
==== Front
Perm J
tpj
tpj
The Permanente Journal
1552-5767
1552-5775
The Permanente Press

38980696
10.7812/TPP/23.157
TPJ-23-157
Commentary
Beyond Shared Decision-Making: Integrating Coproduction, Learning Health Systems, Artificial Intelligence, and Workforce Development for Patient-Centered Care
http://orcid.org/0000-0003-2477-9365
Baysah Clark Kolu S DNP, MPH 1
Rudell Elaine MHA, CHCP 2
Setiadi David PhD 2
Agrawal Tarjani PhD 2
http://orcid.org/0000-0002-7399-622X
Oliver Brant J PhD, MS, MPH, FNP-BC, PMHNP-BC 3 4 5
1 UMass Memorial Medical Center, Worcester, MA, USA
2 Projects In Knowledge Powered by Kaplan, Ft Lauderdale, FL, USA
3 Departments of Community & Family Medicine, Psychiatry, and the Dartmouth Institute for Health Policy & Clinical Practice, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
4 Division of Care Experience, Value Institute, Dartmouth Health, Lebanon, NH, USA
5 Chronic Health Improvement Research Program (CHIRP) at Dartmouth Health, Lebanon, NH, USA
Kolu S Baysah Clark, DNP, MPH Kolu.S.Baysah.Clark.GR@dartmouth.edu
2024
05 6 2024
28 3 284288
© 2024 The Authors.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Published by The Permanente Federation LLC under the terms of the CC BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/.

Financial Disclosures Kolu S Baysah Clark, DNP, MPH, Elaine Rudell, PhD, David Setiadi, PhD, and Tarjani Agrawal, PhD, have no relevant financial relationships to disclose. Brant J Oliver, PhD, MS, MPH, FNP-BC, PMHNP-BC, reports limited consulting fees from Projects In Knowledge Powered by Kaplan.
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pmcIntroduction

Many health care situations offer evidence-based options requiring preference-sensitive trade-off decisions concerning a person’s values or preferences in deciding the best course of action, referred to as “preference-sensitive decisions.”1 Shared decision-making (SDM) is often indicated in preference-sensitive situations.

What is SDM?

SDM a form of communication indicated for preference-sensitive decisions that can improve patient-centered care.2 In patient-centered care, patients are actively involved in decision-making about their health and health care. SDM empowers patients by giving them the information they need to make choices aligned with their preferences and values. SDM is “an approach where clinicians and patients share the best available evidence when faced with the task of making decisions, and where patients are supported to consider options, to achieve informed preferences.”3 In Crossing the Quality Chasm, the National Academy of Medicine, formerly the Institute of Medicine, argued that care should be patient-centered and explicitly mentioned SDM to help facilitate this.2 Charles et al described SDM as having at least two participants actively engaged in the treatment decision agreeing to mutual acceptance, with each party an expert: the practitioner an expert in medical issues and treatment options and the patient an expert in their preferences, values, and concerns.4

Benefits of SDM

SDM has also proven to positively affect practitioner–patient communication and patient satisfaction, as SDM allows people to feel more knowledgeable, better informed, and transparent about their values; be actively engaged in decision-making; and have accurate risk perceptions. Additionally, SDM improves self-management, adherence, and outcomes in chronic conditions; for example, SDM is associated with less prescription opioid misuse through the trust fostered between patients and practitioners.5,6 Following the recent COVID-19 pandemic, SDM was effective in reducing vaccine hesitancy, and it may help increase vaccination rates.7 SDM use in chronic conditions, such as epilepsy, has shown several benefits, including improved decision quality, more informed choices, and better treatment concordance.8

Four critical factors for promoting SDM

In this commentary, the authors explore 4 critical factors to leverage the sustainment and advancement of SDM practice in health care: 1) coproduction, 2) learning health systems (LHS), 3) artificial intelligence (AI), and 4) workforce development. Coproduction can drive SDM in health care encounters, especially for complex, chronic, and costly conditions, such as multiple sclerosis. LHS can drive SDM by utilizing patient-reported outcomes and real-world evidence and feedback loops to inform decision-making and drive improvements in care delivery. Additionally, integrating AI can promote SDM in treatment encounters, offering unprecedented opportunities for enhanced decision-making, personalized care (precision medicine), and streamlined care processes.9 Finally, for SDM to scale and spread, workforce capability must include a workforce trained in SDM and prepared to implement it in care encounters. This commentary investigates the interplay between these areas and how they can drive SDM, focusing on their potential to revolutionize health care delivery. By examining the synergistic effects of these components, the authors aim to further the continuing dialogue about how health care can effectively and efficiently facilitate SDM in the future.

Coproduction of Health Care Service

Coproduction is a form of patient-centered care whereby consumers (patients, families) work in partnership with producers of health care (eg, hospitals, physicians, nurses) to produce health. Batalden et al have described coproduction of health care as a service that is cocreated rather than a product that is delivered—it is “the interdependent work of users and professionals to design, create, develop, deliver, assess, and improve the relationships and actions that contribute to the health of individuals and populations.”10 SDM is one of many coproduction methods that can increase the active participation of patients in their care and strengthen partnerships between patients and their clinicians. Coproduction and SDM both assume mutual respect and trust between parties. When patients feel respected, valued, and listened to, they are more likely to engage in decision-making.6 Coproduction encourages and requires open and honest communication, which can lead to a better understanding of the patient’s preferences, values, and goals—critical components of SDM. Coproduction also promotes patient-centered care, because the care is codesigned with the patient’s input and tailored to the individual.10,11

When patients are actively engaged in coproduction, they can become more informed about their health conditions and treatment,10 which in turn can increase health literacy and strengthen SDM. Treatment adherence can also improve because patients understand the rationale behind the recommendation(s) and take greater accountability and responsibility.10 Coproduction can help address health disparities by acknowledging and incorporating different populations’ diverse needs and perspectives,12 reduce medical errors or unnecessary treatments, and improve care experience.5,6,10,11,13 Common barriers to coproduction include production-based delivery approaches that can limit opportunities for practitioner–patient partnership and communication, knowledge deficits about coproduction that require education of both patients and practitioners, as well as education of staff.

Learning Health Systems

The concept of a LHS was introduced to health care in 2007 by the Institute of Medicine/National Academy of Medicine.14 The Agency for Healthcare Research and Quality defines a LHS as “a health system in which internal data and experience are systematically integrated with external evidence, and that knowledge is put into practice,”15 resulting in patients receiving better, safer, more efficient health care. The Agency for Healthcare Research and Quality has 6 criteria for a LHS, including that 1) its leaders are committed to learning and improvement and that it 2) systematically gathers and applies evidence, 3) uses information technology methods to inform SDM, 4) promotes inclusion of patients as vital members of the learning team, 5) captures and analyzes data to improve care, and 6) continually assesses outcomes to inform improvement and research. This framework applies to care delivery as a system that seeks to continuously learn and improve through the generation and application of best practices by integrating innovation, quality, and practice to promote value in health care. As technology advances, LHS approaches will likely employ more comprehensive and granular data, leading to more personalized treatment options, allowing for precision medicine, predictive analytics, and personally tailored SDM.

There are many LHS models, such as the Promise Partnership LHS, a coproduction LHS in an academic health care system to improve the care of oncology patients. This framework explicitly brings together clinicians, patients, scientists, and health systems administrators (both leadership and operational), and quality improvemen to share and optimize health outcomes and improve care values while generating new knowledge.16 Another example is the inflammatory bowel disease (IBD) Qorus study, a care improvement LHS for patients with IBD at 27 community-based gastroenterology clinics and academic medical centers. This study uses patient-reported outcome data to improve care by applying several metrics specific to this population, including remission status, urgent care needs, steroids use, and emergency department visits, with reductions in these metrics in addition to sharing data, that is then used to improve care over the period of the study.17

By leveraging data, such as best evidence, patients’ preferences, characteristics, and outcomes, the LHS infrastructure can help inform treatments for different populations by providing clinicians and patients with evidence-based options. Finally, LHS approaches enable feed-forward capability, allowing dynamic, individualized dashboards for use by patients and clinicians engaged in clinical decision-making with feedback capability at the clinical, health system, and population levels to simultaneously inform continuous quality improvement, implementation, and related research efforts. The primary barriers to LHS implementation involve the financial, human, and information technology resources required. In many cases, fully functional LHS data systems fail to connect to well-informed improvement, implementation, and strategic action.

Beneficial AI

With the growth and explosion of health care content/data, the exponential effect of networked multifaceted data technologies, and the demand for remote practice from COVID-19, the need for AI to drive clinical support has risen considerably.18 AI refers to using algorithms and computational models to perform tasks that have traditionally required human intelligence.18 AI can analyze large volumes of data in health care to identify patterns and make predictions.9 Beneficial AI integrates machine-learning technologies to provide more effective and efficient patient-centered care to analyze patient data; generate personalized treatment options, including risks and benefits; and support clinical decision-making, aiming to improve health outcomes for patients and overall population health9,18,19 and thereby aiding the SDM process. Wearable technology often interacts with AI to inform medical decisions, such as continuous glucose monitor data using AI to detect patterns, creating actionable decisions at the point of care.

SDM uses patient decision aids (PDAs) to help patients focus on their preferences and concerns, promoting strong partnerships in decision-making. PDAs have been shown to be effective in reducing decisional conflict and improving knowledge of the disease and treatment options, awareness of risk, and satisfaction with the decisions made.2,13 Although PDAs were originally in paper form, they have continually evolved. AI systems have great potential to ensure that SDM is based on the most up-to-date and best available evidence to inform decisions.

AI, such as comprehensive Telehealth technologies, can facilitate communication between practitioners and patients through natural language processing (NLP).9 NLP-powered chatbots, or virtual assistants, can engage with patients in natural language, helping explain complex medical information and facilitating SDM discussions. Implementing NLP to workflow will increase face-to-face practitioner–patient time to make quality shared decisions.

Data security and privacy is a concern voiced by both patients and practitioners. A method to enable secure virtual care delivery is through blockchain.20 Blockchain can reduce the vulnerability and chance of losing data due to theft, fire, or catastrophe by minimizing the chances of overwriting, tampering, deleting data, or committing fraud.20 Current blockchain technology includes mechanisms for data-sharing consent, validation, consistency, reproducibility, transparency, attribution, and accountability.20 There are additional concerns, such as interpretability for the end-users (patients and clinicians) who sometimes express frustration with how AI can create more complexity and tasks, in an already challenging electronic health environment.21

Soliciting end-user input in codesigning AI empowered systems can help improve adoption and use, building trust among users and reducing gaps between practitioners’ and patients’ care expectations and knowledge. Data quality and completeness are vital, so the chance of AI generating incomplete or false data must be minimized. What AI “knows” and can learn is driven by the data it uses, and this has the potential to amplify disparities and potentially widen the disproportionate underrepresentation of marginalized groups. Hence the term—“beneficial AI”—meaning AI that is designed and continuously improved with intentionality for full inclusion, optimal usability, and maximum utility.

Workforce Development

Innovative care models, such as coproduction, LHS, and beneficial AI, will not, on their own, sustain and scale SDM. People will remain an essential and critical factor—making workforce development in SDM knowledge and skills the final critical element. Workforce development encompasses the training, education, and ongoing professional development of health care professionals and support staff. One guideline for making SDM part of everyday care in all health care settings calls for training on communicating risks, benefits, and consequences; using PDAs; and embedding SDM in organizational culture and practices. It also states that organizations should ensure that knowledge, skills, and confidence to support SDM are included in all health care staff’s induction, training, and continuing professional development.22

Effective SDM requires a skilled health care workforce. Training programs can equip people with communication, counseling, and decision-support skills. With innovations like AI, providing appropriate training and education is crucial.9 A well-trained workforce understands the system and the importance of involving patients in decisions about their care while respecting their preferences and values and provides the information needed to make informed choices. Training to ensure cultural competence is vital, because a diverse and culturally competent workforce is essential for the future of SDM. Understanding and respecting cultural differences, beliefs, and practices is also crucial for tailoring care plans that align with the patient’s values and preferences. A well-trained workforce will help reduce health disparities by ensuring that SDM is equitable and accessible to all patients. Navigating complex decisions, such as treatment options, risks, and varying outcomes, requires skills to guide patients through the complexity of trade-off options to make choices that align with their preferences. Finally, as health care technology advances, the workforce must be equipped with the skills to use and integrate digital tools that support SDM.

In a recent multiple sclerosis initiative employing a practical virtual training approach for busy health care clinicians, participants demonstrated improved SDM skills and improved experience and understanding about SDM.23–25 In one of the programs, patients embraced their SDM training and became health care champions.23 Some reported exercising more frequently; one bought an e-bike and became proactive in their own health care. Although their clinicians had made these recommendations in the past, participants commented that the education program helped them realize the critical role they play in their health care decisions and self-care, as well as how better communication with their clinicians leads to a stronger partnership. Barriers/limitations observed include time constraints (clinicians), knowledge deficits, and clinician overestimations of their SDM practices and capabilities prior to training. Many of these barriers were overcome via participation in the programs.23

Conclusion

SDM is a collaborative process where clinicians and patients work together to make health care decisions. SDM empowers patients by giving them the information they need to make choices that align with their preferences and values and promote patient-centered care. Advancements in SDM through coproduction, LHS, beneficial AI, and workforce development represent a convergence of critical factors that will be necessary to spread and scale SDM in the future. Coproduction, which views patients as equal partners, has the potential to drive decision quality, and LHS infrastructures can provide real-time evidence and innovation. Beneficial AI can analyze patient data, generate insights, and support clinical decision-making, and workforce development can shape and empower health care professionals to provide SDM. The result could be a move toward a more integrated patient-centered care approach, collaborative treatment decisions using the best available evidence and individual preferences, and technologies that support decision-making and health care coproduction.

Acknowledgment

The authors would like to acknowledge Michele Ingram, BS, for her exceptional review of the paper prior to journal submission.

Author Contributions: Kolu S Baysah Clark, DNP, MPH, wrote the shared decision-making and coproduction portions and integrated the coauthored sections. Elaine Rudell, PhD, contributed to the workforce development section. David Setiadi, PhD, and Tarjani Agrawal, PhD, contributed to the artifical intelligence section, and Brant J Oliver, PhD, MS, MPH, FNP-BC, PMHNP-BC, provided expertise across all sections.

Conflicts of Interest: None declared

Funding: None declared
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