A Practical Guide for Navigating the Design, Build, and Clinical Integration of Electronic Patient-Reported Outcomes in the Radiation Oncology Department

Philipson, Rebecca G.; Wu, Allan D.; Curtis, William C.; Jablonsky, D.; Hegde, John V.; McCloskey, Susan A.; Kaprealian, Tania; Steinberg, Michael L.; Kishan, Amar U.; Raldow, A.

doi:10.1016/j.prro.2020.12.007

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…Additionally, future work will use SPOKE to identify people that can undergo more intensive evaluation to estimate PD risk using clinical and biomarker assessments, such as smell test or imaging of striatal dopamine transporter binding (37). As EHR databases expand to include non-traditional information streams (e.g., sensor data (38), mobile health monitoring (39) and patient reported outcomes (40)), integration with an extensive biomedical knowledge network may not only improve the SPOKE model further, but also provide a crucial strategy to avoid overload (41) and facilitate clinical prediction, further enabling preventive healthcare.…”

Section: Discussionmentioning

confidence: 99%

Early detection of Parkinson’s disease through enriching the electronic health record using a biomedical knowledge graph

et al. 2023

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IntroductionEarly diagnosis of Parkinson’s disease (PD) is important to identify treatments to slow neurodegeneration. People who develop PD often have symptoms before the disease manifests and may be coded as diagnoses in the electronic health record (EHR).MethodsTo predict PD diagnosis, we embedded EHR data of patients onto a biomedical knowledge graph called Scalable Precision medicine Open Knowledge Engine (SPOKE) and created patient embedding vectors. We trained and validated a classifier using these vectors from 3,004 PD patients, restricting records to 1, 3, and 5 years before diagnosis, and 457,197 non-PD group.ResultsThe classifier predicted PD diagnosis with moderate accuracy (AUC = 0.77 ± 0.06, 0.74 ± 0.05, 0.72 ± 0.05 at 1, 3, and 5 years) and performed better than other benchmark methods. Nodes in the SPOKE graph, among cases, revealed novel associations, while SPOKE patient vectors revealed the basis for individual risk classification.DiscussionThe proposed method was able to explain the clinical predictions using the knowledge graph, thereby making the predictions clinically interpretable. Through enriching EHR data with biomedical associations, SPOKE may be a cost-efficient and personalized way to predict PD diagnosis years before its occurrence.

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Section: Discussionmentioning

confidence: 99%

Early detection of Parkinson’s disease through enriching the electronic health record using a biomedical knowledge graph

et al. 2023

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“…The choice of the most adequate measures for the population of interest provides a plan to minimize potential bias, improving sensitive analysis and clinical utility. The development of digital platforms, which are repositories of translated and validated general and specific instruments is essential for its excellent exposure and selection [ 22 , 25 , 26 , 77 , 119 ].…”

Section: Discussionmentioning

confidence: 99%

Patient reported outcomes in oncology: changing perspectives—a systematic review

Silveira

Sequeira

Gonçalves

et al. 2022

Health Qual Life Outcomes

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In public health context, oncology is associated with severe negative impact on patients and on their relatives’ quality of life. Over the last decades, survival has remained at 50% worldwide for some tumor locations. Patient reported outcomes (PROs) assessment and, the corresponding use in clinical practice, help establishing patient individualized profiling involving caregivers. The purpose of this systematic review was to examine critical success factors for PROs assessment in daily clinical oncology practice. Additionally, we investigated how PROs collection can change oncology perspectives for patients and caregivers. According to PRISMA guidelines, 83 studies were included in this systematic review, whether related with implementation in daily clinical practice or associated with its use in oncology. PROs assessment gathers multi-professional teams, biomedical and clinical expertise, patients, families and caregivers. Institutional involvement, first line for caregiver’s adherence, team continuous formation, encompassing training and support, design of clear workflows, continuous monitoring, and data analysis are crucial for implementation. PROs measures are decisive in oncology. Several items were improved, including caregiver–patient–physician communication, patient risk groups identification, unmet problems and needs detection, disease course and treatment tracking, prognostic markers, cost-effectiveness measurement and comfort/support provision for both patients and caregivers. Routine assessment and implementation of PROs in clinical practice are a major challenge and a paradigm transformation for future.

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“…Recently, a clinician- and patient-facing platform was designed for multiple sclerosis to serve as a personalized visual aid for understanding the disease course [ 10 ]. A framework has also been proposed for integrating questionnaires administered outside usual clinical workflow to supplement the information in the EHR [ 11 ]. However, no standard tool with these capabilities is available that is tailored to the complex issues that arise in PD.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Clinical Information Display to Improve Patient Encounters: Human-Centered Design and Evaluation of the Parkinson Disease-BRIDGE Platform

Brown¹,

Schleimer²,

Bledsoe³

et al. 2022

JMIR Hum Factors

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Background People with Parkinson disease (PD) have a variety of complex medical problems that require detailed review at each clinical encounter for appropriate management. Care of other complex conditions has benefited from digital health solutions that efficiently integrate disparate clinical information. Although various digital approaches have been developed for research and care in PD, no digital solution to personalize and improve communication in a clinical encounter is readily available. Objective We intend to improve the efficacy and efficiency of clinical encounters with people with PD through the development of a platform (PD-BRIDGE) with personalized clinical information from the electronic health record (EHR) and patient-reported outcome (PRO) data. Methods Using human-centered design (HCD) processes, we engaged clinician and patient stakeholders in developing PD-BRIDGE through three phases: an inspiration phase involving focus groups and discussions with people having PD, an ideation phase generating preliminary mock-ups for feedback, and an implementation phase testing the platform. To qualitatively evaluate the platform, movement disorders neurologists and people with PD were sent questionnaires asking about the technical validity, usability, and clinical relevance of PD-BRIDGE after their encounter. Results The HCD process led to a platform with 4 modules. Among these, 3 modules that pulled data from the EHR include a longitudinal module showing motor ratings over time, a display module showing the most recently collected clinical rating scales, and another display module showing relevant laboratory values and diagnoses; the fourth module displays motor symptom fluctuation based on an at-home diary. In the implementation phase, PD-BRIDGE was used in 17 clinical encounters for patients cared for by 1 of 11 movement disorders neurologists. Most patients felt that PD-BRIDGE facilitated communication with their clinician (n=14, 83%) and helped them understand their disease trajectory (n=11, 65%) and their clinician’s recommendations (n=11, 65%). Neurologists felt that PD-BRIDGE improved their ability to understand the patients’ disease course (n=13, 75% of encounters), supported clinical care recommendations (n=15, 87%), and helped them communicate with their patients (n=14, 81%). In terms of improvements, neurologists noted that data in PD-BRIDGE were not exhaustive in 62% (n=11) of the encounters. Conclusions Integrating clinically relevant information from EHR and PRO data into a visually efficient platform (PD-BRIDGE) can facilitate clinical encounters with people with PD. Developing new modules with more disparate information could improve these complex encounters even further.

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A Practical Guide for Navigating the Design, Build, and Clinical Integration of Electronic Patient-Reported Outcomes in the Radiation Oncology Department

Cited by 6 publications

References 21 publications

Early detection of Parkinson’s disease through enriching the electronic health record using a biomedical knowledge graph

Early detection of Parkinson’s disease through enriching the electronic health record using a biomedical knowledge graph

Patient reported outcomes in oncology: changing perspectives—a systematic review

Enhancing Clinical Information Display to Improve Patient Encounters: Human-Centered Design and Evaluation of the Parkinson Disease-BRIDGE Platform

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