Table of Contents
Overview
Definition of Outcomes Research
Outcomes research (OR) is a field that integrates an evaluation process into standard clinical practice, aiming to provide feedback for improving the quality of care. This often involves assessing treatments already in use to determine their effectiveness in real-world settings.[1.1] OR combines principles from various disciplines, including epidemiology, clinical research, health economics, quality of life assessment, and health policy, making it critical for obtaining evidence regarding the benefits, risks, and costs associated with healthcare interventions.[2.1] A distinguishing feature of outcomes research is its broader focus on a range of outcomes beyond traditional clinical measures of treatment success. While traditional measures remain important, OR emphasizes patient-centered outcomes such as functioning, well-being, cost-effectiveness, and patient satisfaction.[3.1] This patient-centric approach is increasingly recognized as vital in contemporary medicine, as it allows for a more comprehensive understanding of treatment effectiveness from the patient's perspective.[5.1] Outcomes research is also characterized by its emphasis on developing new strategies and methods to enhance the validity and efficiency of evaluative research. This includes balancing non-experimental and experimental methods to make informed inferences.[6.1] Although the term "outcomes research" lacks a consistent definition, it generally refers to research focused on the effectiveness of public health interventions and health services.[7.1] Incorporating real-world evidence (RWE) into outcomes research further enriches the understanding of treatment effectiveness. RWE is derived from real-world data (RWD) collected during routine healthcare delivery, which can complement traditional clinical trial data and provide insights into how treatments perform across diverse patient populations.[21.1] The integration of advanced technologies, such as artificial intelligence and machine learning, is expected to revolutionize the analysis of RWD, thereby enhancing the overall understanding of treatment effectiveness in outcomes research.[23.1]Importance in Healthcare
Outcomes research plays a critical role in enhancing healthcare quality and informing policy decisions. One significant aspect of this research is patient-centered outcomes research (PCOR), which emphasizes the importance of engaging patients and other stakeholders in the research process. This engagement has been shown to improve the feasibility and acceptability of studies, thereby enhancing their relevance and applicability to real-world healthcare settings.[8.1] By incorporating patient perspectives, PCOR aims to produce evidence that is meaningful to patients and their treatment decisions, ultimately influencing healthcare policies and practices.[10.1] Furthermore, the integration of social determinants of health (SDoH) into outcomes research is increasingly recognized as essential for understanding health disparities and improving health outcomes. SDoH, which include factors such as socioeconomic status, education, and neighborhood conditions, are estimated to impact approximately 80% of health outcomes.[13.1] Despite their significance, standardized data on SDoH is often lacking in electronic health records, which can limit the effectiveness of research and the quality of care provided.[11.1] Addressing these gaps through comprehensive data collection and analysis is crucial for developing effective interventions and policies that promote health equity.[12.1] Healthcare organizations are actively pursuing person-centered strategies to enhance patient experience, which is defined as the sum of all interactions that shape patient perceptions across the continuum of care.[9.1] This shift towards co-designed healthcare services reflects a broader movement in the healthcare system to prioritize patient and family engagement at all levels, from shared decision-making at the point of care to the development of national healthcare policies.[9.1] Such initiatives are vital for fostering a culture of patient-centeredness that can lead to improved health outcomes and overall well-being. Cost-effectiveness analysis (CEA) plays a vital role in health policy decision-making by providing a formal assessment of the trade-offs involving benefits, harms, and costs associated with various healthcare interventions. It has gained traction among public and private organizations for informing reimbursement decisions, benefit designs, and price negotiations globally.[1.1] CEA aims to quantify the relative costs and benefits of alternative interventions, thereby illuminating potential trade-offs and facilitating discussions on whether the additional resources required for an intervention are justified by the health gains it produces.[1.1] A key metric used in CEA is the incremental cost-effectiveness ratio (ICER), which expresses the trade-off as a "price" for an additional unit of health gained through an intervention.[1.1] However, it is important to recognize that CEA is just one of many factors influencing health policy decisions, and the integration of CEA into policy-making processes presents ongoing challenges.[1.1] As healthcare technologies continue to evolve, the need for careful examination of these trade-offs becomes increasingly critical, highlighting the importance of ongoing dialogue regarding the implications of CEA in shaping effective health policies.[1.1]In this section:
History
Early Developments in Clinical Trials
The early developments in outcomes research can be traced back to significant contributions made by pioneering figures such as Ernest Codman and Florence Nightingale. In 1914, Codman emphasized the necessity for structured assessments of medical interventions, advocating that hospital outcomes should be reported not merely by the number of patients treated but by the actual outcomes achieved for those patients.[45.1] This early recognition of the importance of measuring health outcomes laid the groundwork for future methodologies in outcomes research. Florence Nightingale (1820-1910) was a pioneering figure in nursing whose contributions during the Crimean War in the 1850s significantly advanced medical practices and laid the groundwork for outcomes research. Utilizing her mathematical and statistical knowledge, Nightingale advised the British Army and government on effective medical data collection and management, which led to a substantial reduction in mortality rates among soldiers.[65.1] Her innovative approach included the development of the first statistical pie graph, known as a polar graph, which she employed to communicate her findings effectively.[62.1] The methodologies associated with outcomes research gained prominence during this period, largely due to Nightingale's work, which emphasized the importance of studying end results in healthcare.[48.1] Her transformative impact on medical practices is recognized as a critical foundation for the establishment of a public healthcare system and the nursing profession.[64.1] The term "outcomes research" itself was formally introduced in 1998 by Clancy and Eisenberg, who defined it as the study of health service end results that consider patients' experiences, preferences, and values.[47.1] This definition reflects a shift towards a more patient-centered approach in clinical investigations, emphasizing the measurement of various health outcomes, including symptoms, functional status, and quality of life.[46.1]Recent Advancements
Integration of Big Data Analytics
The integration of big data analytics into healthcare has been significantly influenced by advancements in cloud computing and deep learning technologies, which have transformed medical data analysis and its impact on healthcare delivery and patient outcomes.[102.1] Modern medical practices increasingly rely on data analytics to process vast amounts of information from various sources, including electronic health records (EHRs) and wearable technology, addressing the complexities of healthcare data that exceed the capabilities of traditional statistical tools.[102.1] Furthermore, the incorporation of genomic data into EHRs enhances the accessibility and utilization of genetic information across healthcare settings, thereby promoting personalized medicine approaches.[92.1] This integration allows healthcare providers to formulate tailored treatment plans that align with individual genetic profiles, ultimately improving diagnostic accuracy and treatment efficacy.[92.1] However, challenges remain, such as the interpretation of complex genomic data and ethical concerns related to genetic testing, which necessitate specialized training in genomic medicine for effective implementation in clinical practice.[92.1] Moreover, the incorporation of RWE into clinical trials enhances the robustness of evidence for decision-making in drug development and patient care. By bridging the gap between controlled clinical trials and real-world clinical practice, RWE provides insights into how treatments perform across diverse patient populations and healthcare settings.[95.1] This approach not only accelerates the path to regulatory approval but also ensures that therapies meet the real-world needs of patients.[95.1] The multi-omics approach, which integrates data from various omics technologies, exemplifies the potential of big data analytics in outcomes research. This method allows for the analysis of complex biological data, leading to applications such as cancer classification and biomarker identification.[103.1] Furthermore, the integration of patient-reported outcomes (PROs) into national data sharing infrastructures, as seen in the Patient-Centered Outcomes Research within the Medical Informatics Initiative (PCOR-MII) project, highlights the importance of stakeholder interests in shaping research priorities.[104.1] As healthcare providers increasingly face challenges related to the interpretation of complex genomic data and the ethical implications of genetic testing, the integration of big data analytics offers solutions to enhance diagnostic accuracy and treatment efficacy.[92.1] Overall, the advancements in data integration and analysis are pivotal in prioritizing patient-centered outcomes in health research, ultimately leading to improved healthcare delivery and patient satisfaction.Multi-Omics Approaches
Recent advancements in outcomes research have increasingly focused on multi-omics approaches, particularly the integration of genomic data with artificial intelligence (AI) to enhance personalized medicine. A study highlights the transformative potential of combining deep learning with genomic data, which aims to improve patient-specific treatment outcomes in precision healthcare. This novel framework leverages complex genomic datasets alongside deep learning algorithms, thereby advancing the field of personalized medicine.[89.1] Moreover, AI-driven models that incorporate genomic, clinical, and demographic data have shown high accuracy in predicting treatment outcomes for mental health disorders, such as major depressive disorder and bipolar disorder. This integration not only enhances the precision of treatment plans but also addresses significant challenges in genomic psychiatry, providing strategic directions for the effective use of AI and machine learning (ML) in this domain.[90.1] Next-generation sequencing (NGS) has also played a pivotal role in revolutionizing personalized oncology care. By offering comprehensive cancer profiling, NGS provides exceptional insights into the complex genomic landscape of tumors. This capability enables clinicians and researchers to better understand the molecular basis of cancer, facilitating the development of tailored treatment strategies that align with individual patient profiles.[91.1]Methodologies
Experimental vs. Non-Experimental Designs
Outcomes research utilizes both experimental and non-experimental designs, each with distinct purposes and methodologies. This field, which focuses on patient-oriented outcomes, is crucial for stakeholders such as patients, physicians, healthcare systems, and payers.[122.1] Unlike traditional clinical research, outcomes research measures outcomes from the patient's perspective, incorporating expanded measures like quality of life and cost-effectiveness.[123.1] These methodologies are vital for assessing healthcare interventions and ensuring alignment with the values and needs of stakeholders.[124.1] The choice between experimental and non-experimental designs depends on the study's goals and the nature of the data. Researchers may not always need to address missing data or clustering, as the data structure and research questions guide the selection of statistical models.[127.1] Aligning the research design with the methodology and hypothesis is crucial for ensuring the reliability and quality of research outcomes.[129.1] Selecting appropriate outcome measures during the research design stage is essential, as their validity and reliability significantly influence the methodology.[130.1] Thus, the interplay between experimental and non-experimental designs, along with careful consideration of outcome measures, is fundamental to the success of outcomes research.[124.1]Data Collection Techniques
Data collection techniques in outcomes research encompass a variety of methodologies that aim to ensure the accuracy and reliability of data, particularly in the context of patient-reported outcomes (PROs) and qualitative research. The integration of electronic methods for collecting PRO data has become the preferred approach in clinical research due to its ability to enhance data quality. Electronic patient-reported outcome (ePRO) data collection allows for edit checks that ensure data accuracy and controlled timing of assessments, which are critical for maintaining the integrity of the data collected.[131.1] Furthermore, the use of electronic systems for capturing PRO endpoints has been validated as a best practice, highlighting the advantages of electronic over traditional paper-based methods.[134.1] To address the challenges associated with missing PRO data, a systematic review identified several strategies that can be employed throughout the research process. These include specifying a clinically informative PRO assessment schedule, collecting auxiliary data to facilitate unbiased interpretation, and ensuring that the study design is feasible and adequately resourced.[135.1] Additionally, involving patient partners and trial staff in the design of PRO studies is essential for enhancing the relevance and applicability of the data collected.[135.1] Qualitative research methodologies also play a significant role in outcomes research, necessitating rigorous design and execution to ensure validity. Key strategies for enhancing the rigor of qualitative research include establishing a clear rationale for sampling design decisions, determining data saturation, and employing member checking to validate findings.[137.1] The validity of qualitative research is influenced by a processual approach that considers every stage of the research, from design to data analysis and interpretation.[138.1] Addressing common threats to validity, such as researcher bias and respondent bias, is crucial for obtaining trustworthy results.[139.1]In this section:
Applications
Impact on Patient Care
Outcomes research plays a crucial role in enhancing patient care by focusing on the end results of healthcare practices and interventions. This research employs clinical- and population-based methods to optimize health outcomes, thereby delivering significant benefits and value to stakeholders, including healthcare providers and patients.[159.1] By measuring outcomes from the patient's perspective, outcomes research expands the traditional scope of clinical research to include quality of life and cost-effectiveness, which are essential for understanding the overall impact of healthcare interventions.[161.1] The goals of outcomes research are centered on transforming healthcare delivery and improving patient outcomes by emphasizing the measurement of healthcare attributes, including structure, processes, and outcomes.[162.1] This focus on patient-centered outcomes is vital for ensuring that healthcare practices align with the diverse experiences and preferences of different patient populations. To achieve this, it is essential to utilize appropriate measures of patient-centered care (PCC) tailored to specific populations, which can significantly enhance care quality.[168.1] However, challenges remain in collecting and aggregating data across various care systems, necessitating a transparent and integrated approach to data management within electronic medical records.[167.1] The integration of outcomes research into clinical practice is essential for enhancing the relevance and applicability of clinical findings, as it bridges the gap between clinical research and practical application. While rigorously controlled trials are still important, outcomes research compensates for its limitations in internal validity by providing greater external validity and relevance to clinical practice.[176.1] Effective communication in healthcare is also crucial for improving patient outcomes, as it builds trust, enhances understanding, and ensures that patients are active participants in their care.[180.1] By fostering open, empathetic, and clear communication, healthcare providers can strengthen patient-provider relationships, reduce errors, and promote better health outcomes.[180.1]Role in Health Policy and Decision Making
Outcomes research plays a crucial role in informing health policy and decision-making by integrating data-driven insights into resource allocation and optimization within healthcare systems. The integration of big data and machine learning (ML) in healthcare enhances decision-making processes, improves operational efficiency, and ultimately leads to better patient outcomes.[163.1] Specifically, healthcare data analytics and predictive modeling are pivotal in enhancing outcomes related to resource allocation, disease prevalence, and the identification of high-risk populations.[164.1] However, the effective implementation of outcomes research findings faces several challenges. Policymakers often encounter difficulties in communicating and transmitting research findings, which is essential for evidence-based policy formulation. Political influences can complicate this process, making it challenging to ensure that research insights are effectively utilized in policy decisions.[165.1] Additionally, policies developed at the national level may struggle to maintain consistency at subnational levels, particularly when local governance structures possess varying degrees of political authority.[166.1] The inclusion of patient perspectives in outcomes research significantly influences the design of clinical trials and healthcare policies. Patient-reported outcomes (PROs) provide valuable insights into the physical, functional, and psychological impacts of treatments, thereby informing shared decision-making and influencing healthcare practices.[173.1] Furthermore, patient-centered outcomes research emphasizes the importance of incorporating patient voices throughout the research process, from prioritizing research questions to implementing findings in practice.[174.1] This active engagement of patients not only optimizes clinical trial designs but also enhances the relevance of research outcomes to real-world healthcare challenges.[172.1] Outcomes research also serves as a vital tool in addressing health disparities among diverse populations. By leveraging community-engaged research, which actively includes the voices of affected communities, researchers can better understand and mitigate health disparities.[185.1] The National Institute on Minority Health and Health Disparities (NIMHD) framework highlights the importance of structural interventions that consider various sociocultural determinants and involve multiple stakeholders across sectors, thereby promoting equitable access to healthcare benefits.[186.1] Additionally, integrating social determinants of health (SDOH) into hospital and health system initiatives can further enhance the effectiveness of outcomes research in addressing disparities.[187.1]In this section:
Challenges And Limitations
Ethical Considerations
Patient advocacy groups (PAGs) and patient advocates are integral to addressing ethical considerations in outcomes research. Their involvement is crucial in promoting awareness of clinical research and advocating for patient rights within the research community. By sharing their first-hand experiences and insights into the challenges faced by individuals navigating healthcare systems, PAGs provide a unique perspective that can enhance the ethical framework of research initiatives.[228.1] The Patient-Centered Outcomes Research Institute and Cancer Grand Challenges emphasize the necessity of engaging patient advocates in their research funding programs. Advocates play a vital role throughout the research process, contributing to the formulation of research questions and identifying patient concerns early in the development of research plans. This engagement ensures that ethical considerations are prioritized, as advocates can evaluate whether proposed procedures and inclusion/exclusion criteria are acceptable to patients.[229.1] Their participation not only enriches the research design but also helps safeguard the interests and rights of patients, thereby addressing potential ethical dilemmas that may arise during outcomes research.Variability in Outcomes Measurement
Variability in outcomes measurement presents significant challenges in outcomes research. One of the primary difficulties is the selection of appropriate outcome measures that accurately reflect the effects of interventions on patient care. Researchers often face practical challenges in choosing measures and methods that yield meaningful information, which is essential for improving patient outcomes.[198.1] Additionally, the complexity of interventions, particularly in cognitive pharmacy trials, necessitates careful consideration of various factors that can influence outcomes.[200.1] The measurement of disease-oriented outcomes is frequently favored due to their ease of quantification; however, this approach may not adequately capture the impact on patient-oriented outcomes, which are crucial for informed clinical decision-making.[204.1] Furthermore, the limitations of unsupplemented hospital outcomes data highlight the need for a comprehensive episode-of-care approach that includes data from ambulatory and out-of-hospital settings.[206.1] This broader perspective is essential for understanding the full scope of patient outcomes. Moreover, researchers must navigate the intricacies of causal chains linking interventions to outcomes, which can complicate the design of effective measurement tools.[207.1] The challenge is compounded by the need to address potential biases in study design and data collection methods, which can undermine the reliability of outcomes measurement.[211.1] To mitigate these issues, the incorporation of core outcome sets (COS) has been proposed, which define a minimum set of outcomes to be measured and reported, reflecting the priorities of patients and other stakeholders.[212.1] This approach aims to standardize outcomes measurement and enhance the relevance of research findings to patient care.Future Directions
Innovations in Technology
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing Health Economics and Outcomes Research (HEOR) by offering sophisticated tools for literature screening, data extraction, and summarization. These technologies enhance the analysis of large datasets and improve predictive accuracy in healthcare delivery, providing a distinct advantage over traditional methods.[244.1] However, the integration of AI in outcomes research introduces challenges, such as data quality and algorithmic bias, which can impact the reliability of insights.[241.1] Addressing these issues, recent advancements in AI reliability modeling focus on statistical challenges like out-of-distribution detection and the influence of training sets on model performance.[240.1] To mitigate bias in AI models, researchers have developed techniques to enhance fairness, particularly for underrepresented subgroups, while maintaining overall accuracy.[242.1] This is crucial as ethical concerns related to data quality and bias can significantly affect healthcare outcomes.[241.1] The increasing role of patient-generated health data (PGHD), including data from wearables and mobile health applications, is pivotal in outcomes research. PGHD facilitates early identification of risk factors and proactive interventions, potentially improving health outcomes.[263.1] Wearable health devices, in particular, offer real-time insights that enhance chronic disease management, leading to personalized care and improved patient engagement.[266.1] As these technologies evolve, they will shape the future landscape of outcomes research, necessitating ongoing efforts to understand their impact on health outcomes, costs, and patient satisfaction.[265.1]Potential for Personalized Medicine
The potential for personalized medicine within outcomes research is increasingly recognized as a critical area for future development. Outcomes research, which investigates how patterns of care directly affect patient outcomes, is essential for delivering patient-centered care and improving treatment efficacy.[238.1] The integration of patient-reported outcomes (PROs) into electronic health records (EHRs) has facilitated the systematic collection of symptom data, which is vital for managing post-treatment symptoms and enhancing overall treatment success, adherence, and patient satisfaction.[249.1] As outcomes research evolves, the incorporation of patient-reported outcome measures (PROMs) is expected to play a significant role in personalizing healthcare. PROMs, which capture patients' perspectives on their health status and treatment effects, are increasingly being recognized for their clinical relevance and practical implications in disease management.[237.1] Future research should focus on refining PROMs to ensure they encompass not only validated content but also practical and clinically actionable information that reflects patients' valued goals and concerns.[251.1] Moreover, addressing challenges such as response-shift bias—where patients may report unchanged quality of life despite deteriorating conditions due to adaptive changes—will be crucial for accurately capturing patient experiences.[251.1] Strategies to minimize missing PRO data, such as defining clear eligibility criteria and ensuring the feasibility of PRO assessments, will also enhance the reliability of outcomes research.[252.1]References
[1] Outcomes Research - an overview | ScienceDirect Topics — Outcomes research refers to integrating an evaluation process into standard clinical practice to provide feedback for improving the quality of care, often involving the assessment of treatments already in use to determine their effectiveness in real-world settings.
[2] Outcomes Research - an overview | ScienceDirect Topics — Outcomes research (OR), which is a growing field that combines principles of epidemiology, clinical research, health economics, quality of life assessment, and health policy, is critical in obtaining this evidence.1 Whereas clinical trials aim to generate knowledge on safety and efficacy, OR studies the benefits, risks, and costs that guide
[3] Outcomes Research: An Overview — As Kane (1) points out, outcomes research differs from traditional clinical research primarily in its focus on a broader range of outcomes. Traditional clinical measures of treatment success may still be important in outcomes research, but added to those measures are patient-centered outcomes, such as functioning, well- being, cost-effectiveness, and satisfaction. For example, for a clinical
[5] Outcomes research: an overview - PubMed — Outcomes research is increasingly important in contemporary medicine. Outcomes research differs from traditional clinical research in that outcomes are typically measured from the patient's perspective, and expanded measures of outcome are used, such as quality of life and cost-effectiveness.
[6] What Is Outcomes Research? - Modern Methods of Clinical Investigation ... — The outcomes research agenda focuses on new strategies and methods for making inferences to improve the validity and efficiency of evaluative research. Emphasis is placed on developing a proper balance between non-experimental and experimental methods for making inferences, and exploring the available options.
[7] Outcomes research: what is it and why does it matter? - PubMed — Outcomes research is a broad umbrella term without a consistent definition. However it tends to describe research that is concerned with the effectiveness of public-health interventions and health services; that is, the outcomes of these services.
[8] Understanding the Influence and Impact of Stakeholder Engagement in ... — Understanding the Influence and Impact of Stakeholder Engagement in Patient-centered Outcomes Research: a Qualitative Study - PMC Researchers and Partners Reported that Engaging Patients and Other Stakeholders Had a Multi-faceted Impact on Study Planning and Conduct How engagement shapes PCORI-funded studies: definitions, examples, and frequency of types of impact reported by researcher and partner interview participants Findings confirm earlier analyses of the benefits of patient and other stakeholder engagement in research and that active influence of partners most often improved studies’ feasibility and acceptability.2,6 Findings also offer new insights about how influence happens, show the diversity of influences that stakeholders have, and suggest additional impacts than what has been previously documented, including on engagement approaches.
[9] Patient-Centered Healthcare: From Patient Experience to Human ... — Despite significant variations in patient-centeredness reported globally by various reports, healthcare organizations across the globe have been actively working on various person-centeredness strategies in the pursuit to provide high-quality health outcomes. Patient and family-centered care encompasses “an approach to the planning, delivering, and evaluating health care grounded in mutually beneficial partnerships among healthcare providers, patients, and families.” The primary outcome related to patient-centeredness and patient and family-centered care is patient experience, which is “the sum of all interactions, shaped by an organization’s culture, that influence patient perceptions across the continuum of care.” Future operational patient-centered healthcare models aim to achieve a state of excellence in human experience in healthcare “that is grounded in the experiences of patients & families, members of the healthcare workforce and the communities they serve.” To achieve this optimistic goal, healthcare systems are increasingly moving toward new models of care with co-design and coproduced healthcare services that are shifting the conversation from “What’s the matter with you?” to “What matters to you?” The patient and family engagement across all the levels of a healthcare system, from coproduced shared decision-making at the point of care to co-designed organizational process and national healthcare policy framework, is crucial for improving patient-centeredness across the continuum of the healthcare journey.
[10] Facilitators and Barriers to Patient-Centered Outcomes Research ... — There is growing attention toward engaging patients, community members, and other stakeholders in research to enhance the relevance of findings and accelerate implementation of evidence-based practices. 1 - 3 Patient-centered outcomes research (PCOR) methodology emerged with the purpose of producing evidence that is meaningful to patients and treatment decisions.
[11] Realizing the Potential of Social Determinants Data: A Scoping Review ... — Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality. ... Such advancements are essential for health outcomes research, as they provide a foundation for creating more effective, evidence
[12] PDF — April 2022 REPORT 1 HP-2022-12 Addressing Social Determinants of Health: Examples of Successful Evidence-Based Strategies and Current Federal Efforts Amelia Whitman, Nancy De Lew, Andre Chappel, Victoria Aysola, Rachael Zuckerman, Benjamin D. Sommers KEY POINTS Long-standing health inequities and poor health outcomes remain a pressing policy challenge in the U.S. Studies estimate that clinical care impacts only 20 percent of county-level variation in health outcomes, while social determinants of health (SDOH) affect as much as 50 percent. SDOH include factors such as housing, food and nutrition, transportation, social and economic mobility, education, and environmental conditions. This report provides select examples of the evidence in several of these areas. Building on this evidence base, the U.S. Department of Health and Human Services is taking a multifaceted approach to address SDOH across federal programs through timely and accessible data, integration of public health, health care, and social services, and whole-of-government collaborations, in order to advance health equity, improve health outcomes, and improve well-being over the life course.
[13] Standardizing social determinants of health data: a proposal for a ... — Introduction. Social determinants of health (SDoH) is increasingly recognized as critical factors influencing health outcomes, healthcare utilization, and health disparities. 1 These determinants, encompassing economic stability, education, social and community context, health and healthcare, and neighborhood and built environment, impacting an estimated 80% of health outcomes. 2 The
[21] The Role of Real-World Evidence in Clinical Trials — By integrating RWE into the clinical trial process, researchers and regulators can gain a more comprehensive understanding of a drug's impact, leading to better-informed decisions and, ultimately, more effective and safer treatments for patients. By understanding and leveraging these differences, stakeholders in the healthcare industry can use RWE to complement traditional clinical trial data, leading to more robust and comprehensive evidence for decision-making in drug development and patient care. By incorporating RWE, Notable Labs supports the design of more effective and patient-centered clinical trials, ultimately accelerating the path to regulatory approval and ensuring that therapies meet the real-world needs of patients. By bridging the gap between controlled clinical trials and real-world clinical practice, RWE provides a more comprehensive understanding of how treatments perform across diverse patient populations and healthcare settings.
[23] Real-World Evidence: A Primer - PMC - PubMed Central (PMC) — Real-world evidence (RWE) is clinical evidence on a medical product’s safety and efficacy that is generated using real-world data (RWD) resulting from routine healthcare delivery. Integration of advanced healthcare technology [such as connected devices, analytical methods, artificial intelligence (AI) tools] can help address the data-related challenges to some extent, while also enabling the analysis of RWD to generate real world evidence (RWE) . Generation of RWE depends on the fundamental principle of collecting data under real-world clinical settings from diverse sources, such as healthcare databases, registries, claims databases, health-related data from mobile devices, social media, and patient platforms . Registries usually comprise standardised, continuous, prospective data collection in a real-world setting, where treatment and care management is at the discretion of patients and healthcare providers rather than a study protocol. https://rwe-navigator.eu/use-real-world-evidence/sources-of-real-world-data/patient-registries/.
[45] Outcomes Research: A Methodologic Review - ScienceDirect — History and evolution of outcomes research Outcomes research predates RCTs. The first suggestion for the need of a structured assessment of medical interventions was made by Codman in 1914 , who noted that hospital outcomes should not be reported solely according to the number of patients treated, but instead should address those that
[46] Outcomes research: a review - PubMed — Purpose: The purpose of this article is to review the history of the medical outcomes movement as well as the methodologies used in outcomes research. Concept: Outcomes research refers to a genre of clinical investigation that emphasizes the measurement of patient health outcomes, including the patient's symptoms, functional status, quality of life, satisfaction with treatment, and health care
[47] Outcomes Research: What It Is, What It's Not, and Why It's Important ... — The term "outcomes research" was first introduced in 1998 by Clancy and Eisenberg in their article in Science, which stated that "outcomes research - the study of the end results of health services that takes patients' experiences, preferences, and values into account - is intended to provide scientific evidence relating to decisions made
[48] Outcomes research - Wikipedia — Outcomes research is a branch of public health research which studies the end results ... Although the exact origins of the term "outcomes research" is unclear, the methods associated with outcomes research first gained wide attention in the 1850s as a result of the work of Florence Nightingale during the Crimean War. Nightingale studied death
[62] Florence Nightingale's Environmental Theory and its influence on ... — Florence Nightingale (1820-1910), who died over a century ago, was a true explorer into uncharted territory. ... A lifelong interest in collecting data and tabulating outcomes led Nightingale to develop the first statistical pie graph, known as a polar graph, ... Nursing Research focusing on the Impact of Healthcare Environments. Health
[64] Florence Nightingale and the early origins of evidence-based nursing — Florence Nightingale is now a much ignored historical figure. The publication of the Collected Works of Florence Nightingale by Wilfred Laurier University Press, beginning in 2001, will outline her enormous contribution not only to the foundation of the nursing profession but also to the establishment of a public healthcare system. The Collected Works will include her published works and many
[65] Florence Nightingale (1820-1910): An Unexpected Master of Data — Florence Nightingale is known for her nursing skills in the Crimean War. This article shows how she used her mathematical and statistical knowledge to advise the British Army and government on the best approaches for medical data collection and management, thus significantly reducing mortality rates. ... Her criticism of army medical data and
[89] Enhancing Patient-Specific Treatment Outcomes: Leveraging Deep Learning ... — This study explores the transformative potential of integrating deep learning with genomic data to enhance patient-specific treatment outcomes in personalized medicine. By leveraging advancements in artificial intelligence (AI), we present a novel framework that synergizes complex genomic datasets with deep learning algorithms to advance precision healthcare.
[90] Challenges and prospects in bridging precision medicine and artificial ... — AI-driven models integrating genomic, clinical, and demographic data demonstrated high accuracy in predicting treatment outcomes for major depressive disorder and bipolar disorder. This study also examines the pressing challenges and provides strategic directions for integrating AI and ML in genomic psychiatry, highlighting the importance of
[91] From Genomic Exploration to Personalized Treatment: Next ... - PubMed — Next-generation sequencing (NGS) has revolutionized personalized oncology care by providing exceptional insights into the complex genomic landscape. NGS offers comprehensive cancer profiling, which enables clinicians and researchers to better understand the molecular basis of cancer and to tailor treatment strategies accordingly.
[92] Integrating Genomics into Clinical Practice: Recommendations and Challenges — Healthcare providers face numerous obstacles, including the interpretation of complex genomic data, ethical dilemmas related to genetic testing, concerns about patient privacy, and the need for specialized training in genomic medicine. Moreover, integrating genomic data into Electronic Health Records (EHRs) facilitates seamless access and utilization of genetic information across different healthcare settings, promoting continuity of care and personalized medicine approaches. These systems integrate genomic information with clinical guidelines and evidence-based practices, assisting clinicians in formulating personalized treatment plans tailored to individual genetic profiles. By implementing these strategies, healthcare providers can effectively integrate genomics into routine clinical practice, ultimately improving diagnostic accuracy, treatment efficacy, and patient outcomes in personalized medicine.
[95] The Role of Real-World Evidence in Clinical Trials — By integrating RWE into the clinical trial process, researchers and regulators can gain a more comprehensive understanding of a drug's impact, leading to better-informed decisions and, ultimately, more effective and safer treatments for patients. By understanding and leveraging these differences, stakeholders in the healthcare industry can use RWE to complement traditional clinical trial data, leading to more robust and comprehensive evidence for decision-making in drug development and patient care. By incorporating RWE, Notable Labs supports the design of more effective and patient-centered clinical trials, ultimately accelerating the path to regulatory approval and ensuring that therapies meet the real-world needs of patients. By bridging the gap between controlled clinical trials and real-world clinical practice, RWE provides a more comprehensive understanding of how treatments perform across diverse patient populations and healthcare settings.
[102] Recent Advances in Big Medical Image Data Analysis Through Deep ... — : This comprehensive study investigates the integration of cloud computing and deep learning technologies in medical data analysis, focusing on their combined effects on healthcare delivery and patient outcomes. Modern medical procedures are increasingly driven by data analytics, processing massive volumes of information from diverse sources, including wearable technology, genetic analysis, and EHRs. The complexity and exponential increase in healthcare data are beyond the capacity of traditional statistical tools and require sophisticated computational techniques driven by cloud computing infrastructures and deep learning . These contributions provide healthcare organizations and researchers with actionable insights for implementing deep learning and cloud computing solutions in medical data analysis. Shakor, M.Y.; Khaleel, M.I. Recent Advances in Big Medical Image Data Analysis Through Deep Learning and Cloud Computing.
[103] Multi-omics data integration and analysis pipeline for precision ... — The multi-omics approach involves integrating data from different omics technologies, such as DNA sequencing, RNA sequencing, mass spectrometry, and others, using computational methods and then analyzing the integrated result for different downstream analysis applications such as survival analysis, cancer classification, or biomarker identification. This study aims to give an overview of the multi-omics analysis pipeline, starting with the most popular multi-omics databases used in recent literature, dimensionality reduction techniques, details the different types of data integration techniques and their downstream analysis applications, describes the most commonly used evaluation metrics, highlights the importance of model interpretability, and lastly discusses the challenges and potential future work for multi-omics data integration in precision medicine.
[104] Integrating the Patient Perspective into Healthcare and Real-World ... — This paper presents the Patient-Centered Outcomes Research within the Medical Informatics Initiative (PCOR-MII) project, focusing on the integration of patient-reported outcomes (PROs) into a large-scale national data sharing infrastructure, established in Germany by the Medical Informatics Initiative (MII). PCOR-MII aims to systematically address the interests of various stakeholders in
[122] What Is Outcomes Research? - Modern Methods of Clinical Investigation ... — The outcomes research agenda focuses on new strategies and methods for making inferences to improve the validity and efficiency of evaluative research. Emphasis is placed on developing a proper balance between non-experimental and experimental methods for making inferences, and exploring the available options.
[123] Outcomes research: an overview - PubMed — Outcomes research differs from traditional clinical research in that outcomes are typically measured from the patient's perspective, and expanded measures of outcome are used, such as quality of life and cost-effectiveness. ... such as quality of life and cost-effectiveness. In thi … Outcomes research: an overview ORL J Otorhinolaryngol Relat
[124] Outcomes research: methods and implications - PubMed — Outcomes research is a life sciences field that focuses on patient-oriented outcomes, which are important to a wide range of stakeholders, including patients, physicians, health care systems and payers, and society. ... Outcomes research: methods and implications Semin Respir Crit Care Med. 2010 Feb;31(1):3-12. doi: 10.1055/s-0029-1246281. Epub
[127] Some Old and Some New Statistical Tools for Outcomes Research — Cardiology outcomes researchers will not always need to impute missing data, account for clustering, use propensity scores or find instruments, or undertake a multiple-informant analysis. The overall goal of the study and the structure of the data should guide outcomes researchers to an appropriate statistical model. Is the goal predictive or
[129] Comprehensive guidelines for appropriate statistical analysis methods ... — Aligning the chosen method with the specifics of the research design and hypothesis is paramount, as it can significantly impact the reliability and quality of the research outcomes. Methods: This study explores a comprehensive guideline for systematically choosing appropriate statistical analysis methods, with a particular focus on the
[130] Research Outcome Measures - SpringerLink — The selection of outcome measures should be undertaken at a research design stage and should fit with the methodology, aims, and objectives of the research. There are many factors influencing the choice of outcome measure to report results and is important to know whether the chosen outcome measure is valid and reliable, researchers are
[131] Best Practice Recommendations for Electronic Patient-Reported Outcome ... — Today, the preferred mode for the collection of patient-reported outcome (PRO) data in clinical research is electronic. 1 This preference is largely driven by the enhancements to data quality that electronic PRO (ePRO) data collection affords, such as edit checks to ensure data accuracy and controlled timing of assessments. These quality enhancements are compromised by the use of inconsistent
[134] Training on the Use of Technology to Collect Patient-Reported Outcome ... — Electronic capture of patient-reported outcome (PRO) data has many advantages over paper-based data collection. ... Training on the Use of Technology to Collect Patient-Reported Outcome Data Electronically in Clinical Trials: Best Practice Recommendations from the ePRO Consortium Ther Innov Regul Sci. 2019 Jul;53(4)
[135] The importance of patient-reported outcomes in clinical trials and ... — A large systematic review of strategies for minimizing the problem of missing PRO data revealed how the whole trial team can actively reduce the problems of missing PRO data across all research stages.55 The strategies during study design include: specifying a clinically informative and feasible PRO assessment schedule with defined acceptable time windows and stopping rules, collecting auxiliary data (clinically relevant variables that are likely to be correlated to PRO data and recorded at the same time points) to facilitate unbiased interpretation of PRO data in the presence of missing data and inform statistical imputation of missing PRO data, specifying clear eligibility criteria for the PRO study including literacy and language requirements and the need for a valid baseline PRO assessment, ensuring that the PRO study is feasible and adequately resourced, ensuring the mode of questionnaire administration is feasible and acceptable, minimizing participant burden, selecting a clinically relevant and validated PRO measure, incorporating PROs into all relevant trial documents, involving patient partners and trial staff in the design of PRO studies, ensuring that the trial team is committed to the PRO study, developing quality assurance procedures, ensuring that the PRO sample size is representative and sufficient for planned analyses, and involving a multidisciplinary team into PRO study design.55
[137] A Review of the Quality Indicators of Rigor in Qualitative Research — Specific best practice methods used in the sampling and data collection processes to increase the rigor and trustworthiness of qualitative research include: clear rationale for sampling design decisions, determination of data saturation, ethics in research design, member checking, prolonged engagement with and persistent observation of study participants, and triangulation of data sources.28 The strength of conclusions is dependent upon the extent to which standards of rigor and best practices were demonstrated in design, data collection, data analysis, and interpretation, as described in previous sections of this article.4,12,17,23,24 Quality and rigor expectations for drawing valid conclusions and generating new theories are reflected in the following essential features of rigor and quality, which include: “Close the loop” to clearly link research questions, study design, data collection and analysis, and interpretation of results.
[138] Processual Validity in Qualitative Research in Healthcare — Qualitative validity is also too important and rich to be based on only a few fixed measures at the end of the study, such as Cronbach's alpha among the quantitative research methods. 21,22 Every stage of qualitative research is significant, and a processual approach to ensure validity can influence the quality of all stages. 13,14 The
[139] 6 strategies to increase Validity in Qualitative Research - LinkedIn — 6 strategies to increase Validity in Qualitative Research [Skip to main content](https://www.linkedin.com/pulse/6-strategies-increase-validity-qualitative-research-jaroslaw-kriukow#main-content) 6 strategies to increase Validity in Qualitative Research What are Validity and Reliability in Qualitative research? What seems more relevant when discussing qualitative studies is their validity, which very often is being addressed with regard to three common threats to validity in qualitative studies, namely researcher bias, reactivity and respondent bias (Lincoln and Guba, 1985). It may, however, pose a threat in the form of researcher bias that stems from your, and the participants’, possible assumptions of similarity and presuppositions about some shared experiences (thus, for example, they will not say something in the interview because they will assume that both of you know it anyway – this way, you may miss some valuable data for your study).
[159] Outcomes research: origins, relevance, and potential impacts for ... — Overview of Outcomes Research. Outcomes research entails the application of clinical- and population-based research methods to optimize the end results of health-care practices and interventions, delivering benefits and value to stakeholders. 1 Widely used in human health care, outcomes research principles can assist health-care providers and their patients in making decisions regarding
[161] Outcomes research: an overview - PubMed — Outcomes research: an overview - PubMed Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation Search: Search Your saved search Name of saved search: Add to Search Add to Search Outcomes research differs from traditional clinical research in that outcomes are typically measured from the patient's perspective, and expanded measures of outcome are used, such as quality of life and cost-effectiveness. Outcomes research consists of the clinical study of expanded, patient-based outcomes, as well as the study of populations, databases, and the delivery of health care. Search in MeSH Add to Search Add to Search Add to Search Health Services Research* Add to Search Add to Search Add to Search Add to Search Add to Search Add to Search
[162] Outcomes research: science and action - PMC - PubMed Central (PMC) — 3. Goals of outcomes research. Outcomes research holds the potential and promise to help transform health care delivery and patient outcomes by focusing on the "end results or outcomes" of health care. Health care can be characterized and measured by attributes including structure, processes, and outcomes.
[163] Big Data and Machine Learning for Healthcare Resource Allocation and ... — This paper explores the integration of big data and ML in healthcare resource allocation and optimization, focusing on how these technologies enable data-driven decision-making, improve operational efficiency, and enhance patient outcomes. ... This paper concludes with insights into future directions for research and practice in leveraging big
[164] Healthcare Data Analytics and Predictive Modelling: Enhancing Outcomes ... — (PDF) Healthcare Data Analytics and Predictive Modelling: Enhancing Outcomes in Resource Allocation, Disease Prevalence and High-Risk Populations Healthcare Data Analytics and Predictive Modelling: Enhancing Outcomes in Resource Allocation, Disease Prevalence and High-Risk Populations Purpose: This study aims to explore the role of healthcare data analytics and predictive modeling in enhancing healthcare outcomes, specifically in resource allocation, disease forecasting, and identifying high-risk populations. Purpose: This study aims to explore the role of healthcare data analytics and predictive modeling in enhancing healthcare outcomes, specifically in resource allocation, disease forecasting, and identifying high-risk populations. The implementation of Big Data Analytics (BDA) in healthcare has significant contribution in ameliorating patient care with effective cost, predictive analysis of diseases, improving value in healthcare organizations and accelerating medical research with low costs.
[165] Full article: Evidence-based policymaking: promise, challenges and ... — 5.2. Ideas on how to aggregate policy-relevant research findings. The transmission and communication of research findings to policymakers is an important part of evidence-based policy. As discussed in Section 4.5, this process faces several major challenges, including political influences.
[166] Policy failure and the policy-implementation gap: can policy support ... — 2.2. Implementation in dispersed governance. Policies formulated at national level may face the challenge of ensuring some degree of consistency in delivery at subnational level, a process that is especially fraught where the subnational level has some separate degree of political authority (Norris et al. Citation 2014).Sausman et al. (Citation 2016) draw on the concept of "local
[167] The Challenges of Collecting and Using Patient Care Data From Diverse ... — The challenges are divided into those needed for (1) collecting similar data, (2) aggregating those data across care systems, and (3) using the data to both improve and evaluate care. Start with agreement on goals, methods, transparency, and a data system integrated into the electronic medical record while promptly addressing all the legal
[168] (PDF) Measuring patient-centered care for specific populations: A ... — Utilising appropriate measures of PCC for specific patient populations is crucial to improving care quality. As such, there has been a proliferation of tools to measure patient experience for
[172] Patient-centred clinical trial design | Nature Reviews Nephrology — Patient-centred clinical trial design | Nature Reviews Nephrology However, efforts to involve patients with kidney disease are increasing across all stages of the trial process from priority setting, to study design (including selection of outcomes and approaches to improve participant recruitment and retention) and dissemination and implementation of the findings. Patients can be involved in research priority setting, study design (including participant recruitment and retention and selection of outcomes, including patient-reported outcomes), as well as the dissemination and implementation of trial findings. Models and impact of patient and public involvement in studies carried out by the Medical Research Council Clinical Trials Unit at University College London: findings from ten case studies.
[173] The impact of patient-reported outcome data from clinical trials ... — Patient-reported outcomes (PROs) are increasingly collected in clinical trials as they provide unique information on the physical, functional and psychological impact of a treatment from the patient's perspective. Recent research suggests that PRO trial data have the potential to inform shared decision-making, support pharmaceutical labelling claims and influence healthcare policy and practice.
[174] PDF — Patient-Centered Outcomes Research requires that the patient's voice and perspective drive every step of the research process, including prioritizing the research questions, designing and conducting the research, and implementing the results in practice. However, the best approach to select patients (or their surrogates, or caregivers)
[176] Applying Effectiveness and Outcomes Research to Clinical Practice — Outcomes research is an opportunity to integrate clinical research and clinical practice . Obviously, there will still be a place for rigorously controlled trials. What outcomes research gives up in terms of internal validity it more than makes up for in enhanced external validity and relevance to clinical practice.
[180] Effective Communication for Better Patient Outcomes — Effective communication in healthcare is essential for improving patient outcomes. It builds trust, enhances understanding, and ensures that patients are active participants in their care. By fostering open, empathetic, and clear communication, healthcare providers can create stronger patient-provider relationships, reduce errors, and promote
[185] Community engaged research can improve health equity, outcomes — The work is complicated, but over the past three decades, numerous studies have demonstrated that community-engaged research reduces health disparities and improves public health outcomes. What matters most in community-engaged research is seeing it put into practice, which generally means including the voices of those affected by the research, says Kimberly Parker, an Atlanta-based public health strategist. Another challenge to successful community-engaged public health research is making sure the community representation reflects the affected community, says Glenn Ellis, a Philadelphia-based medical ethicist, equity consultant, and bioethics research fellow at the Harvard Medical School Center for Bioethics. Accordingly, in 2023 the Crime Lab launched its Community Violence Intervention Leadership Academy, a first-of-its-kind program to educate and train leaders of public health and nonprofit violence intervention programs on techniques the Crime Lab has developed through its research.
[186] Structural Interventions to Reduce and Eliminate Health Disparities — The National Institute on Minority Health and Health Disparities (NIMHD) Minority Health and Health Disparity Research Framework can guide structural interventions by emphasizing multiple domains of conceptual constructs that may be relevant to the development of structural interventions such as physical and built environments, sociocultural determinants, and multiple levels of influences in addressing health disparities.7 Furthermore, the most promising interventions should involve diverse stakeholders from multiple sectors, such as criminal justice, education, transportation, housing, business, and other social services, in addition to the health care system.8 For more information on designing, conducting, and analyzing multilevel structural interventions, see the multilevel intervention analytic essay by Agurs-Collins et al.
[187] Impact of hospital and health system initiatives to address Social ... — Impact of hospital and health system initiatives to address Social Determinants of Health (SDOH) in the United States: a scoping review of the peer-reviewed literature | BMC Health Services Research | Full Text Building on prior research characterizing these efforts (Part 1), this scoping review (Part 2) evaluates the effectiveness of these initiatives, with a focus on SDOH data integration, EHR utilization, and the broader scope of hospital efforts in addressing individual- and system-level determinants of health. The earlier article (Part 1) was the first systematic scoping review of hospital and health system initiatives addressing SDOH in the U.S. It made two key contributions: (1) creating a framework to identify the types of SDOH that hospitals and health systems should address and (2) developing a comprehensive model to characterize these efforts.
[198] Challenges in Outcome Measurement: Clinical Research Perspective — Choosing measures and methods for clinical outcome research to produce meaningful information that may be used to improve patient care presents a number of challenges. ... Practical challenges to wide-scale outcome measurement exist, ... resource limitations may be addressed by repurposing and redesigning the existing resources to increase
[200] The challenges of outcome research - PubMed — The challenges of outcome research Int J Clin Pharm. 2016 Jun;38(3):705-8. doi: 10.1007/s11096-016-0293-6. Epub 2016 Apr 2. Authors ... Several factors must be taken into consideration when conducting outcome research-particularly within cognitive pharmacy trials. The interventions are often complex and non-specific, and seek to improve symptom
[204] Understanding Commonly Encountered Limitations in Clinical Research: An ... — Disease-oriented outcomes are often favored in clinical research because they are easier to measure. Examining disease-oriented outcomes may be a necessary step in understanding pharmacology or pathophysiology, but ideally, clinical decisions should be supported by studies demonstrating an effect on patient-oriented outcomes.
[206] Promise and Limitations of Effectiveness and Outcomes Research — The limitations of unsupplemented hospital outcomes data were particularly striking. The core committee recognized the clear need for an episode-of-care approach to analyzing the outcomes of care, which among other things calls for appreciable efforts to collect ambulatory and other out-of-hospital information, including posthospitalization
[207] Challenges to evaluating complex interventions: a content analysis of ... — Having established what outcome(s) an intervention is aiming to achieve, researchers face challenges in designing tools to effectively measure outcomes, understanding 'the length and complexity of the causal chains linking intervention with outcome' , explaining discrepancies between expected and observed outcomes, and capturing the long
[211] Identifying and Avoiding Bias in Research - PMC — Sources of pre-trial bias include errors in study design and in patient recruitment. Recall bias is most likely when exposure and disease status are both known at time of study, and can also be problematic when patient interviews (or subjective assessments) are used as a primary data sources. A study's internal validity reflects the author's and reviewer's confidence that study design, implementation, and data analysis have minimized or eliminated bias and that the findings are representative of the true association between exposure and outcome. An ideal trial design would randomize patients and blind those collecting and analyzing data (high internal validity), while keeping exclusion criteria to a minimum, thus making study and source populations closely related and allowing generalization of results (high external validity) 34.
[212] Patient participation impacts outcome domain selection in core outcome ... — Objectives: The importance of including patients, carers, and the public in health research is well recognized, including the need to consider outcomes in health care research that reflect the priorities of patients. Core outcome sets (COS) define the minimum set of outcomes that should be measured and reported in research of a given condition, determined through consensus among key stakeholders.
[228] Perspective of Patient Advocacy Groups on Clinical Research and ... — Patient advocacy groups (PAGs) and/or patient advocates play an important role in promoting awareness of clinical research and advocating for the rights of patients within the research community. Their first-hand experiences and knowledge of the challenges faced by individuals navigating healthcare systems lend a unique and valuable perspective
[229] Patient Advocates and Researchers as Partners in Cancer Research: A ... — The Patient-Centered Outcomes Research Institute and Cancer Grand Challenges require the engagement of patient advocates for their research funding programs.14 Advocates can contribute at all steps in the process, and they can help formulate the research questions and identify patient concerns early in development of a research plan.1 For clinical trial proposals, advocates can evaluate if a procedure or inclusion and exclusion criteria would be acceptable to patients.
[237] Why measuring outcomes is important in health care - PMC — Doctors' understanding of the effect of disease and treatment on patients' daily lives is poor.28 Thus, hundreds of standardized measures have been developed to capture patient‐reported outcomes.28 These patient‐reported outcome measures (PROMs) are measurements based on a report that comes directly from the patient about the status of particular aspects of or events related to a patient's clinical condition.29 Two important characteristics of PROMs are: (i) they are clinically meaningful with practical implications for disease recognition and management and (ii) they include reporting of outcomes based on a patient's unique perspective (eg, patient‐reported pain scale).29 The PROMs are powerful assessment tools because by using validated questionnaires, clinical signs could be turned into numerical scores that would describe, for example, how much a knee replacement helps a person walk or to quantify the average difference in outcome between a biologic treatment and traditional pharmacotherapy.26, 27 Patient‐reported outcome measures are essential for real‐time clinical care and for how doctors measure, compare, and improve care as a system.26 Making PROMs an integral part of clinical practice leads to better communication and decision‐making between doctors and patients, improving patient satisfaction and allowing doctors to provide better care at the individual patient's level as well as in aggregate for the population.26, 28, 30 The information gathered can bridge the gap between the clinical reality and the patient's world, triggering learning as well as the correct next action.28
[238] Outcomes Research - an overview | ScienceDirect Topics — Future Directions. Outcomes research in hypertension, as in many other disease states, has helped to elucidate some of the potential roadblocks to effective treatment. It is a form of research that investigates how patterns of care directly affect patient outcomes. As practitioners endeavor to deliver the most patient-centered care, the results
[240] Statistical Perspectives on Reliability of Artificial Intelligence Syste — transformed for reliability modeling and assessment of AI systems. We also describe recent developments in modeling and analysis of AI reliability and outline statistical research challenges in this area, including out-of-distribution detection, the effect of the training set, adversarial attacks, model accuracy, and uncertainty
[241] AI Data Quality and Bias: Challenges, Implications, and Solutions in ... — (PDF) AI Data Quality and Bias: Challenges, Implications, and Solutions in Modern Machine Learning AI Data Quality and Bias: Challenges, Implications, and Solutions in Modern Machine Learning The exponential growth of Artificial Intelligence (AI) applications across industries has highlighted the critical importance of data quality and bias mitigation in machine learning systems. This comprehensive review examines the intricate relationship between data quality, algorithmic bias, and AI system performance, presenting both theoretical frameworks and practical implications. The paper introduces a novel taxonomical framework for categorizing AI systems' data quality challenges and bias types, facilitating more targeted intervention strategies. Keywords:artificial intelligence, data quality, algorithmic bias, machine learning, ethical AI, ○ Bias detection systems The implications of poor data quality and bias in AI systems raise significant ethical concerns
[242] Researchers reduce bias in AI models while preserving or improving accuracy — Researchers reduce bias in AI models while preserving or improving accuracy | MIT News | Massachusetts Institute of Technology MIT researchers developed an AI debiasing technique that improves the fairness of a machine-learning model by boosting its performance for subgroups that are underrepresented in its training data, while maintaining its overall accuracy. MIT researchers developed a new technique that identifies and removes specific points in a training dataset that contribute most to a model’s failures on minority subgroups. There are specific points in our dataset that are contributing to this bias, and we can find those data points, remove them, and get better performance,” says Kimia Hamidieh, an electrical engineering and computer science (EECS) graduate student at MIT and co-lead author of a paper on this technique.
[244] Applications of Artificial Intelligence and Machine Learning in Health ... — OBJECTIVES: Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing Health Economics and Outcomes Research (HEOR) by providing powerful tools for screening, extracting, and summarizing literature, analyzing large datasets, improving predictive accuracy, and optimizing healthcare delivery.
[249] Use of Patient-Reported Outcomes in Risk Prediction Model Development ... — The integration of patient-reported outcomes (PROs) into electronic health records (EHRs) has enabled systematic collection of symptom data to manage post-treatment symptoms. The use and integration of PRO data into routine care are associated with overall treatment success, adherence, and satisfaction.
[251] Patient-reported outcome measures and the evaluation of services — Additionally, the boundary between PROMs and patient experience may also need to be relaxed; when, for example, patients are capable of giving meaningful answers to validated questionnaires retrospectively judging the outcomes and benefits of treatments.27–29 An ongoing problem involves the phenomenon of response-shift bias, which occurs when a patient with a deteriorating condition nevertheless reports an unchanged quality of life owing to successful adaptation and a shift in the thresholds they themselves use to describe the severity or impact of a problem.
[252] The importance of patient-reported outcomes in clinical trials and ... — A large systematic review of strategies for minimizing the problem of missing PRO data revealed how the whole trial team can actively reduce the problems of missing PRO data across all research stages.55 The strategies during study design include: specifying a clinically informative and feasible PRO assessment schedule with defined acceptable time windows and stopping rules, collecting auxiliary data (clinically relevant variables that are likely to be correlated to PRO data and recorded at the same time points) to facilitate unbiased interpretation of PRO data in the presence of missing data and inform statistical imputation of missing PRO data, specifying clear eligibility criteria for the PRO study including literacy and language requirements and the need for a valid baseline PRO assessment, ensuring that the PRO study is feasible and adequately resourced, ensuring the mode of questionnaire administration is feasible and acceptable, minimizing participant burden, selecting a clinically relevant and validated PRO measure, incorporating PROs into all relevant trial documents, involving patient partners and trial staff in the design of PRO studies, ensuring that the trial team is committed to the PRO study, developing quality assurance procedures, ensuring that the PRO sample size is representative and sufficient for planned analyses, and involving a multidisciplinary team into PRO study design.55
[263] Using Patient-Generated Health Data for Improved Outcomes — Research suggests that patient-generated health data (PGHD) — data created, captured, and recorded by patients between appointments — has the potential to improve health outcomes. Providers can identify risk factors early and intervene proactively by leveraging remote device-generated clinical data, health history, social determinants of
[265] Patient generated health data use in clinical practice: A ... - PubMed — PGHD and other types of patient-reported data will be part of the health care system narrative and we must continue efforts to understand its impact on health outcomes, costs, and patient satisfaction. Nursing scientists need to lead the process of defining the role of PGHD in the era of precision h …
[266] The Role of Wearable Devices in Chronic Disease Monitoring and Patient ... — In summary, wearable health devices have great potential for improving the monitoring and management of chronic diseases by providing real-time insights and personalized interventions for patients. The research question proposed in our review article is: How effective are wearable health devices in monitoring chronic diseases, and how do they improve patient outcomes and prognosis? Overall, wearables enhance the diagnosis and management of respiratory conditions by providing continuous, real-time data on vital signs, activity levels, and environmental factors, supporting personalized care, improving patient engagement, and leading to better health outcomes for individuals with COPD and asthma . The integration of wearable devices into cancer care represents a significant advancement in the early detection, monitoring, and diagnosis of the disease, with the potential to improve patient outcomes, personalize treatment plans, and revolutionize the approach to clinical trials.