Comparison of an Artificial Intelligence–Enabled Patient Decision Aid vs Educational Material on Decision Quality, Shared Decision-Making, Patient Experience, and Functional Outcomes in Adults With Knee Osteoarthritis

Jayakumar, Prakash; Moore, Meredith G.; Furlough, Kenneth; Uhler, Lauren M.; Andrawis, John P.; Koenig, Karl; Aksan, Nazan; Rathouz, Paul J.; Bozic, Kevin J.

doi:10.1001/jamanetworkopen.2020.37107

Cited by 81 publications

(118 citation statements)

References 74 publications

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“… 34 35 Although we did not find greater utilisation of TKR for patients with knee osteoarthritis among practices with higher PES, a recent randomised controlled trial of SDM and decision aids in the context of management of knee osteoarthritis found that while share decision making implementation positively impacted patients’ experiences and decision quality, it had no impact on rates of TKR. 36 …”

Section: Discussionmentioning

confidence: 99%

Association of patient engagement strategies with utilisation and spending for musculoskeletal problems in the USA: a cross-sectional analysis of Medicare patients and physician practices

2021

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ObjectiveMusculoskeletal problems like hip and knee osteoarthritis and low-back pain are preference sensitive conditions. Patient engagement strategies (PES), such as shared decision-making and motivational interviewing, can help align patients’ preferences with treatment options and potentially reduce spending. We assess the association of physician practice-level adoption of PES with utilisation and spending.DesignCross-sectional study in which patients were matched across low, moderate and high levels of PES via coarsened exact matching.SettingPrimary and secondary care in 2190 physician practices.Participants39 336 hip, 48 362 knee and 67 940 low-back patients who were Medicare beneficiaries were matched to the 2017–2018 National Survey of Healthcare Organizations and Systems.Primary and secondary outcome measuresTotal hip replacement (THR), total knee replacement (TKR), 1–2 level posterior lumbar fusion (LF), total annual spending, components of total annual spending.ResultsTotal annual spending for patients with musculoskeletal problems did not differ for practices with low versus moderate PES, low versus high PES or moderate versus high PES, but spending was significantly lower in some categories for practices with relatively higher PES adoption. For hospital-owned and health system-owned practices, the ORs of receiving LF were 0.632 (95% CI 0.396 to 1.009) for patients attributed to practices with high PES compared with patients attributed to practices with moderate PES. For independent practices, the odds of receiving THR were 1.403 (95% CI 1.035 to 1.902) for patients attributed to practices with moderate PES compared with patients attributed to practices with low PES.ConclusionsPractice-level adoption of PES for patients with musculoskeletal problems was generally not associated with total spending. PES, however, may steer patients toward evidence-based treatments. Opportunities for overall spending reduction exist as indicated by the variation in the subcomponents of total spending by PES adoption.

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Section: Discussionmentioning

confidence: 99%

Association of patient engagement strategies with utilisation and spending for musculoskeletal problems in the USA: a cross-sectional analysis of Medicare patients and physician practices

2021

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“…In cancer care, patients' unmet needs and preferences can be successfully elicited using the Cancer Survivor Profile survey, which utilizes PROMIS item bank and provides a brief but comprehensive profile of problem areas experienced by breast cancer survivors [54] . Patient preference reports or platforms that facilitate clinicians' real-time access to patients' health, concerns, and preferences can help prioritize topics for discussion during medical encounters [55] .…”

Section: Goal Setting and Preference Elicitationmentioning

confidence: 99%

“…Advanced analytical tools using artificial intelligence (AI) can incorporate PRO measures, alongside a wide range of patient characteristics and preferences, to improve the quality of SDM, health outcomes, and patient satisfaction [62] . In a randomized controlled trial, Jayakumar et al developed an AI-enabled patient decision aid incorporating PROMs, patient education, preference assessment, to deliver personalized estimates of future health outcomes for patients with knee OA considering TKR [55] . This patient decision aid was associated with significant improvement in decision quality, level of SDM, patient satisfaction, and functional outcomes when compared with education and usual care alone.…”

Section: Facilitating and Evaluating Sdmmentioning

confidence: 99%

Real-world patient data: Can they support decision making and patient engagement?

Joeris

Zhu

Lambert

et al. 2023

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Patient-reported outcomes (PROs) capture data related to patients' perception of their health status and aspects of health care delivery. In parallel, digital innovations have advanced the administration, storage, processing, and accessibility of PROs, allowing these data to become actively incorporated in day-to-day clinical practice along the entire patient care pathway. Further, the emergence of shared decision making, where patients are engaged in informed treatment selection aligned with their preferences, values, and needs, can be realized by PROs and technology. This technology-enabled, data-driven approach provides insights which, when actioned, can enhance musculoskeletal care of patients and populations, while enriching the clinician-patient experience of decision making. In this review, we provide an overview of the opportunities enabled by PROs and technology for the cycle of orthopedic care.

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“…So, the use of AI accelerate and facilitate the development of new drugs, leading to a cheaper, more efficient process led to the successful conduct of clinical trials [33]. A study showed statistical improvement in decision-making, the quality of the overall decision-making level, patient satisfaction, and functional outcome in an AI-based randomized clinical trial [34]. Individual Case safety report (ICSR) is a document of a particular format to print a specific FDA guidance on the use, bring the NPP (Named Patient Program) information and product-related issues, and consumer reports.…”

Section: Clinical Trialmentioning

confidence: 99%

Health in Digital World: A Regulatory Overview in United States

Mishra

Gowrav

Balamuralidhara

et al. 2021

JPRI

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Adaptation towards digitalization in pharmaceuticals leads to the utilization and development of Artificial Intelligence (AI). Significantly it is reducing human workload with the help of an algorithm. Already AI is acting as a key in clinical trial, health care, quality management, manufacturing, product development, and management. Top pharmaceutical companies have adopted AI in different applications within the pharma sector. Different AI models like Machine learning, Artificial Neural Network, Deep Learning, robotics, and Natural Language Processing are being used in pharmaceuticals and healthcare systems. The Worldwide AI market is growing remarkably with a compound annual growth rate of 49.6% and is expected to reach $18,119 million by 2025. So, for better regulation, concerning safety, privacy regulatory strategy is heading towards a better framework. Different regulatory authorities like China, Europe, and United States (US) have adopted AI for economic and policy aspects. Emerging countries are using these tools for administrative work. US has begun implementing frameworks for AI adaptation, research, and development. The AI policy strategy started in 2016 with a series of workshops conducted under the Obama administration. Federal Food and Drug Administration (FDA) has also published draft guidance for regulatory oversight of AI and Machine Learning. In 2021 FDA published a draft regulation for software as a medical device. This review article provides a snapshot of AI implementation in pharmaceuticals and health care with the regulatory approach in the US.

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Comparison of an Artificial Intelligence–Enabled Patient Decision Aid vs Educational Material on Decision Quality, Shared Decision-Making, Patient Experience, and Functional Outcomes in Adults With Knee Osteoarthritis

Cited by 81 publications

References 74 publications

Association of patient engagement strategies with utilisation and spending for musculoskeletal problems in the USA: a cross-sectional analysis of Medicare patients and physician practices

Association of patient engagement strategies with utilisation and spending for musculoskeletal problems in the USA: a cross-sectional analysis of Medicare patients and physician practices

Real-world patient data: Can they support decision making and patient engagement?

Health in Digital World: A Regulatory Overview in United States

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