ArticleApplied clinical informatics2017
Barriers to Achieving Economies of Scale in Analysis of EHR Data. A Cautionary Tale.
Article in Applied clinical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine.Applied clinical informatics · 2021Pooled it
- Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine.Scientific reports · 2022Trial
- Machine Learning to Identify Point-of-Care Ultrasound and Evaluate Standardized Documentation: Retrospective Operational Cohort Study.Journal of medical Internet research · 2026Article
- Immunologically Adaptive Endovascular Devices: Integrating Thrombo-Inflammation, Biomaterials Design, and Artificial Intelligence for Precision Cardiovascular Intervention.International journal of molecular sciences · 2026Review
- Establishment of a Diabetes-Tailored Data Intelligence Platform Enhances Clinical Care, Enables Risk-Based Monitoring, and Facilitates Population-Health-Based Approaches at a Pediatric Diabetes Network.Journal of diabetes science and technology · 2025Article
- Challenges and Recommendations for Electronic Health Records Data Extraction and Preparation for Dynamic Prediction Modeling in Hospitalized Patients: Practical Guide and Tutorial.Journal of medical Internet research · 2025Article
- Generative AI costs in large healthcare systems, an example in revenue cycle.NPJ digital medicine · 2025Article
- Predicting and Ranking Diabetic Ketoacidosis Risk Among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model.Diabetes technology & therapeutics · 2025Article
- Building models, building capacity: A review of participatory machine learning for HIV prevention.PLOS global public health · 2025Review
- A roadmap to implementing machine learning in healthcare: from concept to practice.Frontiers in digital health · 2025Article
- A multi-center study on the adaptability of a shared foundation model for electronic health records.NPJ digital medicine · 2024Article
- Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study.JMIR formative research · 2023Article
- DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Accelerating health system innovation: principles and practices from the Duke Institute for Health Innovation.Patterns (New York, N.Y.) · 2023Review
- APLUS: A Python library for usefulness simulations of machine learning models in healthcare.Journal of biomedical informatics · 2023Article
- Perspective Toward Machine Learning Implementation in Pediatric Medicine: Mixed Methods Study.JMIR medical informatics · 2022Article
- Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities.JMIR medical informatics · 2022Article
- Monitoring Approaches for a Pediatric Chronic Kidney Disease Machine Learning Model.Applied clinical informatics · 2022Article
- Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis.NAM perspectives · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Signed in 2009, the Health Information Technology for Economic and Clinical Health Act infused $28 billion of federal funds to accelerate adoption of electronic health records (EHRs). Yet, EHRs have produced mixed results and have even raised concern that the current technology ecosystem stifles innovation. We describe the development process and report initial outcomes of a chronic kidney disease analytics application that identifies high-risk patients for nephrology referral. The cost to validate and integrate the analytics application into clinical workflow was $217,138. Despite the success of the program, redundant development and validation efforts will require $38.8 million to scale the application across all multihospital systems in the nation. We address the shortcomings of current technology investments and distill insights from the technology industry. To yield a return on technology investments, we propose policy changes that address the underlying issues now being imposed on the system by an ineffective technology business model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.