PCaGuard: A Software Platform to Support Optimal Management of Prostate Cancer

Provider Perceptions of an Electronic Health Record Prostate Cancer Screening Tool

OBJECTIVES

 We conducted a focus group to assess the attitudes of primary care physicians (PCPs) toward prostate-specific antigen (PSA)-screening algorithms, perceptions of using decision support tools, and features that would make such tools feasible to implement.

METHODS

 A multidisciplinary team (primary care, urology, behavioral sciences, bioinformatics) developed the decision support tool that was presented to a focus group of 10 PCPs who also filled out a survey. Notes and audio-recorded transcripts were analyzed using Thematic Content Analysis.

RESULTS

 The survey showed that PCPs followed different guidelines. In total, 7/10 PCPs agreed that engaging in shared decision-making about PSA screening was burdensome. The majority (9/10) had never used a decision aid for PSA screening. Although 70% of PCPs felt confident about their ability to discuss PSA screening, 90% still felt a need for a provider-facing platform to assist in these discussions. Three major themes emerged: (1) confirmatory reactions regarding the importance, innovation, and unmet need for a decision support tool embedded in the electronic health record; (2) issues around implementation and application of the tool in clinic workflow and PCPs' own clinical bias; and (3) attitudes/reflections regarding discrepant recommendations from various guideline groups that cause confusion.

CONCLUSION

 There was overwhelmingly positive support for the need for a provider-facing decision support tool to assist with PSA-screening decisions in the primary care setting. PCPs appreciated that the tool would allow flexibility for clinical judgment and documentation of shared decision-making. Incorporation of suggestions from this focus group into a second version of the tool will be used in subsequent pilot testing.

Clinical Significance of Action Research-Based Seamless Care to Improve Imaging Efficiency and Patients' Cognition, and Alleviate Patient Anxiety

Objective

The present study was undertaken to assess the clinical significance of action research-based seamless care to improve imaging efficiency and alleviate patient anxiety.

Methods

A total of eighty patients who underwent imaging examinations in our hospital between May 2019 and November 2020 were recruited for this study. The patients were randomly assigned to two groups: the control group receiving routine care and the observation group receiving seamless care based on action research. The random assignment was conducted using a simple random sampling technique, ensuring an equal allocation of participants to each group at a 1:1 ratio, resulting in 40 cases in each group. Outcome measures included imaging examination duration, mean nursing duration, examination cognition, and negative emotion scores.

Results

Seamless care provided shorter imaging examination duration and nursing duration, and better ensured uneventful examinations than routine care (P<0.05). Patients given seamless care exhibited higher examination cognition versus those receiving routine care (P<0.05). Seamless care offered more mitigation of negative emotions for patients than routine care (P<0.05).

Conclusion

Action research-based seamless care effectively improves imaging efficiency and patients' awareness of imaging examinations and contributes to alleviating patients' adverse events.

Lifespan and medical expenditure prognosis for cancer metastasis - a simulation modeling using semi-Markov process

BACKGROUND AND OBJECTIVE

A key reason of high mortality of cancers is attributed to the metastasized cancer, whereas, the medical expense for the treatment of cancer metastases generates heavily financial burden. The population size of metastases cases is small and comprehensive inferencing and prognosis is hard to conduct.

METHODS

Because metastases and finance state can develop dynamic transitions over time, this study proposes a semi-Markov model to perform risk and economic evaluation associated to major cancer metastasis (i.e., lung, brain, liver and lymphoma cancer) against rare cases. A nationwide medical database in Taiwan was employed to derive a baseline study population and costs data. The time until development of metastasis and survivability from metastasis, as well as the medical costs were estimated through a semi-Markov based Monte Carlo simulation.

RESULTS

In terms of the survivability and risk associated to metastatic cancer patients, 80% lung and liver cancer cases are tended to metastasize to other part of the body. The highest cost is generated by brain cancer-liver metastasis patients. The survivors group generated approximately 5 times more costs, in average, than the non-survivors group.

CONCLUSIONS

The proposed model provides a healthcare decision-support tool to evaluate the survivability and expenditure of major cancer metastases.

Harrison M, Swiger J, A. Lomotan E, Dymek C, Dullabh P. Lessons Learned from a National Initiative Promoting Publicly Available Standards-Based Clinical Decision Support

BACKGROUND

Clinical decision support (CDS), which provides tools to assist clinical decision-making, can improve adherence to evidence-based practices, prevent medical errors, and support high-quality and patient-centered care delivery. Publicly available CDS that uses standards to express clinical logic (i.e., standards-based CDS) has the potential to reduce duplicative efforts of translating the same clinical evidence into CDS across multiple health care institutions. Yet development of such CDS is relatively new and its potential only partially explored.

OBJECTIVES

This study aimed to describe lessons learned from a national initiative promoting publicly available, standards-based CDS resources, discuss challenges, and report suggestions for improvement.

METHODS

Findings were drawn from an evaluation of the Agency for Healthcare Research and Quality Patient-Centered Outcomes Research CDS Initiative, which aimed to advance evidence into practice through standards-based and publicly available CDS. Methods included literature and program material reviews, key informant interviews, and a web-based survey about a public repository of CDS artifacts and tools for authoring standards-based CDS.

RESULTS

The evaluation identified important lessons for developing and implementing standards-based CDS through publicly available repositories such as CDS Connect. Trust is a critical factor in uptake and can be bolstered through transparent information on underlying evidence, collaboration with experts, and feedback loops between users and developers to support continuous improvement. Additionally, while adoption of standards among electronic health record developers will make it easier to implement standards-based CDS, lower-resourced health systems will need extra support to ensure successful implementation and use. Finally, although we found the resources developed by the Initiative to offer valuable prototypes for the field, health systems desire more information about patient-centered, clinical, and cost-related outcomes to help them justify the investment required to implement standards-based, publicly available CDS.

CONCLUSION

While the standards and technology to publicly share standards-based CDS have increased, broad dissemination and implementation remain challenging.

Uptake of a Cervical Cancer Clinical Decision Support Tool: A Mixed-Methods Study

OBJECTIVES

Clinical decision support (CDS) tools that provide point-of-care reminders of patients' care needs may improve rates of guideline-concordant cervical cancer screening. However, uptake of such electronic health record (EHR)-based tools in primary care practices is often low. This study describes the frequency of factors associated with, and barriers and facilitators to adoption of a cervical cancer screening CDS tool (CC-tool) implemented in a network of community health centers.

METHODS

This mixed-methods sequential explanatory study reports on CC-tool use among 480 community-based clinics, located across 18 states. Adoption of the CC-tool was measured as any instance of tool use (i.e., entry of cervical cancer screening results or follow-up plan) and as monthly tool use rates from November 1, 2018 (tool release date) to December 31, 2020. Adjusted odds and rates of tool use were evaluated using logistic and negative-binomial regression. Feedback from nine clinic staff representing six clinics during user-centered design sessions and semi-structured interviews with eight clinic staff from two additional clinics were conducted to assess barriers and facilitators to tool adoption.

RESULTS

The CC-tool was used ≥1 time in 41% of study clinics during the analysis period. Clinics that ever used the tool and those with greater monthly tool use had, on average, more encounters, more patients from households at >138% federal poverty level, fewer pediatric encounters, higher up-to-date cervical cancer screening rates, and higher rates of abnormal cervical cancer screening results. Qualitative data indicated barriers to tool adoption, including lack of knowledge of the tool's existence, understanding of its functionalities, and training on its use.

CONCLUSION

Without effective systems for informing users about new EHR functions, new or updated EHR tools are unlikely to be widely adopted, reducing their potential to improve health care quality and outcomes.

Realizing the potential of artificial intelligence in healthcare: Learning from intervention, innovation, implementation and improvement sciences

Introduction

Artificial intelligence (AI) is widely seen as critical for tackling fundamental challenges faced by health systems. However, research is scant on the factors that influence the implementation and routine use of AI in healthcare, how AI may interact with the context in which it is implemented, and how it can contribute to wider health system goals. We propose that AI development can benefit from knowledge generated in four scientific fields: intervention, innovation, implementation and improvement sciences.

Aim

The aim of this paper is to briefly describe the four fields and to identify potentially relevant knowledge from these fields that can be utilized for understanding and/or facilitating the use of AI in healthcare. The paper is based on the authors' experience and expertise in intervention, innovation, implementation, and improvement sciences, and a selective literature review.

Utilizing knowledge from the four fields

The four fields have generated a wealth of often-overlapping knowledge, some of which we propose has considerable relevance for understanding and/or facilitating the use of AI in healthcare.

Conclusion

Knowledge derived from intervention, innovation, implementation, and improvement sciences provides a head start for research on the use of AI in healthcare, yet the extent to which this knowledge can be repurposed in AI studies cannot be taken for granted. Thus, when taking advantage of insights in the four fields, it is important to also be explorative and use inductive research approaches to generate knowledge that can contribute toward realizing the potential of AI in healthcare.

Initial experience with AI Pathway Companion: Evaluation of dashboard-enhanced clinical decision making in prostate cancer screening

Purpose

Rising complexity of patients and the consideration of heterogeneous information from various IT systems challenge the decision-making process of urological oncologists. Siemens AI Pathway Companion is a decision support tool that provides physicians with comprehensive patient information from various systems. In the present study, we examined the impact of providing organized patient information in comprehensive dashboards on information quality, effectiveness, and satisfaction of physicians in the clinical decision-making process.

Methods

Ten urologists in our department performed the entire diagnostic workup to treatment decision for 10 patients in the prostate cancer screening setting. Expenditure of time, information quality, and user satisfaction during the decision-making process with AI Pathway Companion were recorded and compared to the current workflow.

Results

A significant reduction in the physician’s expenditure of time for the decision-making process by -59.9% (p < 0,001) was found using the software. System usage showed a high positive effect on evaluated information quality parameters completeness (Cohen’s d of 2.36), format (6.15), understandability (2.64), as well as user satisfaction (4.94).

Conclusion

The software demonstrated that comprehensive organization of information improves physician’s effectiveness and satisfaction in the clinical decision-making process. Further development is needed to map more complex patient pathways, such as the follow-up treatment of prostate cancer.

Artificial Intelligence Applications in Health Care Practice: A Scoping Review (Preprint)

Background

Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood.

Objective

The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible?

Methods

A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized.

Results

Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare.

Conclusions

Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.