Integration of health information technology to improve patient safety
Bibliographic record
Abstract
Background: Medical errors and unsafe care continue to harm and kill thousands of patients every year, exceeding deaths attributed to motor vehicle accidents, heart failure and breast cancer. It has been more than ten years since the Institute of Medicine (IOM) released two landmark reports that galvanized attention on the scope and severity of the problem and started a national movement to improve patient safety in healthcare. Both of these reports emphasized the role that health information technology (HIT) can play in improving the quality and safety of patient care. This has lead federal and state legislators to prominently feature HIT, patient safety, and quality outcomes in the current health care debate, laws, and payment schemes. The objective of this systematic literature review is to assess current and proposed use of technology to prevent adverse events and the regulatory drivers that are promoting the use of technology for patient safety. Methods: A systematic literature search from 2010-2013 using CINAHL, Cochrane Library, Medline and Ovid (via EBSCO),Google Scholar and government web sites, was conducted to identify technology drivers, advancements and effects in the patient safety areas of electronic health records, patient identification, patient falls, pressure ulcers, and medication errors. Results: Forty four articles were selected for inclusion in this review addressing legal and regulatory drivers for use of technology to support patient safety, and specific technologies to prevent adverse events related to patient identification, patient falls, pressure ulcers, and medication errors. Search criteria included technology to prevent each of these event types, and the Affordable Care Act (ACA), American Reinvestment and Recovery Act (ARRA), and the Health Information Technology for Economic and Clinical Health (HITECH) provisions related to patient safety. Conclusion: Healthcare reform initiatives are promoting an expanded role for HIT in improving the safety and quality of patient care in the U.S. healthcare system. Findings suggest that there are several technologies currently in development and use to prevent adverse events in patients. While patient safety technology shows great promise in preventing error and injury, it also presents potential to harm if not effectively developed, implemented and used. It is an adjunct to, not a replacement of, a skilled and attentive care giver. Normal 0 7.8 磅 0 2 false false false EN-US ZH-CN X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:普通表格; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";}
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".