OPINIONS, PERSPECTIVE, AND COMMENTARY

From Pilot to Proof: Why Enterprise Digital Health Evidence Must Enter the Published Record

Sandeep Bhat, MSE

Visualized Ventures LLC, Princeton Junction, NJ, USA

Keywords: digital medicine, enterprise research, implementation science, real-world evidence, telehealth

Abstract

Enterprise digital health is no longer a collection of pilots. It is becoming operational infrastructure across life sciences, health systems, med tech, and digital health. Yet much of the most useful evidence never leaves the enterprise. It stays in internal briefs, dashboards, and readouts. This point of view makes a simple case: if telehealth and digital medicine are going to be trusted at scale, enterprise evidence needs to be published.

This article explains what can constitute publishable work and how teams can get started. It begins with questions to clarify and qualify, provides a brief counterexample story in which work was never shared, outlines six types of publishable archetypes, and lists a minimum threshold for evidence. It also addresses the real barriers: time, imperfect real-world data, and legal constraints. The focus is practical. In scope, disidentify or aggregate as appropriate, and be explicit about limitations. The goal is to convert internally locked learning into peer-reviewed contributions that advance knowledge. It concludes with the fact that THMT is a natural home for credible real-world telehealth stories and evidence.

Plain Language Summary

Digital health is now infrastructure. Many organizations generate evidence on telehealth, remote monitoring, and artificial intelligence-enabled workflows, but these lessons often remain inside slide decks and dashboards. When results are not shared, others cannot learn from them or avoid repeating mistakes. Many enterprise evaluations can be publishable if methods are transparent and limitations are stated clearly. Examples include implementation studies, pragmatic outcomes evaluations, protocol and operating model descriptions, governance case studies, interoperability lessons, and equity analyses. This article offers a practical pathway to convert internal evaluations into publishable manuscripts and encourages enterprises to share what they learn in a peer-reviewed venue such as Telehealth and Medicine Today.

 

 

Digital health has crossed a threshold. In many enterprise settings it is no longer an innovation experiment; it is operational infrastructure. Telehealth and remote monitoring shape care pathways at scale. Artificial intelligence (AI)-enabled tools influence triage, documentation, imaging workflows, and clinical operations. In clinical development, digital endpoints and decentralized or hybrid approaches are increasingly normal. This perspective is a follow-up to a THMT podcast on the same theme and extends that discussion with a practical publication pathway for enterprise teams.

A persistent gap remains. Enterprises generate evidence daily, but much of it never enters the published record.

• If enterprise digital health is to be trusted, its evidence must be shared.
• Adoption is not the win condition anymore.

For years, the narrative emphasized speed and adoption. Those measures still matter, but the field now needs evidence that withstands real operating conditions:

• Does the intervention improve outcomes that matter to patients and care teams?
• Does it reduce burden or shift it to clinicians, nurses, caregivers, or patients?
• Does it generalize beyond a motivated pilot site?
• What are the failure modes, and how were they detected and mitigated?
• What governance, safety monitoring, and oversight were required, especially for AI?
• Who benefited, who was excluded, and why?

When answers remain unpublished, the result is duplicated effort and slow learning. Pragmatic evaluations are not second-class evidence, and there are established expectations for transparent reporting.¹

Enterprise Research Is Often Already Happening

Many teams say, “We are not doing research.” In practice, enterprise work becomes publishable when teams deploy an intervention, measure what changed, describe context and methods clearly, and state limitations honestly. This is called applied science and implementation maturity.

• Micro Case Vignette: Operational Evidence That Stayed Invisible

During the peak of the COVID-19 pandemic, I supported an investigator-initiated Phase III clinical trial at an academic cardiovascular research center evaluating a repurposed anti-inflammatory therapy. In my enterprise role, I led the concept, scope, and user experience design for a custom eConsent solution built to function under severe time and operational constraints.

The solution moved from concept to deployment within weeks, faster than typical enterprise implementation cycles for eConsent capabilities at that time, and it was deployed in multiple languages to support broader participant access. A key friction point was that the feasibility and cycle-time advantage of this approach remained largely invisible outside the immediate project team and was not reflected in the trial’s scientific publications or readouts. The lesson is that enterprises routinely generate credible evidence about decentralized and hybrid trial enablement, but when these operational innovations are not published, the field loses reusable knowledge and repeats avoidable learning curves.

Six publishable archetypes enterprises can recognize are as follows:

1. 1) Implementation and workflow evaluation: how the intervention fits into operations, where friction emerges, and what changes were required

2. 2) Real-world outcomes evaluation: pragmatic analyses linking deployment to clinical, operational, experience, safety, and equity outcomes

3. 3) Protocol and operating model papers: reproducible playbooks for virtual care, remote monitoring, hybrid trials, and eConsent-enabled enrollment pathways

4. 4) Governance and risk management case studies (including AI): oversight, monitoring, drift and bias management, auditability, escalation, and human factors

5. 5) Data integration and interoperability lessons learned: standards, transformation approaches, data quality constraints, and failure modes

6. 6) Equity, access, and inclusion evaluations: evidence on who benefits, who faces barriers, and what mitigations change the distribution of outcomes

The minimum publishable evidence threshold, a practical minimum bar often includes:

• Clear context and population
• Reproducible intervention description and workflow
• Baseline or comparator, even if pragmatic
• Outcomes that matter
• Limitations and potential sources of bias
• Governance framing, privacy approach, and conflict-of- interest transparency

“Messy” evidence can still be valuable when it is transparent and responsibly interpreted. However, although constraints are real, publication is still feasible.

Enterprises face legal review cycles, Intellectual Property (IP) constraints, data rights, and reputational risk. Publication is still feasible when teams:

• De-identify and aggregate where possible
• Publish methods and workflow when outcomes are sensitive
• Focus on lessons learned and decision rationale rather than commercial terms
• Describe oversight and what could not be measured and why

Publication is a disciplined spectrum of transparency. Responsible narratives address feasibility, resources, equity, and unintended consequences.²

Why THMT Is a Pragmatic Home for Enterprise Evidence

The industry needs venues that welcome applied evidence not only idealized studies. Publishing converts local experience into shared knowledge that can influence practice, accelerate responsible adoption, and strengthen expectations for governance and safety. A peer-reviewed record rewards clarity, measured outcomes, and honest limitations.

Conclusions

Digital health is now infrastructure, and infrastructure demands evidence. Enterprises already generate publishable knowledge through implementation evaluation, pragmatic outcomes analysis, protocol development, governance and oversight work, interoperability lessons, and equity assessment. There is clear opportunity to disseminate internal learning into the published record, enabling the field to evolve faster, scale more safely, and strengthen trust.

Practical Next Steps for Enterprise Teams

• Select one deployed initiative with measured outcomes, even if mixed
• Choose the manuscript archetype that best fits the work
• Draft a structured abstract and plain language summary
• Align early on what can be shared (de-identified, aggregated, workflow-focused)
• Submit with transparent disclosures, including AI use if applicable

Data Availability Statement (DAS), Data Sharing, Reproducibility, and Data Repositories

No data were generated or analyzed in the presented work.

Application of AI-Generated Text or Related Technology

• Tool(s): ChatGPT 5.4 OpenAI
• Purpose: Drafting and language editing of a perspective manuscript (no generation of data, analyses, or figures)
• Date(s) of use: March 6, 2026
• Methods used to verify AI generated content: Author performed personal review, independent fact checking of claims, and ensured no fabricated citations or references were introduced.
• Human oversight and responsibility: Author retains full responsibility for the integrity, accuracy, and originality of the submission.
• Bias and ethical considerations: No sensitive data were entered into the AI tool. The author considered the risk of bias or overgeneralization in AI-assisted text and revised content accordingly.

Contributions

The author is responsible for all aspects in the development of this editorial.

References

1. Zwarenstein M, Treweek S, Gagnier JJ, Altman DG, Tunis S, Haynes B, et al. Improving the reporting of pragmatic trials: an extension of the CONSORT statement. BMJ. 2008;337:a2390. https://doi.org/10.1136/bmj.a2390
2. World Health Organization. WHO guideline: recommendations on digital interventions for health system strengthening. Geneva: World Health Organization; 2019.

Copyright Ownership: This is an open-access article distributed in accordance with the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0. The authors of this article own the copyright.