Understanding the Role and Challenges of Patient Preferences in Disparities in Rheumatologic Disease Care

Health Disparities in Rheumatology in the United States

Objective

Underserved populations are often at risk of experiencing systematic healthcare disparities. Existing disparities in care access, quality of care received, and treatment outcomes among patients with rheumatic disease are not well understood.

Methods

We conducted a targeted literature review to understand disparities in health outcomes, treatment patterns, and healthcare management faced by rheumatology patients in the United States, with a focus on rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS).

Results

The findings of this review indicate that disparities in RA, PsA, and AS affect several historically underserved populations, including underrepresented racial and ethnic groups, persons with lower socioeconomic status (SES), persons experiencing homelessness, and patients with Medicare or Medicaid insurance types. The disparities experienced by these populations include greater disease activity and severity, decreased or delayed access to specialist care, decreased likelihood of receiving advanced therapeutics, and worse clinical outcomes.

Conclusion

To provide equitable healthcare for all patients with RA, PsA, and AS, multiple closely linked health disparities must be addressed. Possible solutions include partnerships between healthcare systems and community-based organizations, targeted outreach tailored to patients with low SES, interventions to improve patient adherence and knowledge, and interventions to improve access to care for rural-residing and unhoused patients. In all, the findings of this literature review underscore the need for mitigation of health disparities in rheumatology care and may serve as a foundation for developing strategies to reduce disparities.

Shared decision-making and detection of comorbidities in an online acromegaly consultation with and without the Acromegaly Disease Activity Tool ACRODAT® using the simulated person approach

OBJECTIVE

A patient-centered approach to the management of acromegaly includes disease activity control, shared decision-making and identification of comorbidities. The Acromegaly Disease Activity Tool (ACRODAT®) is intended to assist physicians in providing such holistic management. The present study investigated this claim using the simulated person (SP) approach.

METHODS

We studied patient-doctor interaction via online video consultation in a randomized prospective study design with SPs trained to simulate a specific acromegaly profile. We analyzed the proportion of conversation time devoted to health content and the specific acromegaly and comorbidity relevant categories mentioned in the conversation. We collected physicians' feedback on the usefulness of ACRODAT®, SPs subjective perception of the quality of the conversation and compared consultations with and without ACRODAT® using a qualitative approach.

RESULTS

The sample (N = 30) consisted of endocrinologists treating patients with acromegaly in Germany. For SP-physician interactions (N = 60), the proportion of time spent on conversation content (e.g. IGF-I, quality of life) was distributed according to the focus of the patient profile. Comorbidities were less well identified than the need for a change in therapy. Only 18.3% of the SPs were actively asked to participate in the decision-making process. ACRODAT® did not lead to any significant differences in the course of the discussion.

CONCLUSIONS

Shared decision-making was underrepresented in this SP-physician interaction in acromegaly management. Physicians adapted the content of the discussion to the SP's needs, but did not adequately address comorbidities. According to the analysis criteria used, ACRODAT® did not contribute to a more holistic patient management in the present study.

Development of a Machine Learning Model to Predict the Use of Surgery in Patients With Rheumatoid Arthritis

OBJECTIVE

One in five patients with rheumatoid arthritis (RA) rely on surgery to restore joint function. However, variable response to disease-modifying antirheumatic drugs (DMARDs) complicates surgical planning, and it is difficult to predict which patients may ultimately require surgery. We used machine learning to develop predictive models for the likelihood of undergoing an operation related to RA and which type of operation patients who require surgery undergo.

METHODS

We used electronic health record data to train two extreme gradient boosting machine learning models. The first model predicted patients' probabilities of undergoing surgery ≥5 years after their initial clinic visit. The second model predicted whether patients who underwent surgery would undergo a major joint replacement versus a less intensive procedure. Predictors included demographics, comorbidities, and medication data. The primary outcome was model discrimination, measured by area under the receiver operating characteristic curve (AUC).

RESULTS

We identified 5,481 patients, of whom 278 (5.1%) underwent surgery. There was no significant difference in the frequency of DMARD or steroid prescriptions between patients who did and did not have surgery, though nonsteroidal anti-inflammatory drug prescriptions were more common among patients who did have surgery (P = 0.03). The model predicting use of surgery had an AUC of 0.90 ± 0.02. The model predicting type of surgery had an AUC of 0.58 ± 0.10.

CONCLUSIONS

Predictive models using clinical data have the potential to facilitate identification of patients who may undergo rheumatoid-related surgery, but not what type of procedure they will need. Integrating similar models into practice has the potential to improve surgical planning.