The Value of Electronic Health Records Since the Health Information Technology for Economic and Clinical Health Act: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Electronic health records (EHRs) are the electronic records of patient health information created during ≥1 encounter in any health care setting. The Health Information Technology Act of 2009 has been a major driver of the adoption and implementation of EHRs in the United States. Given that the adoption of EHRs is a complex and expensive investment, a return on this investment is expected. OBJECTIVE: This literature review aims to focus on how the value of EHRs as an intervention is defined in relation to the elaboration of value into 2 different value outcome categories, financial and clinical outcomes, and to understand how EHRs contribute to these 2 value outcome categories. METHODS: This literature review was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The initial search of key terms, EHRs, values, financial outcomes, and clinical outcomes in 3 different databases yielded 971 articles, of which, after removing 410 (42.2%) duplicates, 561 (57.8%) were incorporated in the title and abstract screening. During the title and abstract screening phase, articles were excluded from further review phases if they met any of the following criteria: not relevant to the outcomes of interest, not relevant to EHRs, nonempirical, and non-peer reviewed. After the application of the exclusion criteria, 80 studies remained for a full-text review. After evaluating the full text of the residual 80 studies, 26 (33%) studies were excluded as they did not address the impact of EHR adoption on the outcomes of interest. Furthermore, 4 additional studies were discovered through manual reference searches and were added to the total, resulting in 58 studies for analysis. A qualitative analysis tool, ATLAS.ti. (version 8.2), was used to categorize and code the final 58 studies. RESULTS: The findings from the literature review indicated a combination of positive and negative impacts of EHRs on financial and clinical outcomes. Of the 58 studies surveyed for this review of the literature, 5 (9%) reported on the intersection of financial and clinical outcomes. To investigate this intersection further, the category "Value-Intersection of Financial and Clinical Outcomes" was generated. Approximately 80% (4/5) of these studies specified a positive association between EHR adoption and financial and clinical outcomes. CONCLUSIONS: This review of the literature reports on the individual and collective value of EHRs from a financial and clinical outcomes perspective. The collective perspective examined the intersection of financial and clinical outcomes, suggesting a reversal of the current understanding of how IT investments could generate improvements in productivity, and prompted a new question to be asked about whether an increase in productivity could potentially lead to more IT investments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".