Factors Influencing Innovation in Healthcare: A conceptual synthesis
Bibliographic record
Abstract
ABSTRACTThis paper examines the factors driving innovation in the health sector. It specifically explores the factors that drive innovation in the National Health Service (NHS) United Kingdom. A literature review of innovation models and drivers for innovations in organizations was conducted. The secondary data were collected from various NHS publications on healthcare innovation. Data from secondary sources were reviewed and synthesised with the existing models in the literature. The findings show that there are several factors driving innovation in the health sector. In addition to other factors found in the literature, innovation is spurred through responses to the challenges of cost, supply chain problems and sustainability concerns. This implies that certain non-medical factors can influence the need for innovation in the health sector. A conceptual framework is developed to describe the factors influencing the need for innovation in the health sector.Keywords: Innovation, Health Sector, Change, NHSIntroductionInnovation has been a consistent feature of the private sector for a number of years. Likewise, studies into innovative practices in the public sector have increased during the last three decades. Despite this relatively broad period in which innovation has been discussed and studied, the way it emerges in the literature shows that more is leftto be learnt. It is not surprising, therefore, to see the adoption of innovation arising in public debates and academic discussions. Innovation may mean different things to different people, professions and businesses (Mulgan and Albury 2003; Borins, 2001). Additionally, innovation will not perform its intended purpose in an organisation until appropriate building blocks are put in place. The ability to understand and leverage these factors determines the degree to which innovation can be disseminated within an organisation (Greenhalgh et al., 2004). Some studies have been carried out to discover the barriers to innovation diffusion (Fitzgerald et al., 2001; Leeman et al, 2007). Innovation must be part of the organizational culture. It must be both encouraged and rewarded; this organizational entrepreneurship is very rare in highly centralized organizations.The National Health Service (NHS) is the largest publicly funded healthcare system in Europe, providing high quality and safe health services to the residents of the United Kingdom (UK). As an important institution within the UK public sector, the significance of the NHS goes beyond healthcare provision. It is also the largest employer in the UK, with a workforce of more than 1.7 million (www.nhs.uk). The NHS's vision is to provide affordable and accessible healthcare based on patients' needs (NHS Plan, 2000). The NHS has deployed various initiatives to move healthcare closer its local population, using innovative services and technologies (Department of Health, 2009a). In the NHS Constitution, innovation is identified as one of the tools for improving healthcare (NHS, 2010). The National Innovation Centre (NIC) was established to regulate issues of clinical performance and innovation. One of the major achievements of NIC is a tool called scorecard, which helps clinicians and commissioners discover the strength and weakness of their ideas (NHS Institute, 2008). The scorecard also provides improvement suggestions for ideas generated within the NHS. Despite these efforts, the NHS has a lot to do in the area of service innovation to fully achieve its objectives (Wanless, 2004; Sheldon, 2004; Black, 2006; Cooksey, 2006; Liddell et al., 2008; Darzi, 2008). This is not suprising since it is not a new thought in organizational theory and behaviour that large bureaucratic, government controlled, centrally planned organizations are monumentally difficult to change.Researchers have also shown that organisations initiate and implement new ideas in unplanned manners (Knudsen & Roman, 2004; Hargadon, 2003). …
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".