Advanced decision‐making using patient‐reported outcome measures in total joint replacement

Jayakumar, Prakash; Bozic, Kevin J.

doi:10.1002/jor.24614

Cited by 40 publications

(39 citation statements)

References 58 publications

(118 reference statements)

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“…Another study found that presurgical pain was not related to post-surgical pain [26] indicating that more factors should be involved in the decision for surgical intervention. Most recently, shared decision-making has been studied to shift the focus away from clinical assessment and incorporate patient-reported outcomes measures such as pain, function, and quality of life in the determination of the appropriate timing of surgery [27]. This study did not assess the proportion of patients who were contemplating surgery though not yet planned or the portion that accept the physician's recommendation for surgery.…”

Section: Discussionmentioning

confidence: 99%

Pain severity and healthcare resource utilization in patients with osteoarthritis in the United States

Nalamachu¹,

Robinson

Viktrup

et al. 2020

Postgraduate Medicine

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Objective: To evaluate healthcare resource utilization (HCRU) by osteoarthritis (OA) pain severity. Methods: Cross-sectional surveys of US physicians and their patients were conducted between February and May 2017. Using the Numeric Rating Scale, patients were classified by self-reported pain intensity in the last week into mild (0-3), moderate (4-6), and severe (7-10) cohorts. Parameters assessed included clinical characteristics, HCRU, and current caregiver support. Descriptive statistics were obtained, and analysis of variance and chi-square tests were performed. Results: Patients (n = 841) were mostly female (60.9%) and white (77.8%), with mean age of 64.6 years. Patients reported mild (45.4%), moderate (35.9%), and severe (18.7%) OA pain. Mean number of affected joints varied by pain severity (range mild: 2.7 to severe: 3.6; p < 0.0001). Pain severity was associated with an increased number of physician-reported and patient-reported overall healthcare provider visits (HCPs; both p < 0.001). As pain increased, patients reported an increased need for mobility aids, accessibility modifications to homes, and help with daily activities due to functional disability. The number of imaging tests used to diagnose OA was similar across pain severity but varied when used for monitoring (X-rays: p < 0.0001; computerized tomography scans: p < 0.0447). Hospitalization rates for OA were low but were significantly associated with pain severity (mild: 4.9%; severe: 11.5%). Emergency department visits were infrequent but increasing pain severity was associated with more prior and planned surgeries. Conclusion: Greater current pain was associated with more prior HCRU including imaging for monitoring progression, HCP visits including more specialty care, hospitalizations, surgery/planned surgery, and loss of independence due to functional disability. Yet rates of hospitalizations and X-ray use were still sizable even among patients with mild pain. These cross-sectional findings warrant longitudinal assessment to further elucidate the impact of pain on HCRU.

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Section: Discussionmentioning

confidence: 99%

Pain severity and healthcare resource utilization in patients with osteoarthritis in the United States

Nalamachu¹,

Robinson

Viktrup

et al. 2020

Postgraduate Medicine

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“… 20 , 37 Promising work in artificial intelligence can enhance decision making with generated personalized predictions using prior patient reported outcome measures, patient clinical risk factors, and psychosocial risk factors (depression, patient activation). 38 - 40 Personalized predictions provide an additional metric to engage patients, and guide discussions about surgical appropriateness and postoperative expectations. Importantly, engagement methods focused on decision making can be introduced into the clinical setting without impacting efficiency of the office visits, which benefits all stakeholders.…”

Section: Discussionmentioning

confidence: 99%

Patient Engagement Approaches in Total Joint Arthroplasty: A Review of Two Decades

Alokozai

Bernstein

Samuel

et al. 2021

Journal of Patient Experience

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Patient engagement is a comprehensive approach to health care where the physician inspires confidence in the patient to be involved in their own care. Most research studies of patient engagement in total joint arthroplasty (TJA) have come in the past 5 years (2015-2020), with no reviews investigating the different patient engagement methods in TJA. The primary purpose of this review is to examine patient engagement methods in TJA. The search identified 31 studies aimed at patient engagement methods in TJA. Based on our review, the conclusions therein strongly suggest that patient engagement methods in TJA demonstrate benefits throughout care delivery through tools focused on promoting involvement in decision making and accessible care delivery (eg, virtual rehabilitation, remote monitoring). Future work should understand the influence of social determinants on patient involvement in care, and overall cost (or savings) of engagement methods to patients and society.

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“…Advanced algorithms offer an avenue to learn and adapt to different datasets including those relating to a patient's physical and psychosocial health and well-being from different populations and practices. Highly sophisticated analysis of a wide range of data is capable of generating impactful metrics that can be used to aid in better decision-making processes [73]. Jayakumar et al conducted a Purnomo et al Arthroplasty (2021) 3:37 randomized clinical trial of 129 patients with knee pain associated with OA.…”

Section: Clinical Decision Making and Future Directionsmentioning

confidence: 99%

Artificial intelligence in arthroplasty

2021

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Artificial intelligence (AI) is altering the world of medicine. Given the rapid advances in technology, computers are now able to learn and improve, imitating humanoid cognitive function. AI applications currently exist in various medical specialties, some of which are already in clinical use. This review presents the potential uses and limitations of AI in arthroplasty to provide a better understanding of the existing technology and future direction of this field.Recent literature demonstrates that the utilization of AI in the field of arthroplasty has the potential to improve patient care through better diagnosis, screening, planning, monitoring, and prediction. The implementation of AI technology will enable arthroplasty surgeons to provide patient-specific management in clinical decision making, preoperative health optimization, resource allocation, decision support, and early intervention. While this technology presents a variety of exciting opportunities, it also has several limitations and challenges that need to be overcome to ensure its safety and effectiveness.

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Advanced decision‐making using patient‐reported outcome measures in total joint replacement

Cited by 40 publications

References 58 publications

Pain severity and healthcare resource utilization in patients with osteoarthritis in the United States

Pain severity and healthcare resource utilization in patients with osteoarthritis in the United States

Patient Engagement Approaches in Total Joint Arthroplasty: A Review of Two Decades

Artificial intelligence in arthroplasty

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