Medical Care最新文献

{"title":"Using Well-Being Measurements to Enhance Clinical Practice: Why and How to Ask Patients About Their Broader Well-Being.","authors":"Dawne Vogt, Shelby Borowski, Bella Etingen, Vanessa L Merker, Barbara Bokhour, Benjamin Kligler","doi":"10.1097/MLR.0000000000002072","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002072","url":null,"abstract":"","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142605266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hospital-Level Variation in COVID-19 Treatment Among Hospitalized Adults in the United States: A Retrospective Cohort Study.","authors":"G Caleb Alexander, Brian T Garibaldi, Huijun An, Kathleen M Andersen, Matthew L Robinson, Kunbo Wang, Yanxun Xu, Joshua F Betz, Albert W Wu, Arielle Fisher, Shanna A Egloff, Kenneth E Sands, Hemalkumar B Mehta","doi":"10.1097/MLR.0000000000002086","DOIUrl":"10.1097/MLR.0000000000002086","url":null,"abstract":"<p><strong>Study design: </strong>Retrospective cohort study.</p><p><strong>Objective: </strong>To characterize variation in dexamethasone and remdesivir use over time among hospitals.</p><p><strong>Background: </strong>Little is known about hospital-level variation in COVID-19 drug treatments in a large and diverse network in the United States.</p><p><strong>Methods: </strong>We selected individuals hospitalized with COVID-19 across 163 hospitals between February 23, 2020 and October 31, 2021 from using the HCA CHARGE, an electronic health record repository from a network of community health care facilities in the United States. We quantified receipt of dexamethasone, remdesivir, and combined use of dexamethasone and remdesivir during the hospital stay. We used 2-level logistic regression models to determine the intraclass correlation coefficient (ICC) at the hospital level, adjusting for patient and hospital characteristics. The ICC shows the proportion of total variation in drug use accounted for by hospitals.</p><p><strong>Results: </strong>Among 161,667 individuals hospitalized with COVID-19, 73.0% were treated with dexamethasone, 49.1% with remdesivir, and 45.0% with both dexamethasone and remdesivir. The proportion of variation in dexamethasone use was 12.7% (adjusted ICC: 0.127), 8.5% for remdesivir, and 11.3% for combined drug use, indicating low interhospital variation. In the fully adjusted models, between-facility variation in dexamethasone use declined from 34.1% in February-March 2020 to 11.3% in January-March 2021 and then increased to 17.3% in July-October 2021. The variation in remdesivir use remained relatively stable during the study period.</p><p><strong>Conclusions: </strong>During the first 2 years of the pandemic, there was relatively consistent use of dexamethasone and remdesivir across the hospitals examined. Consistent adoption and implementation of treatment guidelines across the hospitals examined may have led to a decrease in variation in drug usage over time.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142469509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plan of Care Visits: Implementation During Hospitalization and Association With 30-Day Readmissions in a Large, Integrated Health Care System.","authors":"Steven P Masiano, Susannah Rose, Judith Wolfe, Nancy M Albert, Alex Milinovich, Leslie Jurecko, Beri Ridgeway, Michael W Kattan, Anita D Misra-Hebert","doi":"10.1097/MLR.0000000000002081","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002081","url":null,"abstract":"<p><strong>Background: </strong>Plan of Care of Visits (POCV), including the patient, nurse, and hospital provider were implemented across an integrated health system to improve provider-patient communication during hospitalization and patient outcomes.</p><p><strong>Objectives: </strong>To assess POCV adoption after implementation, patient characteristics assosites were classified as teachsites were classified as teachsites were classified as teachsites were classified as ciated with POCV completion, and association of POCV with 30-day readmissions.</p><p><strong>Methods: </strong>This retrospective cohort study utilized electronic medical record (EMR) data of 237,430 adult patients discharged to home from 11 hospitals from January 2020 to December 2022. POCV completion was a discrete EMR variable. POCV adoption was estimated monthly by hospital as proportion of patients with at least 1 POCV during hospitalization, with variation among hospitals measured using the Variance Partition Coefficient (VPC). Multivariable logistic regressions assessed factors associated with POCV completion and POCV association with 30-day readmission.</p><p><strong>Results: </strong>POCV adoption increased from 69% to 94% (2020-2022) and varied by 50% across hospitals (VPC 0.50, 95% CI: 0.29-0.70). Odds of a discharge-day POCV were lower among older patients (≥65 vs. 18-34 y, OR 0.81, CI: 0.79-0.83), and higher among female (OR 1.06; CI: 1.04-1.07), Asian (vs. White, OR 1.13; CI: 1.06-1.21), Hispanic (OR 1.09; CI: 1.05-1.13), and surgical patients (vs. medical, OR 1.33; CI: 1.30-1.35). Patients completing discharge-day POCV had lower 30-day readmission odds (2022 OR 0.76, CI: 0.73-0.79). Patients with POCV on ≥75% of hospital days had similar readmission odds trends.</p><p><strong>Conclusions: </strong>POCV implementation was successful, and POCV completion was associated with fewer 30-day readmissions. Future work should focus on increasing POCV adoption while reducing hospital variation.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142469512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Defining and Validating Criteria to Identify Populations Who May Benefit From Home-Based Primary Care.","authors":"Maggie R Salinger, Katherine A Ornstein, Hannah Kleijwegt, Abraham A Brody, Bruce Leff, Harriet Mather, Jennifer Reckrey, Christine S Ritchie","doi":"10.1097/MLR.0000000000002085","DOIUrl":"10.1097/MLR.0000000000002085","url":null,"abstract":"<p><strong>Background: </strong>Home-based primary care (HBPC) is an important care delivery model for high-need older adults. Currently, target patient populations vary across HBPC programs, hindering expansion and large-scale evaluation.</p><p><strong>Objectives: </strong>Develop and validate criteria that identify appropriate HBPC target populations.</p><p><strong>Research design: </strong>A modified Delphi process was used to achieve expert consensus on criteria for identifying HBPC target populations. All criteria were defined and validated using linked data from Medicare claims and the National Health and Aging Trends Study (NHATS) (cohort n=21,727). Construct validation involved assessing demographics and health outcomes/expenditures for selected criteria.</p><p><strong>Subjects: </strong>Delphi panelists (n=29) represented diverse professional perspectives. Criteria were validated on community-dwelling Medicare beneficiaries (age ≥70) enrolled in NHATS.</p><p><strong>Measures: </strong>Criteria were selected via Delphi questionnaires. For construct validation, sociodemographic characteristics of Medicare beneficiaries were self-reported in NHATS, and annual health care expenditures and mortality were obtained via linked Medicare claims.</p><p><strong>Results: </strong>Panelists proposed an algorithm of criteria for HBPC target populations that included indicators for serious illness, functional impairment, and social isolation. The algorithm's Delphi-selected criteria applied to 16.8% of Medicare beneficiaries. These HBPC target populations had higher annual health care costs [Med (IQR): $10,851 (3316, 31,556) vs. $2830 (913, 9574)] and higher 12-month mortality [15% (95% CI: 14, 17) vs. 5% (95% CI: 4, 5)] compared with the total validation cohort.</p><p><strong>Conclusions: </strong>We developed and validated an algorithm to define target populations for HBPC, which suggests a need for increased HBPC availability. By enabling objective identification of unmet demands for HBPC access or resources, this algorithm can foster robust evaluation and equitable expansion of HBPC.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142469508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Community Benefit and Tax-Exemption Levels at Non-Profit Hospitals Across U.S. States.","authors":"D August Oddleifson, Huaying Dong, Rishi K Wadhera","doi":"10.1097/MLR.0000000000002064","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002064","url":null,"abstract":"<p><strong>Objective: </strong>To assess the association between state policies and sociodemographic characteristics and state mean fair share spending at non-profit hospitals. Fair share spending is a hospital's charity care and community investment less the estimated value of their tax-exempt status.</p><p><strong>Background: </strong>Hospitals with non-profit status in the United States are exempt from paying taxes. In return, they are expected to provide community benefits by subsidizing medical care for those who cannot pay and investing in the health and social needs of their community.</p><p><strong>Methods: </strong>We used a multivariable linear regression model to determine the association of state-level sociodemographics and policies with state-level mean fair share spending in 2019. Fair share spending data was obtained from the Lown Institute.</p><p><strong>Results: </strong>We found no association between the percentage of people living in poverty, in rural areas, or U.S. region and fair share spending. Similarly, there was no association found for state minimum community benefit and reporting requirements. The state percentage of racial/ethnic minorities was associated with higher mean fair share spending [+$1.48 million for every 10% increase (95% CI: 0.01 to 2.96 million)]. Medicaid expansion status was associated with a 6.9-million-dollar decrease (95% CI: -10.4 to -3.3 million).</p><p><strong>Conclusions: </strong>State-level community benefit policies have been ineffective at raising community benefit spending to levels comparable to the value of non-profit hospital tax-exempt status.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142391711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Outcomes Among High-Risk Primary Care Patients With Diabetic Kidney Disease: Methodological Challenges and Results From the STOP-DKD Study.","authors":"Hayden B Bosworth, Uptal D Patel, Allison A Lewinski, Clemontina A Davenport, Jane Pendergast, Megan Oakes, Matthew J Crowley, Leah L Zullig, Sejal Patel, Jivan Moaddeb, Julie Miller, Shauna Malone, Huiman Barnhart, Clarissa J Diamantidis","doi":"10.1097/MLR.0000000000002043","DOIUrl":"10.1097/MLR.0000000000002043","url":null,"abstract":"<p><strong>Background/objective: </strong>Slowing the progression of diabetic kidney disease (DKD) is critical. We conducted a randomized controlled trial to target risk factors for DKD progression.</p><p><strong>Methods: </strong>We evaluated the effect of a pharmacist-led intervention focused on supporting healthy behaviors, medication management, and self-monitoring on decline in estimated glomerular filtration rate (eGFR) for 36 months compared with an educational control.</p><p><strong>Results: </strong>We randomized 138 individuals to the intervention group and 143 to control. At baseline, mean (SD) eGFR was 80.7 (21.7) mL/min/1.73m 2 , 56% of participants had chronic kidney disease and a history of uncontrolled hypertension with a baseline SBP of 134.3 mm Hg. The mean (SD) decline in eGFR by cystatin C from baseline to 36 months was 5.0 (19.6) and 5.9 (18.6) mL/min/1.73m 2 for the control and intervention groups, respectively, with no significant between-group difference ( P =0.75).</p><p><strong>Conclusions: </strong>We did not observe a significant difference in clinical outcomes by study arm. However, we showed that individuals with DKD will engage in a pharmacist-led intervention. The potential explanations for a lack of change in DKD risk factors can be attributed to 5 broad issues, challenges: (1) associated with enrolling patients with low eGFR and poor BP control; (2) implementing the intervention; (3) limited duration during which to observe any clinical benefit from the intervention; (4) potential co-intervention or contamination; and (5) low statistical power.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141748542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trajectories and Transitions in Service Use Among Older Veterans at High Risk of Long-Term Institutional Care.","authors":"Erin D Bouldin, Ben J Brintz, Jared Hansen, Rand Rupper, Rachel Brenner, Orna Intrator, Bruce Kinosian, Mikayla Viny, Stuti Dang, Mary Jo Pugh","doi":"10.1097/MLR.0000000000002051","DOIUrl":"10.1097/MLR.0000000000002051","url":null,"abstract":"<p><strong>Background: </strong>We aimed to identify combinations of long-term services and supports (LTSS) Veterans use, describe transitions between groups, and identify factors influencing transition.</p><p><strong>Methods: </strong>We explored LTSS across a continuum from home to institutional care. Analyses included 104,837 Veterans Health Administration (VHA) patients 66 years and older at high-risk of long-term institutional care (LTIC). We conduct latent class and latent transition analyses using VHA and Medicare data from fiscal years 2014 to 2017. We used logistic regression to identify variables associated with transition.</p><p><strong>Results: </strong>We identified 5 latent classes: (1) No Services (11% of sample in 2015); (2) Medicare Services (31%), characterized by using LTSS only in Medicare; (3) VHA-Medicare Care Continuum (19%), including LTSS use in various settings across VHA and Medicare; (4) Personal Care Services (21%), characterized by high probabilities of using VHA homemaker/home health aide or self-directed care; and (5) Home-Centered Interdisciplinary Care (18%), characterized by a high probability of using home-based primary care. Veterans frequently stayed in the same class over the three years (30% to 46% in each class). Having a hip fracture, self-care impairment, or severe ambulatory limitation increased the odds of leaving No Services, and incontinence and dementia increased the odds of entering VHA-Medicare Care Continuum. Results were similar when restricted to Veterans who survived during all 3 years of the study period.</p><p><strong>Conclusions: </strong>Veterans at high risk of LTIC use a combination of services from across the care continuum and a mix of VHA and Medicare services. Service patterns are relatively stable for 3 years.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11545584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141988239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Social Risk Screening on Discharge Care Processes and Postdischarge Outcomes: A Pragmatic Mixed-Methods Clinical Trial During the COVID-19 Pandemic.","authors":"Andrea S Wallace, Alycia A Bristol, Erin Phinney Johnson, Catherine E Elmore, Sonja E Raaum, Angela Presson, Kaleb Eppich, Mackenzie Elliott, Sumin Park, Benjamin S Brooke, Sumin Park, Marianne E Weiss","doi":"10.1097/MLR.0000000000002048","DOIUrl":"10.1097/MLR.0000000000002048","url":null,"abstract":"<p><strong>Background: </strong>Social risk screening during inpatient care is required in new CMS regulations, yet its impact on inpatient care and patient outcomes is unknown.</p><p><strong>Objectives: </strong>To evaluate whether implementing a social risk screening protocol improves discharge processes, patient-reported outcomes, and 30-day service use.</p><p><strong>Research design: </strong>Pragmatic mixed-methods clinical trial.</p><p><strong>Subjects: </strong>Overall, 4130 patient discharges (2383 preimplementation and 1747 postimplementation) from general medicine and surgical services at a 528-bed academic medical center in the Intermountain United States and 15 attending physicians.</p><p><strong>Measures: </strong>Documented family interaction, late discharge, patient-reported readiness for hospital discharge and postdischarge coping difficulties, readmission and emergency department visits within 30 days postdischarge, and coded interviews with inpatient physicians.</p><p><strong>Results: </strong>A multivariable segmented regression model indicated a 19% decrease per month in odds of family interaction following intervention implementation (OR=0.81, 95% CI=0.76-0.86, P<0.001), and an additional model found a 32% decrease in odds of being discharged after 2 pm (OR=0.68, 95% CI=0.53-0.87, P=0.003). There were no postimplementation changes in patient-reported discharge readiness, postdischarge coping difficulties, or 30-day hospital readmissions, or ED visits. Physicians expressed concerns about the appropriateness, acceptability, and feasibility of the structured social risk assessment.</p><p><strong>Conclusions: </strong>Conducted in the immediate post-COVID timeframe, reduction in family interaction, earlier discharge, and provider concerns with structured social risk assessments likely contributed to the lack of intervention impact on patient outcomes. To be effective, social risk screening will require patient/family and care team codesign its structure and processes, and allocation of resources to assist in addressing identified social risk needs.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142154538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conduct of Large, Multisite, Comparative Clinical Effectiveness Research Studies: Learnings From the Patient-Centered Outcomes Research Institute's Palliative Care Learning Network.","authors":"Carly L Paterson, Shannon Reefer, Shreeya Khatiwada, Joanna G Philips, Brendaly Rodríguez, Steven B Clauser, Neeraj K Arora","doi":"10.1097/MLR.0000000000002031","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002031","url":null,"abstract":"","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142154536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing Patient-Centered Outcomes Research Institute Stakeholder Engagement Principles in Models of Palliative Care Delivery and Advance Care Planning Research.","authors":"Anne M Walling, Manisha Verma, Corita R Grudzen, Susan Enguidanos, Nadine J Barrett, Kimberly S Johnson, Angela K Combe, Fabian M Johnston, Joseph A Greer","doi":"10.1097/MLR.0000000000002025","DOIUrl":"10.1097/MLR.0000000000002025","url":null,"abstract":"<p><strong>Background: </strong>Given the many challenges of conducting research that addresses the palliative and end-of-life care needs of patients with serious illnesses, stakeholder engagement starting from the moment of study conceptualization and design is critical to ensure successful participant recruitment, data collection, intervention delivery, data analysis, and dissemination.</p><p><strong>Methods: </strong>Guided by a conceptual model published by the Patient-Centered Outcomes Research Institute (PCORI) entitled, \"Measuring What Matters for Advancing the Science and Practice of Engagement\"14 and with the support of a PCORI Engagement Officer, representatives from 9 PCORI-funded study teams formed a working group to survey team members and review, outline, and describe key lessons learned and best practices for promoting stakeholder engagement in palliative care research.</p><p><strong>Results: </strong>Almost all study teams engaged with patients/caregivers, clinicians, researchers, and health care system experts as stakeholder partners. About half the teams also included payers and training institutions as part of their stakeholder advisors as well as a range of content experts. Study teams relied on a variety of support structures and resources, and they employed 10 distinct methods for maintaining engagement. All engagement methods were generally considered to be effective by teams who used the method, though there was some variability in team-rated engagement quality of each method. Nine barriers to stakeholder engagement were identified across the 9 studies as well as 9 strategies (or facilitators) to overcome these barriers. We share examples of how stakeholder engagement impacted studies in all phases, including the preparatory phase, study initiation phase, execution phase, and data analysis/dissemination phase.</p><p><strong>Conclusions: </strong>Teams utilized a variety of resources and support structures as well as capitalized on multiple engagement methods for fostering stakeholder engagement, resulting in a high level of collaboration and integration.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373890/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142154539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}