guide resources to modulate a patient's environmental stressors. This research establishes a framework for determining and optimizing the capability of a facility's information infrastructure to support the sense of coherence defined by the experiences of its patients. CONCLUSIONS: This groundbreaking research provides a framework for health care provider organizations to understand and assess the ability of their information infrastructure to support and improve the patient experience. The tool assists providers in defining their technology-dependent operational goals around patient experience and, consequently, in identifying the information capabilities needed to support these goals. The results demonstrate how a fundamental shift in thinking about the use of information infrastructure can transform the patient experience. This study details an approach to describing information infrastructure within an experience-oriented framework that enables the impact of technology on experience to be designed explicitly. The contribution to knowledge is a new perspective on modeling how information infrastructure can contribute to supportive health-promoting environments. Furthermore, it may significantly affect the design and deployment of future digital infrastructures in health care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".