Accelerating Virtual Health Implementation Following the COVID-19 Pandemic: Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic accelerated drivers for virtual health adoption and triggered the US federal government to implement regulatory changes to reduce barriers to virtual health implementation. Consequently, virtual health solutions have been increasingly adopted, and health systems in the United States have been reorganizing their care delivery process with unprecedented speed. OBJECTIVE: This study aimed to assess and make recommendations on the strategy, business model, implementation, and future considerations for scaling and sustaining virtual health solutions based on the views of executives from the largest health systems in the United States. METHODS: In September 2020 and October 2020, the Health Management Academy conducted 29 quantitative surveys and 23 qualitative interviews involving 58 executives from 41 of the largest health systems in the United States. Participating health systems were approximately equally distributed across size categories (small, medium, and large, defined as annual total operating revenue US $2-3 billion, $3-6 billion, and >$6 billion, respectively) and US Census Bureau regions (Northeast, Midwest, South, and West). RESULTS: Based on the Health Management Academy's assessment of approaches to governance, financing, data infrastructure, and clinical integration of virtual health, most participating health systems (13/24, 54%) had a mid-stage level of maturity in virtual health implementation. Executives reported the pandemic is forcing health systems to re-examine strategic priorities; the most commonly raised key impacts were increased access (15/21, 71%) and flexibility (10/21, 48%) as well as lower costs of care delivery (9/21, 43%). Most executives (16/28, 57%) reported their organization had a defined budget for virtual health, and many noted that virtual health is best supported through value-based payment models. Irrespective of health system maturity, reimbursement was consistently rated as a key challenge to virtual health scaling, along with patient access to and understanding of virtual health technology. The success of virtual health implementation was most commonly measured by patient satisfaction, health care provider engagement, and proportion of health care providers using virtual health solutions (reported by 7/8, 88%; 6/8, 75%; and 7/8, 75% of information technology executives, respectively). Almost all health systems (27/29, 93%) expect to continue growing their virtual health offerings for the foreseeable future, with user-friendliness and ease of integration into the electronic medical record as key factors in making go-forward decisions on virtual health solutions (each selected by 9/10, 90% executives). CONCLUSIONS: The increased demand for virtual health solutions during the COVID-19 pandemic is expected to continue postpandemic. Consequently, health systems are re-evaluating their current platforms, processes, and strategy to develop a sustainable, long-term approach to virtual health. To ensure future success, health system leaders need to proactively build on their virtual health solutions; advocate for payment, site flexibility, and reimbursement parity for virtual health; and demonstrate continued engagement and boldness to evolve care beyond established models.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".