Integration of Patient-reported Outcomes in a Total Joint Arthroplasty Program at a High-volume Academic Medical Center

Patient preferences as human factors for health data recommender systems and shared decision making in orthopaedic practice

Background

A core set of requirements for designing AI-based Health Recommender Systems (HRS) is a thorough understanding of human factors in a decision-making process. Patient preferences regarding treatment outcomes can be one important human factor. For orthopaedic medicine, limited communication may occur between a patient and a provider during the short duration of a clinical visit, limiting the opportunity for the patient to express treatment outcome preferences (TOP). This may occur despite patient preferences having a significant impact on achieving patient satisfaction, shared decision making and treatment success. Inclusion of patient preferences during patient intake and/or during the early phases of patient contact and information gathering can lead to better treatment recommendations.

Aim

We aim to explore patient treatment outcome preferences as significant human factors in treatment decision making in orthopedics. The goal of this research is to design, build, and test an app that collects baseline TOPs across orthopaedic outcomes and reports this information to providers during a clinical visit. This data may also be used to inform the design of HRSs for orthopaedic treatment decision making.

Methods

We created a mobile app to collect TOPs using a direct weighting (DW) technique. We used a mixed methods approach to pilot test the app with 23 first-time orthopaedic visit patients presenting with joint pain and/or function deficiency by presenting the app for utilization and conducting qualitative interviews and quantitative surveys post utilization.

Results

The study validated five core TOP domains, with most users dividing their 100-point DW allocation across 1-3 domains. The tool received moderate to high usability scores. Thematic analysis of patient interviews provides insights into TOPs that are important to patients, how they can be communicated effectively, and incorporated into a clinical visit with meaningful patient-provider communication that leads to shared decision making.

Conclusion

Patient TOPs may be important human factors to consider in determining treatment options that may be helpful for automating patient treatment recommendations. We conclude that inclusion of patient TOPs to inform the design of HRSs results in creating more robust patient treatment profiles in the EHR thus enhancing opportunities for treatment recommendations and future AI applications.

Facilitators and barriers to implementing electronic patient-reported outcome and experience measures in a health care setting: a systematic review

OBJECTIVE

This systematic literature review aimed to identify factors that influence the implementation of electronic patient-reported outcome measures (ePROMs) and patient-reported experience measures (ePREMs) in healthcare settings.

INTRODUCTION

Improvements in health care through increased patient engagement have gained traction in recent years. Patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) are tools used to improve the quality of care from the patient perspective. The influence of implementing PROMs and PREMs using electronic information systems (ePROMs and ePREMs) is not well understood.

INCLUSION CRITERIA

Studies with information related to the implementation of ePROMs and/or ePREMs with a focus on health-related services, irrespective of provider type, were included.

METHODS

A literature search of peer-reviewed databases was conducted on the 24th of January 2022 for articles about barriers and facilitators of the implementation of ePROMs/ePREMs in healthcare settings. Two reviewers independently extracted relevant findings from the included studies and performed a descriptive code-based synthesis before collaboratively creating a final consensus set of code categories, which were then mapped to the consolidated framework of implementation research (CFIR). Study quality was appraised using a mixed-methods appraisal tool (MMAT).

RESULTS

24 studies were eligible for inclusion in the screening of 626 nonduplicate studies. Quality assessment using the MMAT revealed that 20/24 studies met at least 60% of the MMAT criteria. Ninety-six code categories were identified and mapped to the constructs across all CFIR domains.

CONCLUSION

To guide the effective implementation of ePROMs/ePREMs in healthcare settings, factors shown to influence their implementation have been summarised as an implementation checklist for adoption and use by clinicians, organisations, and policymakers.

Implementation of an Online Glaucoma-Specific Quality of Life Computerized Adaptive Test System in a US Glaucoma Hospital

Purpose

The feasibility of implementing a computerized adaptive test (CAT) system in routine clinical care in ophthalmology has not been assessed. We evaluated the implementation of a glaucoma-specific CAT (GlauCAT) in outpatients at Massachusetts Eye and Ear Institute.

Methods

In this implementation study (July 2020–April 2021), 216 adults (mean ± SD age 64.8 ± 15.3 years; 56.0% women) completed six adaptive GlauCAT quality of life (QOL) tests on an internet-enabled tablet at the clinic. A real-time printable report summarizing domain scores was shared with physicians prior to consultation. The implementation was evaluated using Proctor's outcomes: acceptability (patient satisfaction); appropriateness (independent complete rate [%]); feasibility (acceptance rate [%]; completion time); and fidelity (percentage of patients discussing GlauCAT results with their physician). Physician barriers/facilitators were explored using open-ended questions.

Results

Patients’ mean ± SD satisfaction score was 3.5 ± 0.5 of 4, with >95% of patients willing to recommend it to others. Of the 216 (89.2%) patients accepting to participate, 173 (80%) completed GlauCAT independently. Patients took 8 minutes and 5 seconds (median) to complete all 6 GlauCAT tests. Almost two-thirds (n = 136/216) of the patients reported discussing their GlauCAT results with their doctor. Physicians described the GlauCAT summary report as helpful and user-friendly, although lack of time and uncertainty about how to action information were reported.

Conclusions

Pilot implementation of six GlauCAT QOL tests in glaucoma outpatient clinics was feasible and acceptable. Integration of GlauCAT with electronic medical records (EMRs) and evaluation of long-term implementation outcomes are needed.

Translational Relevance

GlauCAT's multiple outcomes and low test-taking burden makes it attractive for measuring glaucoma-specific QOL in routine clinical care.

Electronic health record-integrated approach for collection of patient-reported outcome measures: a retrospective evaluation

Background

The integration of Patient Reported Outcome Measures (PROMs) into clinical care presents many challenges for health systems. PROMs provide quantitative data regarding patient-reported health status. However, the most effective model for collecting PROMs has not been established. Therefore the purpose of this study is to report the development and preliminary evaluation of the standardized collection of PROMs within a department of orthopedic surgery at a large academic health center.

Methods

We utilized the Users’ Guide to Integrating Patient-Reported Outcomes in Electronic Health Records by Gensheimer et al., 2018 as a framework to describe the development of PROMs collection initiative. We framed our initiative by operationalizing the three aspects of PROM collection development: Planning, Selection, and Engagement. Next, we performed a preliminary evaluation of our initiative by assessing the response rate of patients completing PROMs (no. of PROMs completed/no. of PROMs administered) across the entire department (18 clinics), ambulatory clinics only (14 clinics), and hospital-based clinics only (4 clinics). Lastly, we reported on the mean response rates for the top 5 and bottom 5 orthopaedic providers to describe the variability across providers.

Results

We described the development of a fully-integrated, population health based implementation strategy leveraging the existing resources of our local EHR to maximize clinical utility of PROMs and routine collection. We collected a large volume of PROMs over a 13 month period (n = 10,951) across 18 clinical sites, 7 clinical specialties and over 100 providers. The response rates varied across the department, ranging from 29 to 42%, depending on active status for the portal to the electronic health record (MyChart). The highest single provider mean response rate was 52%, and the lowest provider rate was 13%. Rates were similar between hospital-based (26%) and ambulatory clinics (29%).

Conclusions

We found that our standardized PROMs collection initiative, informed by Gensheimer et al., achieved scope and scale, but faced challenges in achieving a high response rate commensurate with existing literature. However, most studies reported a targeted recruitment strategy within a narrow clinical population. Further research is needed to elucidate the trade-off between scalability and response rates in PROM collection initiatives.

Leveraging Patient Reported Outcomes Measurement via the Electronic Health Record to Connect Patients with Cancer to Smoking Cessation Treatment

Tobacco use negatively impacts cancer treatment outcomes, yet too few providers actively support their patients in quitting. Barriers to consistently addressing tobacco use and referring to treatment include time constraints and lack of knowledge surrounding treatment options. Patient Reported Outcomes (PRO) measurement is best practice in cancer care and has potential to help address these barriers to tobacco cessation treatment. This descriptive program evaluation study reports preliminary results following implementation of a novel automated PRO tobacco use screener and referral system via the electronic health record (EHR) patient portal (MyChart) that was developed and implemented as a part of a population-based tobacco treatment program at the Robert H. Lurie Comprehensive Cancer Center of Northwestern University. Between 25 June 2019 and 6 April 2020, 4589 unique patients completed the screener and 164 (3.6%) unique patients screened positive for recent (past month) cigarette smoking. All patients who screened positive were automatically referred to a smoking cessation treatment program integrated within the Lurie Cancer Center, and 71 (49.7%) patients engaged in treatment, as defined by completing at least one behavioral counseling session. Preliminary results indicate that the PRO/MyChart system may improve smoker identification and increase offering of treatment and, despite the "cold call" following a positive screen, may result in a treatment engagement rate that is higher than rates of treatment engagement previously documented in oncology settings. Longer term evaluation with formal statistical testing is needed before drawing conclusions regarding effectiveness, but PRO measurement via the EHR patient portal may serve a potentially important role in a multi-component approach to reaching and engaging cancer patients in comprehensive tobacco cessation treatment.