Wayne Kuang, Eric J. Yang, Roland Truong, Benjamin K.P. Woo
Virtual reality (VR) stands as an innovative technology transforming our interactions with the digital world. Its integration into health care has proven advantageous for both patients and health care providers across multiple levels and modalities. Given that VR is becoming increasingly accessible and prevalent, health care providers should explore incorporating the technology into their practices, particularly within the pediatric population, which is becoming progressively more accustomed to the technology. This topic synopsis provides a broad discussion of the current literature, exploring current and probable future applications of VR in pediatric patient care, particularly in improving the hospital experience, facilitating education during hospitalizations, providing an alternative to pharmacological therapy for pain management, and enhancing mental health care practices. ( J Patient Cent Res Rev. 2024;11:107-111
{"title":"Bringing Virtual Reality to Mainstream Pediatric Care","authors":"Wayne Kuang, Eric J. Yang, Roland Truong, Benjamin K.P. Woo","doi":"10.17294/2330-0698.2063","DOIUrl":"https://doi.org/10.17294/2330-0698.2063","url":null,"abstract":"Virtual reality (VR) stands as an innovative technology transforming our interactions with the digital world. Its integration into health care has proven advantageous for both patients and health care providers across multiple levels and modalities. Given that VR is becoming increasingly accessible and prevalent, health care providers should explore incorporating the technology into their practices, particularly within the pediatric population, which is becoming progressively more accustomed to the technology. This topic synopsis provides a broad discussion of the current literature, exploring current and probable future applications of VR in pediatric patient care, particularly in improving the hospital experience, facilitating education during hospitalizations, providing an alternative to pharmacological therapy for pain management, and enhancing mental health care practices. ( J Patient Cent Res Rev. 2024;11:107-111","PeriodicalId":16724,"journal":{"name":"Journal of Patient-Centered Research and Reviews","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141640872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cruising Speed: Our Journal's 10-Year Voyage","authors":"D. Baumgardner, Joe Grundle","doi":"10.17294/2330-0698.2080","DOIUrl":"https://doi.org/10.17294/2330-0698.2080","url":null,"abstract":"","PeriodicalId":16724,"journal":{"name":"Journal of Patient-Centered Research and Reviews","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Abstracts From the 2024 Health Care Systems Research Network (HCSRN) Annual Conference, Milwaukee, Wisconsin","authors":"Health Care Systems Research Network","doi":"10.17294/2330-0698.2105","DOIUrl":"https://doi.org/10.17294/2330-0698.2105","url":null,"abstract":"","PeriodicalId":16724,"journal":{"name":"Journal of Patient-Centered Research and Reviews","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Denise D. Quigley, Marc N. Elliott, Nabeel Qureshi, Zachary Predmore, Ron D. Hays
{"title":"How the CAHPS Clinician and Group Patient Experience Survey Data Have Been Used in Research: A Systematic Review","authors":"Denise D. Quigley, Marc N. Elliott, Nabeel Qureshi, Zachary Predmore, Ron D. Hays","doi":"10.17294/2330-0698.2056","DOIUrl":"https://doi.org/10.17294/2330-0698.2056","url":null,"abstract":"","PeriodicalId":16724,"journal":{"name":"Journal of Patient-Centered Research and Reviews","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicholas Sommers, Jason C. Rubenstein, Abdur Rahman Ahmad, James Oujiri, Ridhima Kapoor, Graham Adsit, Marcie Berger
{"title":"Implementation of a Patient Decision Aid for Atrial Fibrillation Ablation Improves Patient Procedural Knowledge but Does Not Impact Perceived Involvement With the Shared Decision-Making Process","authors":"Nicholas Sommers, Jason C. Rubenstein, Abdur Rahman Ahmad, James Oujiri, Ridhima Kapoor, Graham Adsit, Marcie Berger","doi":"10.17294/2330-0698.2055","DOIUrl":"https://doi.org/10.17294/2330-0698.2055","url":null,"abstract":"","PeriodicalId":16724,"journal":{"name":"Journal of Patient-Centered Research and Reviews","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michael J. Williams, Sol Atienza, Renee H. Aranda, Kayleigh B. Flint, S. Sana, S. C. Medlin, Zartash Gul, Federico A. Sanchez, Michael A. Thompson
the form of intrathecal (IT) chemotherapy and/or high-dose intravenous (IV) methotrexate. CNS-IPI scores were calculated for all patients who received CNS prophylaxis or those who experienced CNS disease. Long-term outcomes at five years from diagnosis included CNS progression/relapse and survival. Results Of 234 patients who met criteria, 20 (8.6%) received either IV methotrexate or IT chemotherapy; most received IT methotrexate. No patients in the IT prophylaxis group developed CNS disease, while two of eight IV methotrexate patients experienced CNS disease involvement. The incidence of CNS progression was 3.7% in the no prophylaxis group and 10% in those who received prophylaxis. Conclusions This study revealed low utilization of CNS prophylaxis and CNS-IPI documentation in a community hospital system. Given large differences between groups, claims of CNS prophylaxis efficacy are unable to be made. CNS relapse rates were consistent with existing literature and promote continued evaluation of the utility of current CNS prophylaxis approaches in DLBCL. New unambiguously effective therapeutic approaches are needed and may encourage a higher rate of standardized use. ( J Patient Cent Res Rev. 2024;11:81-87.)
鞘内化疗和/或大剂量静脉注射甲氨蝶呤。对所有接受中枢神经系统预防治疗或出现中枢神经系统疾病的患者计算中枢神经系统-IPI评分。诊断后五年的长期结果包括中枢神经系统疾病进展/复发和存活率。结果 在符合标准的 234 名患者中，20 人（8.6%）接受了静脉注射甲氨蝶呤或 IT 化疗；大多数人接受了 IT 甲氨蝶呤。IT预防组没有患者出现中枢神经系统疾病，而8名静脉注射甲氨蝶呤的患者中有2名出现中枢神经系统疾病。未接受预防治疗组的中枢神经系统疾病进展发生率为 3.7%，而接受预防治疗组的中枢神经系统疾病进展发生率为 10%。结论 本研究显示，在社区医院系统中，中枢神经系统预防和中枢神经系统 IPI 文件的使用率较低。由于各组之间存在巨大差异，因此不能断言中枢神经系统预防措施具有疗效。中枢神经系统复发率与现有文献一致，因此需要继续评估当前中枢神经系统预防方法在DLBCL中的效用。需要新的明确有效的治疗方法，并鼓励提高标准化使用率。( J Patient Cent Res Rev. 2024;11:81-87.)
{"title":"Central Nervous System Prophylaxis Utilization in Patients With Newly Diagnosed Diffuse Large B-Cell Lymphoma Within a Large Community Health System","authors":"Michael J. Williams, Sol Atienza, Renee H. Aranda, Kayleigh B. Flint, S. Sana, S. C. Medlin, Zartash Gul, Federico A. Sanchez, Michael A. Thompson","doi":"10.17294/2330-0698.2060","DOIUrl":"https://doi.org/10.17294/2330-0698.2060","url":null,"abstract":"the form of intrathecal (IT) chemotherapy and/or high-dose intravenous (IV) methotrexate. CNS-IPI scores were calculated for all patients who received CNS prophylaxis or those who experienced CNS disease. Long-term outcomes at five years from diagnosis included CNS progression/relapse and survival. Results Of 234 patients who met criteria, 20 (8.6%) received either IV methotrexate or IT chemotherapy; most received IT methotrexate. No patients in the IT prophylaxis group developed CNS disease, while two of eight IV methotrexate patients experienced CNS disease involvement. The incidence of CNS progression was 3.7% in the no prophylaxis group and 10% in those who received prophylaxis. Conclusions This study revealed low utilization of CNS prophylaxis and CNS-IPI documentation in a community hospital system. Given large differences between groups, claims of CNS prophylaxis efficacy are unable to be made. CNS relapse rates were consistent with existing literature and promote continued evaluation of the utility of current CNS prophylaxis approaches in DLBCL. New unambiguously effective therapeutic approaches are needed and may encourage a higher rate of standardized use. ( J Patient Cent Res Rev. 2024;11:81-87.)","PeriodicalId":16724,"journal":{"name":"Journal of Patient-Centered Research and Reviews","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kathryn A. Wyman-Chick, Matthew J. Barrett, Michael J Miller, Jennifer L. Kuntz, Ella A. Chrenka, R. Rossom
Numerous studies have demonstrated that dementia is associated with increased utilization of health care services, which in turn results in increased costs of care. Dementia with Lewy bodies (DLB) is associated with greater costs of care relative to other forms of dementia due to higher rates of hospitalization and nursing home placement directly related to neuropsychiatric symptoms, parkinsonism, increased susceptibility to delirium, and elevated rates of caregiver burden. There is a critical need for researchers to identify potentially modifiable factors contributing to increased costs of care and poor clinical outcomes for patients with DLB, which may include comorbidities, polypharmacy/contraindicated medications, and access to specialty care. Previous research has utilized Medicare claims data, limiting the ability to study patients with early-onset (ie, prior to age 65) DLB. Integrated health systems offer the ability to combine electronic medical record data with Medicare, Medicaid, and commercial claims data and may therefore be ideal for utilization research in this population. The goals of this narrative review are to 1) synthesize and describe the current literature on health care utilization studies for patients with DLB, 2) highlight the current gaps in the literature, and 3) provide recommendations for stakeholders, including researchers, health systems, and policymakers. It is important to improve current understanding of potentially modifiable factors associated with increased costs of care among patients with DLB to inform public health policies and clinical decision-making, as this will ultimately improve the quality of patient care. ( J Patient Cent Res Rev. 2024;11:97-106.)
{"title":"Factors Associated With Increased Health Care Utilization for Patients With Dementia With Lewy Bodies: A Narrative Review","authors":"Kathryn A. Wyman-Chick, Matthew J. Barrett, Michael J Miller, Jennifer L. Kuntz, Ella A. Chrenka, R. Rossom","doi":"10.17294/2330-0698.2059","DOIUrl":"https://doi.org/10.17294/2330-0698.2059","url":null,"abstract":"Numerous studies have demonstrated that dementia is associated with increased utilization of health care services, which in turn results in increased costs of care. Dementia with Lewy bodies (DLB) is associated with greater costs of care relative to other forms of dementia due to higher rates of hospitalization and nursing home placement directly related to neuropsychiatric symptoms, parkinsonism, increased susceptibility to delirium, and elevated rates of caregiver burden. There is a critical need for researchers to identify potentially modifiable factors contributing to increased costs of care and poor clinical outcomes for patients with DLB, which may include comorbidities, polypharmacy/contraindicated medications, and access to specialty care. Previous research has utilized Medicare claims data, limiting the ability to study patients with early-onset (ie, prior to age 65) DLB. Integrated health systems offer the ability to combine electronic medical record data with Medicare, Medicaid, and commercial claims data and may therefore be ideal for utilization research in this population. The goals of this narrative review are to 1) synthesize and describe the current literature on health care utilization studies for patients with DLB, 2) highlight the current gaps in the literature, and 3) provide recommendations for stakeholders, including researchers, health systems, and policymakers. It is important to improve current understanding of potentially modifiable factors associated with increased costs of care among patients with DLB to inform public health policies and clinical decision-making, as this will ultimately improve the quality of patient care. ( J Patient Cent Res Rev. 2024;11:97-106.)","PeriodicalId":16724,"journal":{"name":"Journal of Patient-Centered Research and Reviews","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michael A. Horberg, Suzanne Simons, Robert T. Greenlee
{"title":"Recognizing 30 Years of Accomplishments and Envisioning an Innovative Future - The 2024 Annual Conference of the Health Care Systems Research Network","authors":"Michael A. Horberg, Suzanne Simons, Robert T. Greenlee","doi":"10.17294/2330-0698.2104","DOIUrl":"https://doi.org/10.17294/2330-0698.2104","url":null,"abstract":"","PeriodicalId":16724,"journal":{"name":"Journal of Patient-Centered Research and Reviews","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose Artificial intelligence (AI) technology is being rapidly adopted into many different branches of medicine. Although research has started to highlight the impact of AI on health care, the focus on patient perspectives of AI is scarce. This scoping review aimed to explore the literature on adult patients' perspectives on the use of an array of AI technologies in the health care setting for design and deployment. Methods This scoping review followed Arksey and O'Malley's framework and Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Scoping Reviews (PRISMA-ScR). To evaluate patient perspectives, we conducted a comprehensive literature search using eight interdisciplinary electronic databases, including grey literature. Articles published from 2015 to 2022 that focused on patient views regarding AI technology in health care were included. Thematic analysis was performed on the extracted articles. Results Of the 10,571 imported studies, 37 articles were included and extracted. From the 33 peer-reviewed and 4 grey literature articles, the following themes on AI emerged: (i) Patient attitudes, (ii) Influences on patient attitudes, (iii) Considerations for design, and (iv) Considerations for use. Conclusions Patients are key stakeholders essential to the uptake of AI in health care. The findings indicate that patients' needs and expectations are not fully considered in the application of AI in health care. Therefore, there is a need for patient voices in the development of AI in health care.
{"title":"Patient Perspectives on the Use of Artificial Intelligence in Health Care: A Scoping Review.","authors":"Sally Moy, Mona Irannejad, Stephanie Jeanneret Manning, Mehrdad Farahani, Yomna Ahmed, Ellis Gao, Radhika Prabhune, Suzan Lorenz, Raza Mirza, Christopher Klinger","doi":"10.17294/2330-0698.2029","DOIUrl":"https://doi.org/10.17294/2330-0698.2029","url":null,"abstract":"Purpose\u0000Artificial intelligence (AI) technology is being rapidly adopted into many different branches of medicine. Although research has started to highlight the impact of AI on health care, the focus on patient perspectives of AI is scarce. This scoping review aimed to explore the literature on adult patients' perspectives on the use of an array of AI technologies in the health care setting for design and deployment.\u0000\u0000\u0000Methods\u0000This scoping review followed Arksey and O'Malley's framework and Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Scoping Reviews (PRISMA-ScR). To evaluate patient perspectives, we conducted a comprehensive literature search using eight interdisciplinary electronic databases, including grey literature. Articles published from 2015 to 2022 that focused on patient views regarding AI technology in health care were included. Thematic analysis was performed on the extracted articles.\u0000\u0000\u0000Results\u0000Of the 10,571 imported studies, 37 articles were included and extracted. From the 33 peer-reviewed and 4 grey literature articles, the following themes on AI emerged: (i) Patient attitudes, (ii) Influences on patient attitudes, (iii) Considerations for design, and (iv) Considerations for use.\u0000\u0000\u0000Conclusions\u0000Patients are key stakeholders essential to the uptake of AI in health care. The findings indicate that patients' needs and expectations are not fully considered in the application of AI in health care. Therefore, there is a need for patient voices in the development of AI in health care.","PeriodicalId":16724,"journal":{"name":"Journal of Patient-Centered Research and Reviews","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140751991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kristen K. Will, Yue Liang, Chih-Lin Chi, Gerri Lamb, Michael Todd, Connie Delaney
Purpose Team-based care has been linked to key outcomes associated with the Quadruple Aim and a key driver of high-value patient-centered care. Use of the electronic health record (EHR) and machine learning have significant potential to overcome previous barriers to studying the impact of teams, including delays in accessing data to improve teamwork and optimize patient outcomes. Methods This study utilized a large EHR dataset (n=316,542) from an urban health system to explore the relationship between team composition and patient activation, a key driver of patient engagement. Teams were operationalized using consensus definitions of teamwork from the literature. Patient activation was measured using the Patient Activation Measure (PAM). Results from multilevel regression analyses were compared to machine learning analyses using multinomial logistic regression to calculate propensity scores for the effect of team composition on PAM scores. Under the machine learning approach, a causal inference model with generalized overlap weighting was used to calculate the average treatment effect of teamwork. Results Seventeen different team types were observed in the data from the analyzed sample (n=12,448). Team sizes ranged from 2 to 5 members. After controlling for confounding variables in both analyses, more diverse, multidisciplinary teams (team size of 4 or more) were observed to have improved patient activation scores. Conclusions This is the first study to explore the relationship between team composition and patient activation using the EHR and big data analytics. Implications for further research using EHR data and machine learning to study teams and other patient-centered care are promising and could be used to advance team science.
{"title":"Measuring the Impact of Primary Care Team Composition on Patient Activation Utilizing Electronic Health Record Big Data Analytics.","authors":"Kristen K. Will, Yue Liang, Chih-Lin Chi, Gerri Lamb, Michael Todd, Connie Delaney","doi":"10.17294/2330-0698.2019","DOIUrl":"https://doi.org/10.17294/2330-0698.2019","url":null,"abstract":"Purpose\u0000Team-based care has been linked to key outcomes associated with the Quadruple Aim and a key driver of high-value patient-centered care. Use of the electronic health record (EHR) and machine learning have significant potential to overcome previous barriers to studying the impact of teams, including delays in accessing data to improve teamwork and optimize patient outcomes.\u0000\u0000\u0000Methods\u0000This study utilized a large EHR dataset (n=316,542) from an urban health system to explore the relationship between team composition and patient activation, a key driver of patient engagement. Teams were operationalized using consensus definitions of teamwork from the literature. Patient activation was measured using the Patient Activation Measure (PAM). Results from multilevel regression analyses were compared to machine learning analyses using multinomial logistic regression to calculate propensity scores for the effect of team composition on PAM scores. Under the machine learning approach, a causal inference model with generalized overlap weighting was used to calculate the average treatment effect of teamwork.\u0000\u0000\u0000Results\u0000Seventeen different team types were observed in the data from the analyzed sample (n=12,448). Team sizes ranged from 2 to 5 members. After controlling for confounding variables in both analyses, more diverse, multidisciplinary teams (team size of 4 or more) were observed to have improved patient activation scores.\u0000\u0000\u0000Conclusions\u0000This is the first study to explore the relationship between team composition and patient activation using the EHR and big data analytics. Implications for further research using EHR data and machine learning to study teams and other patient-centered care are promising and could be used to advance team science.","PeriodicalId":16724,"journal":{"name":"Journal of Patient-Centered Research and Reviews","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140754429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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