Transform Care Delivery Research Articles

The Family and Pregnancy Pop-Up Village: Developing a one-stop shop of services to reduce pregnancy care-related inequities in San Francisco.

Centering affected individuals and forming equitable institutional-community partnerships are necessary to meaningfully transform care delivery systems. We describe our use of the PRECEDE-PROCEED framework to design, plan, and implement a novel care delivery system to address perinatal inequities in San Francisco. Community engagement (PRECEDE phases 1-2) informed the "Pregnancy Village" prototype, which would unite key organizations to deliver valuable services alongside one another, as a recurring "one-stop-shop" community-based event, delivered in an uplifting, celebratory, and healing environment. Semi-structured interviews with key partners identified participation facilitators and barriers (PRECEDE phases 3-4) and findings informed our implementation roadmap. We measured feasibility through the number of events successfully produced and attended, and organizational engagement through meeting attendance and surveys. The goals of Pregnancy Village resonated with key partners. Most organizations identified resource constraints and other participation barriers; all committed to the requested 12-month pilot. During its first year, 10 pilot events were held with consistent organizational participation and high provider engagement. Through deep engagement and equitable partnerships between community and institutional stakeholders, novel systems of care delivery can be implemented to better meet comprehensive community needs.

Dynamic mirroring: unveiling the role of digital twins, artificial intelligence and synthetic data for personalized medicine in laboratory medicine.

In recent years, the integration of technological advancements and digitalization into healthcare has brought about a remarkable transformation in care delivery and patient management. Among these advancements, the concept of digital twins (DTs) has recently gained attention as a tool with substantial transformative potential in different clinical contexts. DTs are virtual representations of a physical entity (e.g., a patient or an organ) or systems (e.g., hospital wards, including laboratories), continuously updated with real-time data to mirror its real-world counterpart. DTs can be utilized to monitor and customize health care by simulating an individual's health status based on information from wearables, medical devices, diagnostic tests, and electronic health records. In addition, DTs can be used to define personalized treatment plans. In this study, we focused on some possible applications of DTs in laboratory medicine when used with AI and synthetic data obtained by generative AI. The first point discussed how biological variation (BV) application could be tailored to individuals, considering population-derived BV data on laboratory parameters and circadian or ultradian variations. Another application could be enhancing the interpretation of tumor markers in advanced cancer therapy and treatments. Furthermore, DTs applications might derive personalized reference intervals, also considering BV data or they can be used to improve test results interpretation. DT's widespread adoption in healthcare is not imminent, but it is not far off. This technology will likely offer innovative and definitive solutions for dynamically evaluating treatments and more precise diagnoses for personalized medicine.

Evaluation of patient satisfaction of receiving Systemic Anti-Cancer Therapy prescribed by nurse Non-Medical Prescribers

PurposeTo evaluate patient satisfaction of patients receiving Systemic Anti-Cancer Treatment prescribed by nurse Non-Medical Prescribers as a new model of care at a Cancer Unit in Northern Ireland, United Kingdom. MethodsA cross-sectional survey design, with a convenience sample of patients from five tumour groups who received Systemic Anti-Cancer Therapy by nurse Non-Medical Prescribers, across a 3-month period in 2022 was employed. Anonymised data were collected via postal survey, which incorporated a minimally modified version of the 45-item Leeds Satisfaction Questionnaire (LSQ). ResultsOne-hundred and sixteen surveys were returned, yielding a 36% response rate. Overall patients’ satisfaction levels with nurse non-medical prescribing of systemic anti-cancer therapy were high across all six subscales of the modified LSQ corroborated by qualitative free-text comments. Eighty-five percent of participants indicated they were happy to continue being prescribed systemic anti-cancer therapy by the nurse non-medical prescribers. ConclusionOverall patient satisfaction of Systemic Anti-Cancer Treatment prescribed by nurse Non-Medical Prescribers was positively rated; with high standards of compassionate, person-centred care reported, demonstrating an acceptable transformation in care delivery from a consultant-led model. Nonetheless, there was scope for improved health literacy to enhance patients’ understanding and compliance with treatment.

A Re-evaluation of Global Financial Outlooks and Prospects in the Telehealth Industry Amidst the Transition to a Post-Pandemic Era

The onset of the COVID-19 pandemic propelled the telehealth sector into a period of rapid expansion, with web-based care emerging as a crucial tool for ensuring the safety of patients and healthcare providers alike. However, with the introduction of vaccines, the perceived need for telehealth services appeared to diminish, leading to a decline in value for telehealth companies. This shift prompted both existing players and newcomers in the telehealth space to innovate and redefine their value propositions in order to regain investor and customer confidence and stand out amidst increasing competition. The struggle to reshape the perception of telehealth from a pandemic-era necessity to a valuable complement to traditional in-person care has been reflected in the volatility of stock prices and declining valuations. This dynamic has underscored the need for telehealth companies to differentiate themselves through technological advancements and the convenience they offer. Analyzing the market through qualitative secondary research, incorporating insights from contemporary sources, financial data from platforms like Yahoo! Finance, and peer-reviewed literature, it becomes evident that the telehealth market experienced a surge in 2020, reaching $17.9 billion in the US alone, and is projected to grow to $140.7 billion by 2030. This growth was accompanied by a nearly twofold increase in digital health venture funding, reaching $14.1 billion in total funding. However, this rapid expansion led to an oversaturation of the market, with supply outpacing demand, resulting in a significant drop in valuations as vaccination rates rose in 2021. In response to this rebalancing, telehealth companies must chart a course for the post-pandemic era. The current role of suppliers in the telehealth space, whether they are established healthcare industry incumbents or innovative "telehealth-first" challengers, will influence the growth strategies they pursue. Moreover, identifying medical specialties and patient demographics that are best suited for web-based care will be critical for long-term success in the evolving healthcare delivery system. Looking ahead, investors should anticipate a bullish market characterized by growing share prices. Success in the telehealth industry is likely to hinge on fundamentally transforming care delivery models rather than simply transitioning traditional care to a web-based format. As the market consolidates, established telehealth giants capable of adapting to the changing landscape are expected to thrive, albeit amidst continued growth at a slower pace.

Machine learning-based prognostication of mortality in stroke patients

ObjectivesPredicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke. Materials and methodsData from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors. ResultsThe final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes. ConclusionsThis model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments.

Early results of a pancreas cancer learning health network: Canopy Cancer Collective.

645 Background: Pancreas cancer (PC) is the third leading cause of cancer death in the United States. Canopy Cancer Collective (CCC) was founded in 2019 given the urgent need for improvements in survival and patient experience. Through interdisciplinary teams inclusive of patients, 14 care centers formed a learning health network (LHN) to share data and best practices to accelerate improvements in PC care delivery and outcomes. Methods: In October 2022, LHN representatives gathered to establish aims for the year. Through a consensus process, the LHN arrived at four aims to achieve by December 31, 2023. The aims are: Patients screened for 1) clinical trials will increase from 39% to 100%, 2) palliative care will increase from 30% to 100%, 3) enzyme need will increase from 37% to 100%, and 4) goals of care assessed will increase from 57% to 100% . CCC established measures to guide improvement toward the aims, which are tracked using a registry. Sites co-developed operational definitions for each measure and implemented new practices to identify and manage all patients with pancreas adenocarcinoma who enter their care. For each aim, each care center identified their current processes for screening, documented their desired future processes, tested changes to close gaps, and standardized successful changes. Many began with a subset of their population, or in a single clinic, and are working to expand to additional populations and settings. CCC facilitates best practice sharing among LHN peers and data sharing. Care centers have had access to the following supports: 1) quality improvement coaching, 2) topical workgroups, 3) peer workgroups, 4) network-wide webinars, 5) summaries of best practices to adapt locally, 6) data at site and network levels, and 7) a community platform for documentation and collaboration. Results: There are 2,443 patients in the CCC registry. All sites report data on patients discussed in pancreas clinic or tumor board and are working to include their full PC population. Data analyzed using Shewhart control charts (p-charts) show improvement at an aggregate level across all aims. A January 2021-December 2022 baseline includes 1,218 patients. Care centers began testing changes in January 2023, and 1,225 patients have been included since. The percent of patients screened for clinical trials, enzyme need, and palliative care has shifted above the upper control limits for at least six months. The percent of patients with goals of care assessed shifted above the upper control limit for five months. Just one data point outside the upper control limit (UCL) signifies non-random variation. Conclusions: These aims put our 14 care centers on a path to delivering measurably improved care for patients through better access to nutrition, clinical trials, and palliative and supportive care services. A LHN is a model of collaboration and community building that can successfully transform care delivery at scale for patients with PC.

Needs and preferences regarding a mobile application to support diabetic foot self-care.

To identify the needs and preferences of individuals with type 2 diabetes regarding the functionalities and characteristics for a mobile application to support foot self-care. Qualitative research with 16 individuals diagnosed with type 2 diabetes recruited during clinical care at a university hospital in Porto, Portugal. Data were collected through semi-structured interviews between March and June 2022 and analyzed using inductive content analysis. Three categories and nine subcategories were identified. Categories included informational needs, essential functionalities for foot health self-care, and user-relevant experience. The preference for objective, limited data input, flexible, and customizable applications was an important factor influencing technology engagement. The research highlighted a preference for customizable and flexible applications, aiding nurses in creating solutions that transform care delivery and enhance the quality of life for individuals living with diabetes.

Standardizing Care for Huntington’s Disease: The Development of an openEHR Template for Motor Symptoms

Huntington’s disease (HD) is a rare yet debilitating neurodegenerative disorder originating from an increase in CAG repeats within the HTT gene. Despite ongoing research, a curative treatment for HD remains elusive, with current therapeutic strategies primarily targeting symptom management. Among the spectrum of HD symptoms, motor impairments are often the most obvious and disabling. To standardize treatment regimens and enhance patient care and quality of life, global evidence-based Clinical Practice Guidelines (CPGs) have been published to reduce variations in care. The integration of these guidelines into clinical information systems could significantly improve physician adherence to them, thereby optimizing patient outcomes. This study aims to develop an openEHR template specifically designed to represent recommendations for managing Huntington’s motor symptoms, contributing to standardized care delivery, and enabling the seamless exchange of treatment information among different healthcare providers involved in the management of HD. We assessed the International Guidelines for the Treatment of Huntington’s Disease commissioned by the European Huntington’s Disease Network (EHDN) and selected the most representative variables regarding the HD motor symptoms. The openEHR template development followed the methodological guidance of the openEHR data modeling approach (’odma’). A total of 101 data items were extracted from the guideline section related to motor symptoms. After the classification and merging of these data elements, six domain concepts were organized for the motor symptom’s treatment of Huntington’s disease. This study successfully developed an openEHR template for managing Huntington’s disease motor symptoms. These advancements have the potential to enhance treatment outcomes and transform care delivery for Huntington’s disease.

Putting Patient Experience Data into Action! A Workshop to Explore the Collection and Use of Patient Experience Data in Practice

Health systems globally are in pursuit of the quintuple aim – better population health, improved patient and caregiver experiences, improved clinician experiences, sustainable per capita costs1 and greater health equity. Patients and their care partners (family and friends who provide care) have unique insights on the quality of healthcare delivery, yet, few organizations are poised to meaningfully integrate these voices into care delivery transformation efforts. Patient experience (PE) data comes in many forms including (and not limited to) structured and unstructured survey data, narratives of patient and family complaints or accolades, adverse event reporting, or qualitative interviews and focus groups. These data are often temporally lagging the patient’s interaction with the health system, rarely includes the voices of equity deserving groups (i.e, individuals who have been historically marginalized and underrepresented in research) and are seldom used for improving care.
 To meet the expectations of patients and care partners, we require a comprehensive, systematic, and sustainable approach to integrating patient and care partner voices into how we reform, design and deliver care.
 Through a scoping review and stakeholder engagements we have highlighted that methods for the collection and implementation of PE data should be multidimensional and have identified four components of a PE Tool-kit (PET) to move from knowledge to action: 1) engage the community meaningfully; 2) identify and curate multiple forms of PE data; 3) aggregate and synthesize PE data; and 4) integrate PE data into quality improvement efforts.
 We are conducting this workshop to further explore domains within our proposed Patient Experience Tool-Kit that resonates with an international audience and identify gaps and implementation considerations.
 The workshop will be grounded by a brief theory burst about the challenges the PET and its components aim to address followed by interactive sessions in small groups to address the following questions:
 1)How will the proposed components support the link between the collection and use of PE data into health system improvement efforts? What tool-kit components are missing?
 2)What are the barriers and facilitators to implementing the PE Tool-Kit?
 Note: we will direct participants to use a sociotechnical systems approach when identifying barriers and facilitators which consists of: people, environment (macro/micro), tools (i.e., technology), and tasks (i.e., process).
 3)What are the considerations/adaptations to consider that will support the Tool-Kit’s adoption across different sectors of the integrated care system?
 
 Introduction to Workshop/ Overview of Activities – 5 mins
 Presentation of Patient Experience Tool-Kit Components- 10 mins
 Small group discussions to address the three questions (each table to start with a different question)- 30 mins
 Large group activity to report back and synthesis small group discussion, including the l use Mentimeter to capture core insights). The facilitators will summarize key insights and highlight core themes captures in the discussion at the end of the activity – 20 minutes
 Wrap up to assess interest in developing a community of practice for continued learning and sharing- 5 mins
 

Accelerating Care Delivery Transformation — The CMS Innovation Center’s Role in the Next Decade

Summary Over the past decade, the U.S. Centers for Medicare & Medicaid Services (CMS) Center for Medicare and Medicaid Innovation (CMS Innovation Center) has tested more than 50 payment models with a range of participants and value-based payment features. Value-based payment models can provide the necessary flexibility to support care delivery that better addresses people’s health and health-related social needs. This transformation of the delivery system, supported by value-based payment models, can drive better care, improve outcomes, and lower costs. The impact of CMS Innovation Center models has largely focused on financial impacts, with varied results in terms of total health care cost reduction. There has been less focus on the extent to which payment models have enabled care delivery transformation. To inform the development of a new framework to better assess the impacts of payment models and to drive care delivery transformation, the CMS Innovation Center undertook a retrospective review and synthesis of select models to assess if — and which — care delivery changes have been observed. The review indicated demonstrable evidence of enhanced care delivery in several areas, such as care coordination, team-based care, and leveraging data to risk-stratify patients, among other strategies. Three broad themes are shared among the more successful efforts. (1) Participants across models used common care coordination and other strategies to deliver person-centered care, (2) practice changes enabled by value-based care models showed evidence of tailoring care to local needs, and (3) care delivery trends and changes extend beyond the CMS Innovation Center models. This article summarizes those findings, which are informing the development of a new framework to accelerate care transformation.

Improving the Value of Health Care and Accelerating Care Delivery Transformation

Summary This issue of NEJM Catalyst Innovations in Care Delivery includes articles, case studies, and research reports on U.S. Centers for Medicare & Medicaid payment models, mental health navigation, patient transfers, machine learning, HIV preexposure prophylaxis, patient-reported outcome measures and hospital rankings, and post-acute care.

Alaskans’ Thoughts on Improving Healthcare Service

This paper explores how healthcare technologies have transformed care delivery to patients with a particular reference to Alaska. The paper first examines how electronic health records help build communication among stakeholders. Then, it studies how remote healthcare services technologies change the delivery of services. Finally, it scrutinizes how patient experiences are transformed by adopting technological components. Through a survey of residents, Alaskans’ acceptance and suggestions for improving healthcare services are displayed. The findings of the research will be of value to policy makers and healthcare organizations.

U.S. physicians’ perspectives on the complexities and challenges of permanent contraception provision

ObjectivesEvidence shows many misconceptions exist around permanent contraception, and there are numerous barriers to accessing the procedure. This qualitative study explored physician perspectives regarding patients’ informational and decision-support needs, the complexities and challenges of counseling and access, and how these factors may differ for people living on lower incomes. Study designWe conducted 15 semistructured, telephone interviews with obstetrician-gynecologists in three geographic regions of the United States to explore their perspectives on providing permanent contraception counseling and care. We analyzed the interviews using content analysis. ResultsPhysicians discussed a tension between respecting individual reproductive autonomy and concern for future regret; they wanted to support patients’ desire for permanent contraception but were frequently concerned patients did not have the information they needed or the foresight to make high-quality decisions. Physicians also identified barriers to counseling including lack of time, lack of continuity over the course of prenatal care, and baseline misinformation among patients. Physicians identified additional barriers in providing a postpartum procedure even after thedecision was made including lack of personnel and operating room availability. Finally, physicians felt that people living on lower incomes faced more challenges in access primarily due to the sterilization consent regulations required by Medicaid. ConclusionsPhysicians report numerous challenges surrounding permanent contraception provision and access. Strategies are needed to support physicians and patients to enhance high-quality, patient-centered sterilization decision making and ensure that patients are able to access a permanent contraceptive procedure when desired. ImplicationsThis qualitative study demonstrates the various challenges faced by physicians to support permanent contraception decision making. These challenges may limit patients’ access to the care they desire. This study supports the need to transform care delivery models and improve the federal sterilization policy to ensure equitable patient-centered access to desired permanent contraception. DisclaimerAlthough the term permanent contraception has increasingly replaced the word sterilization in clinical settings, we use sterilization in some places throughout this paper as that was the standard terminology at the time the interviews were conducted and the language the interviewed physicians used.

The Breadth and Depth Needed for Care Delivery Transformation

Summary This issue of NEJM Catalyst Innovations in Care Delivery contains articles, case studies, and research reports on health information exchange policy, palliative care practices, comprehensive care to reduce readmissions, a head and neck cancer clinic, full-cycle stroke care delivery, and patients with intellectual and developmental disabilities.

Applying embedded program evaluation for care delivery transformation: An analysis of a home-based urgent care program.

BackgroundIn 2014, Mass General Brigham, formerly Partners HealthCare, launched a novel urgent home‐based medical care program to provide rapid medical evaluation and treatment to homebound patients and older adults with frailty or limited mobility named the partners mobile observation unit (PMOU) program.MethodsWe conducted a pragmatic, embedded evaluation assessing the impact of PMOU on postreferral utilization and total medical expenditure (TME). We used propensity weighting and logistic regression to estimate the 30‐day adjusted odds ratios (ORs) of emergency department (ED) utilization and inpatient medical hospitalization for patients enrolled in PMOU (891 episodes of care) relative to those who were referred but not enrolled in the program (57 episodes of care) during the period of April 2017 to June 2018. We additionally conducted a difference‐in‐differences analysis assessing program impact on TME, comparing claims data 30 days pre/post referral.ResultsDespite positive trends, there were no statistically significant differences between the two groups with regard to postreferral ED visits or hospitalizations, with an OR of 0.83 (p = 0.56) and OR of 0.64 (p = 0.21), respectively. There was no statistically significant difference in pre/post referral TME for intervention relative to control episodes (p = 0.64). In post hoc analysis of control episodes, 75% received care elsewhere within 14 days of referral.ConclusionAlthough the results suggested positive trends, this analysis of this relatively mature program was unable to identify statistically significant reductions in ED visits, hospitalizations, or TME associated with the PMOU program. Future efforts to build home‐based urgent care programs or related programs targeting older adults with frailty or limited mobility should aim to improve patient targeting and identify opportunities to improve program operations and generate meaningful reductions in healthcare utilization and spending.

A maturity model framework for integrated virtual care

PurposeRemote patient monitoring (RPM) and virtual visits have the potential to transform care delivery and outcomes but require intentional planning around how these technologies contribute to integrated care. Since maturity models are useful frameworks for understanding current performance and motivating progress, the authors developed a model describing the features of RPM that can advance integrated care.Design/methodology/approachThis work was led by St. Joseph's Health System Centre for Integrated Care in collaboration with clinical and programme leads and frontline staff offering RPM services as part of Connected Health Hamilton in Ontario, Canada. Development of the maturity model was informed by a review of existing telehealth maturity models, online stakeholder meetings, and online interviews with clinical leads, programme leads, and staff.FindingsThe maturity model comprises 4 maturity levels and 17 sub-domains organised into 5 domains: Technology, Team Organisation, Programme Support, Integrated Information Systems, and Performance and Quality. An implementation pillars checklist identifies additional considerations for sustaining programmes at any maturity level. Finally, the authors apply one of Connected Health Hamilton's RPM programmes to the Team Organisation domain as an example of the maturity model in action.Originality/valueThis work extends previous telehealth maturity models by focussing on the arrangement of resources, teams, and processes needed to support the delivery of integrated care. Although the model is inspired by local programmes, the model is highly transferable to other RPM programmes.

Our Nursing Profession at a Crossroads: Time to Chart a Course for the Future.

Nurse navigators, in a role that originally focused on a single health condition and improving specified services for an individual patient, have expanded to include care management and care coordination. As the role evolves, nurse navigators are demonstrating proficiency and achieving success transforming care delivery to improve population health while improving quality outcomes, patient satisfaction, and decreasing cost. One innovative health system's clinical nurse specialist team partnered with primary care providers, ancillary care teams, home care, skilled nursing facilities, community agencies, and partners in public health and schools. Clinical nurse specialists, now called "transitional care nurses (TCNs)," have created an accountable community of health for their high-risk population. By following patients from one setting to the next, TCNs identified opportunities for improvement, created innovative programs to bridge gaps, improved teamwork, and integrated care, resulting in lower cost, high-quality care. Results included 50% reduction in hospitalization for patients with chronic disease, pre- and post-TCN partnership. Patients with diabetes were supported with access to diabetes coaches, which resulted in a 12% reduction in AIC, while patients completing pulmonary rehabilitation programs experienced reduced readmission rates from 24% to 2.7%.

An Overview of Medical Electronic Hardware Security and Emerging Solutions

Electronic healthcare technology is widespread around the world and creates massive potential to improve clinical outcomes and transform care delivery. However, there are increasing concerns with respect to the cyber vulnerabilities of medical tools, malicious medical errors, and security attacks on healthcare data and devices. Increased connectivity to existing computer networks has exposed the medical devices/systems and their communicating data to new cybersecurity vulnerabilities. Adversaries leverage the state-of-the-art technologies, in particular artificial intelligence and computer vision-based techniques, in order to launch stronger and more detrimental attacks on the medical targets. The medical domain is an attractive area for cybercrimes for two fundamental reasons: (a) it is rich resource of valuable and sensitive data; and (b) its protection and defensive mechanisms are weak and ineffective. The attacks aim to steal health information from the patients, manipulate the medical information and queries, maliciously change the medical diagnosis, decisions, and prescriptions, etc. A successful attack in the medical domain causes serious damage to the patient’s health and even death. Therefore, cybersecurity is critical to patient safety and every aspect of the medical domain, while it has not been studied sufficiently. To tackle this problem, new human- and computer-based countermeasures are researched and proposed for medical attacks using the most effective software and hardware technologies, such as artificial intelligence and computer vision. This review provides insights to the novel and existing solutions in the literature that mitigate cyber risks, errors, damage, and threats in the medical domain. We have performed a scoping review analyzing the four major elements in this area (in order from a medical perspective): (1) medical errors; (2) security weaknesses of medical devices at software- and hardware-level; (3) artificial intelligence and/or computer vision in medical applications; and (4) cyber attacks and defenses in the medical domain. Meanwhile, artificial intelligence and computer vision are key topics in this review and their usage in all these four elements are discussed. The review outcome delivers the solutions through building and evaluating the connections among these elements in order to serve as a beneficial guideline for medical electronic hardware security.

Realizing the Dream: The Future of Primary Care Research.

Primary care research is central to the successful transformation of care delivery, providing the crucial evidence to overcome the longstanding and widespread threats and challenges to the realization of primary care's full potential. The Agency for Healthcare Research and Quality (AHRQ), as the federal agency specifically charged with conducting and supporting primary care research, plays a pivotal role in supporting the research and generating the evidence needed to advance primary care. Drawing upon decades of AHRQ-supported research studies, extensive stakeholder consultation, and a Primary Care Research Summit held in fall 2020, we discuss the primary care research central to successful primary care transformation and for realizing the vision of a high-performing US health system to effectively serve all Americans and their communities while advancing health equity.Realizing the potential of primary care will require wise investments in primary care research. Newly generated evidence needs to be rapidly incorporated into the design of the delivery system, clinical care, and community interventions. Investments in evidence-informed primary care redesign can catalyze progress to achieving the quintuple aim-improved health outcomes, increased value, better patient and clinician experience, and health equity. Primary care research can provide the evidence to help stem the twin epidemics of clinician burnout and lack of trust in the health system. Actualizing this vision will require a concerted and coordinated effort by policy makers, researchers, clinicians, and community members and a commitment to ensuring people and communities have ready access to primary care.Appeared as Annals "Online First" article.

Influence of Race, Insurance, Rurality, and Socioeconomic Status on Equity of Lung and Colorectal Cancer Care.

This study evaluated the influence that social determinants of health had on stage at diagnosis and receipt of cancer-directed surgery for patients with lung and colorectal cancer in the North Carolina Central Cancer Registry (2010-2015). This study examined non-Hispanic uninsured or privately-insured patients 18 to 64 years of age. Multivariable logistic regression models, including two-way interaction terms, assessed the influence of race, insurance status, rurality, and Social Deprivation Index on stage at diagnosis and receipt of surgery. 6574 lung cancer patients and 5355 colorectal cancer patientswere included. Among the lung cancer patients, the uninsured patients had higher odds of having stage IV disease (odds ratio [OR]=1.46; 95 % confidence interval [CI]=1.22-1.76) and lower odds of receiving surgery (OR=0.48; 95 % CI=0.34-0.69) than the privately-insured patients. Among the colorectal cancer patients, uninsured status was associated with higher odds of stage IV disease (OR=1.53; 95 % CI=1.17-2.00) than privately-insured status. A significant insurance status and rurality interaction (p = 0.03) was found in the colorectal model for receipt of surgery. In the privately-insured group, non-Hispanic Black and rural patients had lower odds of receiving colorectal surgery (OR=0.69; 95 % CI= 0.50-0.94 and OR= 0.68; 95 % CI= 0.52-0.89; respectively) than their non-Hispanic White and urban counterparts. After controlling for confounding and evaluation of interactions between patient-, community-, and geographic-level factors, uninsured status remained the strongest driver of patients' presentation with late-stage lung and colorectal cancer. As policy and care delivery transformation targets uninsured and vulnerable populations, explicit recognition, and measurement of intersectionality should be considered.