Human Resource Information Systems in Health Care: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Compared with the eHealth literature as a whole, there has been relatively little published research on the use and impact of information and communication technologies (ICTs) designed to support business functions within health organizations. Human resource information systems (HRISs) have the potential to improve organizational efficiency and effectiveness by facilitating workforce planning, financial and operational administration, staff training, and management analytics. However, the evidence base regarding HRIS in health care is widely distributed across disciplinary boundaries and previous reviews have been somewhat limited in scope. This rigorous systematic review will identify, appraise, and synthesize existing international research on the implementation and impacts of HRIS in health organizations, to provide insights and recommendations that may guide future purchasers, commissioners, implementers, evaluators, and users of such systems. OBJECTIVE: The objectives of this review are threefold: (1) to determine the prevalence and scope of existing research and evaluation pertaining to HRIS in health organizations; (2) to analyze, classify, and synthesize existing evidence on the processes and impacts of HRIS development, implementation, and adoption; and (3) to generate recommendations for HRIS research, practice, and policy, with reference to the needs of different stakeholders and communities of practice. METHODS: A high-level scoping review was first undertaken to inform a draft search strategy, which was refined through several cycles of piloting and iteration to optimize its sensitivity and specificity. This was used by the first author, with the help of a medical librarian, to search international electronic databases indexing medical, business, ICT, and multi-disciplinary research. Sources of gray literature and reference lists of included studies were also searched. There were no restrictions on language or publication year. Two reviewers are now screening and coding titles and abstracts for potentially eligible studies, for which full text articles will be retrieved. Reasons for exclusion will be noted for the remaining articles. A structured form will be used to summarize and classify the articles. Any disagreements between reviewers will be resolved through consensus or arbitration by a third reviewer. A PRISMA flow diagram will illustrate the study selection process and ensure transparency of the review. Finally, content experts will be consulted to ensure that important articles have not been missed. RESULTS: The initial searches have now been completed and the results are being analyzed. The review is expected to be completed and published by the end of 2015. CONCLUSIONS: By synthesizing the existing evidence base, identifying areas in which knowledge is currently lacking, and generating recommendations for research and practice, this review will be a useful resource for decision makers and managers considering or implementing HRIS, as well as encouraging new research in this area. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews: CRD42015023581; http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42015023581#.VYu1BPlVjDU (Archived by WebCite at http://www.webcitation.org/6ckJCDdCL).
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 | 0.090 | 0.126 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.014 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.091 | 0.011 |
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".