Digital Transformation in Healthcare: Preliminary Results from a Senior Leadership Study.
Bibliographic record
Abstract
Organizations of all types are facing challenges in the new digital age to remain competitive (Hess et al, 2016). With customer expectations changing and new technologies emerging, organizations need to change their business models to remain relevant and sustain competitive advantage. Healthcare in particular is one segment that is undergoing digital transformation. This is motivated largely by the desire to improve cost, patient satisfaction, patient outcomes, quality of care, provider experience and other important facets of the healthcare experience. According to some, strategic changes enabled by digital technology can allow healthcare organizations to re-shape their business models and improve the aforementioned facets. Digital transformation in healthcare therefore emphasizes strategic endeavors enabled by emerging digital technologies to improve patient care and enhance patient outcomes in particular (Gupta, 2016). Although achieving strategic advantage is the overall stated goal of digital transformation, advancements are needed to understand the link between digital transformation activities and the overall business strategy, specifically in the healthcare industry. The key question business leaders need to respond to is - how to incorporate digital transformation and use it as a means for competitive advantage (Hess et al, 2016). The concept of digital transformation has also been one of interest to information systems academics as of late. Several scholars have attempted to argue the link between digital transformation strategies and integration efforts of various new technologies. Despite these efforts, the link between digital transformation strategies and the overall approach as to how the firm expects to achieve competitive advantage is unclear. As well, several scholars have argued that digital transformation lacks a substantive underlying theory, and work in this area will further guide future research and innovation activities. The purpose of this research is to determine how digital transformation efforts of a healthcare firm explain their approach to achieve competitive advantage. This paper reports on the preliminary results of an ongoing qualitative study involving 18 C-level information technology leaders in American healthcare organizations. The purpose of this TREO talk is twofold. First, the authors wish to share the preliminary findings of the study. Second, the authors wish to discuss the potential of integrating various theoretical approaches into research on digital transformation in healthcare, to give the concept of digital transformation a better theoretical underpinning for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".