Cautious Optimism Building: What HIE Managers Think About Adding Artificial Intelligence to Improve Patient Matching

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you for sharing this fascinating research expressing the 'Cautious AI Optimism' of HIE executives about incorporating AI use for patient matching in HIEs. Please consider the following comments.

Introduction
1. Although HIEs are now a pretty generally understood term, it can be helpful to add a simple, clear definition of HIE towards the beginning.

2. Although the interview quotes in the introduction and literature review are so vivid and seem well placed, as results of the study, they seem misplaced. Consider adding how it is discussed in the introduction to the discussion section.

3. Another challenge of patient matching using AI is the lack of available information, for example, one organization may not share all the information available or only certain parts of a patient record (opt-in/out).

Literature Review

4. Excellent historical review! This would make a great commentary as well.

5. Curious about the remaining 8 people not included in the final interviews. Why were they excluded?

Methods?

6. Sections 2.5, 2.6, and 2.7 should be methodology in a section on their own.

7. How many researchers participated in the protocol development, interviews, and analyses? Were they all part of the research or just helping with specific parts (i.e., analyses)?

8. How were differences/disagreements in coding solved?

Results

9. Great quote selection!

Author Response

Reviewer 1

Comments and Suggestions for Authors

Thank you for sharing this fascinating research expressing the 'Cautious AI Optimism' of HIE executives about incorporating AI use for patient matching in HIEs. Please consider the following comments.

• Thank you.

 

Introduction
1. Although HIEs are now a pretty generally understood term, it can be helpful to add a simple, clear definition of HIE towards the beginning.

• We added right at the beginning that “Health Information Exchanges (HIEs) are secure information systems that allow medical providers to share and access medical information immediately across providers, unifying a patient’s information across those providers in the standardized manner even when the information is stored and presented differently by each provider.”

2. Although the interview quotes in the introduction and literature review are so vivid and seem well placed, as results of the study, they seem misplaced. Consider adding how it is discussed in the introduction to the discussion section.

• We added to the opening section of the Discussion that “A key, rather surprising, discovery of this study is that while the interviewed HIE executives thought about incorporating AI into their HIE systems, and some even started testing it, none of them had actually started using AI in their HIE. The interviews reveal some of the reasons leading to that caution. The grounded theory section combines those insights into a model, shown in Figure 3. The model presents the AI forging pathways, comprising a perceived AI imperative but also challenges, and how those relate to patient matching pressures and values. What is also interesting in that model is that there was no mention of trust or distrust in the AI or the companies implementing it. That is surprising because in other contexts trust is an important antecedent of AI adoption (Benk et al., 2024), including in medical settings (Prakash and Das, 2021), and how it is used (Gefen et al., 2025). It is possible that because of the strong medical regulatory context of HIE and the oversight it entails that trust, as in the case of other IT where regulations are strong and enforced (e.g., Gefen and Pavlou (2012)), is of secondary importance considering the power of regulations and oversight to guarantee outcomes. More research on that is needed.”

 

3. Another challenge of patient matching using AI is the lack of available information, for example, one organization may not share all the information available or only certain parts of a patient record (opt-in/out).

• Thank you for adding that point. We have accordingly added to the paper that “Adding to the difficulties of patient matching is the problem of confusing patient consent models of opt-in / opt out across the HIE ecosystems which further underscores the complexities and challenges that affect HIE data flow and interoperability. Discrepancies in consent requirements across states also create confusion for both patients and providers, hindering effective data sharing. Providers often struggle to explain these models to patients, adding to the uncertainty and complicating informed decision-making.”

 

Literature Review

4. Excellent historical review! This would make a great commentary as well.
5. Curious about the remaining 8 people not included in the final interviews. Why were they excluded?

• We added that “Up to 50 interviews were initially approved by the IRB, 35 HIE experts were prescreened, and 27 accepted and completed the interview process to form the primary data source. Data saturation was indeed reached at 27 informants as no new information or themes emerged from the data. And in total in the USA there are only about 120 HIE systems with some states having multiple HIEs. Based on Charmaz (2014), we stopped collecting new interviews at that stage. In the paper we present representative quotes. Quotes that repeat themes brough by other interviewees were excluded in the interest of being succinct and avoiding duplication. To provide context, the HIE community is not that large, only 48 states run HIEs.”

 

Methods?

6. Sections 2.5, 2.6, and 2.7 should be methodology in a section on their own.

• That you for the suggestion. Done.

 

7. How many researchers participated in the protocol development, interviews, and analyses? Were they all part of the research or just helping with specific parts (i.e., analyses)?

• We added that “All the interviews were conducted by the lead author. The interview transcripts were analyzed and coded by the lead author, an HIE executive and subject matter expert, advised and overseen by the co-authors. There were but a few disagreements and those were clarified by the lead author explaining in more detail what he did and why.”

 

8. How were differences/disagreements in coding solved?

• We added that “There were but a few disagreements and those were clarified by the lead author explaining in more detail what he did and why.”

 

 

Results

9. Great quote selection!

• Thank you.

 

Reviewer 2 Report

Comments and Suggestions for Authors

Summary:

The study reveals the factors shaping patient matching and AI adoption in Health Information Exchange (HIE) systems via interviews, emphasizing a cautious but growing optimism toward AI. While AI offers potential to improve precision and efficiency, its adoption is tempered by ethical, technical, and resource challenges. Rather than a quick fix, AI is seen as a powerful tool that requires careful, strategic integration to enhance patient safety and healthcare interoperability.

Comments:

Please provide the IRB approval details.
Additionally, please describe the snowball sampling strategy used in your study and explain the steps taken to minimize selection bias associated with this sampling method.

Author Response

Reviewer 2

Open Review

Comments and Suggestions for Authors

Summary:

The study reveals the factors shaping patient matching and AI adoption in Health Information Exchange (HIE) systems via interviews, emphasizing a cautious but growing optimism toward AI. While AI offers potential to improve precision and efficiency, its adoption is tempered by ethical, technical, and resource challenges. Rather than a quick fix, AI is seen as a powerful tool that requires careful, strategic integration to enhance patient safety and healthcare interoperability.

• Thank you.

 

Comments:

Please provide the IRB approval details.
Additionally, please describe the snowball sampling strategy used in your study and explain the steps taken to minimize selection bias associated with this sampling method.

• We added that “The semi-structured interview protocol received Drexel University Institutional Review Board (IRB) approval #2402010358 in February of 2024.”
• And that “The informants were all senior HIE executives. Starting with New York State, we identified the informants through the professional connections of the lead author, himself a senior HIE executive in New York, and using a snowball sampling approach, requested those informants to put the lead author in contact with other HIE executives including those also in other US states. This was deemed necessary because we did not expect HIE executives to be easily accessible. Rather, as it turned out to be the case, a referral from another HIE executive they knew became a necessary prerequisite to ensure participation in our study.”

Reviewer 3 Report

Comments and Suggestions for Authors

This qualitative study explores how Health Information Exchange (HIE) leaders perceive the potential of Artificial Intelligence (AI) in improving patient matching processes. The number of interviews is really lacking and insufficient. The authors identify a framework of "Cautious AI Optimism," revealing that while HIE leaders are hopeful about AI's promise, they remain wary due to concerns over explainability, bias, cost, and implementation feasibility. The study outlines perceived AI benefits like improved precision and efficiency, but also details serious challenges such as lack of technical competency, ethical risks, and resource constraints. Overall, the paper seeks to contribute to literature on digital health, decision-making, and innovation adoption in healthcare but I am concerned that there is limited contributions/advancements and recommend to reject the work.

(1) While the paper identifies tensions and proposes a framework, it lacks a clear articulation of how this advances or challenges existing theories in health informatics or innovation adoption.

(2)A more structured recruitment strategy or justification is needed. The sample size is also too small.

(3) The paper should include comparative analysis—e.g., views of those who have implemented AI tools vs. those who have not—to strengthen the robustness of conclusions.

For the reasons above, I believe the work does not meet the bar for publication in its current form. The contribution is largely descriptive, and the theoretical novelty is limited. Moreover, methodological limitations (e.g., sample bias, lack of triangulation) weaken its empirical credibility. 

Comments on the Quality of English Language

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Author Response

Reviewer 3

Comments and Suggestions for Authors

This qualitative study explores how Health Information Exchange (HIE) leaders perceive the potential of Artificial Intelligence (AI) in improving patient matching processes. The number of interviews is really lacking and insufficient.

• To clarify the point, we added that “Up to 50 interviews were initially approved by the IRB, 35 HIE experts were prescreened, and 27 accepted and completed the interview process to form the primary data source. Data saturation was indeed reached at 27 informants as no new information or themes emerged from the data. Based on Charmaz (2014), we stopped collecting new interviews at that stage. In the paper we present representative quotes. Quotes that repeat themes brough by other interviewees were excluded in the interest of being succinct and avoiding duplication. To provide context, the HIE community is not that large, only 48 states run HIEs. And in total in the USA there are only about 120 HIE systems with some states having multiple HIEs.”

 

 

The authors identify a framework of "Cautious AI Optimism," revealing that while HIE leaders are hopeful about AI's promise, they remain wary due to concerns over explainability, bias, cost, and implementation feasibility. The study outlines perceived AI benefits like improved precision and efficiency, but also details serious challenges such as lack of technical competency, ethical risks, and resource constraints. Overall, the paper seeks to contribute to literature on digital health, decision-making, and innovation adoption in healthcare but I am concerned that there is limited contributions/advancements and recommend to reject the work.

• The paper correctly reflects the opinions expressed by the interviewees. Their caution and realization of the risks are one of the key contributions.

 

 

(1) While the paper identifies tensions and proposes a framework, it lacks a clear articulation of how this advances or challenges existing theories in health informatics or innovation adoption.

• Placing the model developed through the interviews in the context of previous research on AI adoption, we added this to the Discussion. “The grounded theory section combines those insights into a model, shown in Figure 3. The model presents the AI forging pathways, comprising a perceived AI imperative but also challenges, and how those relate to patient matching pressures and values. What is also interesting in that model is that there was no mention of trust or distrust in the AI or the companies implementing it. That is surprising because in other contexts trust is an important antecedent of AI adoption (Benk et al., 2024), including in medical settings (Prakash and Das, 2021), and how it is used (Gefen et al., 2025). It is possible that because of the strong medical regulatory context of HIE and the oversight it entails that trust, as in the case of other IT where regulations are strong and enforced (e.g., Gefen and Pavlou (2012)), is of secondary importance considering the power of regulations and oversight to guarantee outcomes. More research on that is needed.”

 

(2)A more structured recruitment strategy or justification is needed. The sample size is also too small.

• Addressing the process and sample size, we added that “Up to 50 interviews were initially approved by the IRB, 35 HIE experts were prescreened, and 27 accepted and completed the interview process to form the primary data source. Data saturation was indeed reached at 27 informants as no new information or themes emerged from the data. Based on Charmaz (2014), we stopped collecting new interviews at that stage. In the paper we present representative quotes. Quotes that repeat themes brough by other interviewees were excluded in the interest of being succinct and avoiding duplication. To provide context, the HIE community is not that large, only 48 states run HIEs. And in total in the USA there are only about 120 HIE systems with some states having multiple HIEs.”
• We also added that “This snowball sampling method facilitated access to numerous HIE industries leads – necessary as there is no existing listing of those people. Once an informant completed the interview, we asked them to make introductions to additional participants in their professional circle. Those recommended were then pre-screened to ensure they had the applicable HIE ecosystem experience. The selected interviewees included HIE leaders, government policymakers, health technology experts, data scientists, payors, and healthcare providers. This diverse representation ensured a well-rounded view of the patient-matching landscape. All participants held positions ranging from Director of Operations to CEO, including some MDs who have transitioned into informatics roles and vendors and consultants. This pool covered experiences across 30 of the 48 contiguous US states that utilize HIE.”

 

 

(3) The paper should include comparative analysis—e.g., views of those who have implemented AI tools vs. those who have not—to strengthen the robustness of conclusions.

• The truth is that all the HIE managers that we interviewed indicated that AI is not yet ready for implementation. To address your point, we added in the Limitations section that “We initially considered running a comparative analysis to learn what led some HIE managers to adopt AI and others to wait. Being able to make such comparisons could have shed light on the risks and values of adding AI to HIE. However, as it turned out, all the interviewees told us that their organization had not yet implemented AI. They were all thinking, talking, testing, but had not yet transitioned to deployment of AI-based tools. Future studies can track the deployment processes that will inevitably occur and AI-based technologies mature and use cases become more prevalent.”

 

For the reasons above, I believe the work does not meet the bar for publication in its current form. The contribution is largely descriptive, and the theoretical novelty is limited. Moreover, methodological limitations (e.g., sample bias, lack of triangulation) weaken its empirical credibility. 

 

• On the theoretical contribution, please refer to our answer to your bullet #1.
• We added in the new Limitations section that “The study analyzed the interview transcripts of 27 senior HIE executives, combined with the notes the lead author took during those interviews. The number of interviewees might seem small in comparison with survey research, however, it is sufficient in qualitative interview research according to Charmaz (2014) whose methodology we applied, especially when the themes being raised by the interviewees converge and stop revealing new themes. There are also only so many senior HIE executives that could be interviewed, and, knowing their busy schedule, that we managed to interview 27 of those is rather outstanding. Nonetheless, we acknowledge the value of interviewing more senior HIE executives, especially outside the US, where regulations allowed a better integration of HIE systems across providers.”
• As said above we also added that “We initially considered running a comparative analysis to learn what led some HIE managers to adopt AI and others to wait. Being able to make such comparisons could have shed light on the risks and values of adding AI to HIE. However, as it turned out, all the interviewees told us that their organization had not yet implemented AI. They were all thinking, talking, testing, but had not yet transitioned to deployment of AI-based tools. Future studies can track the deployment processes that will inevitably occur and AI-based technologies mature and use cases become more prevalent.”

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

After carefully reviewing the updated manuscript, I am afraid that my earlier comments and concerns still hold. While I appreciate the effort made to address the previous feedback, the manuscript does not yet demonstrate sufficient research value to meet the standards required for publicatio.

Comments on the Quality of English Language

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