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Multiple Chronic Conditions

Multiple Chronic Conditions (MCC) refer to the coexistence of two or more chronic medical conditions within an individual.
These complex health states can present unique challenges in management and treatment, as the interactions and compounding effects of multiple conditions can impact patient outcomes.
Researchers studying MCC must navigate a vast landscape of research protocols to identify the most effective approaches.
PubCompare.ai, an AI-powered platform, streamlines this process by helping researchers easily locate relevant protocols from literature, pre-prints, and patents, while leveraging AI-driven comparisons to identify the optimal protocols and products.
By elevating the research process, PubCompare.ai empowers researchers to uncover valuable insights and deliver impactful findings in the field of multi-chronic condition management.

Most cited protocols related to «Multiple Chronic Conditions»

1
Chronic Disease Comorbidity Review
We performed a semistructured literature review to identify relevant articles. Specifically, we queried MEDLINE for peer-reviewed publications that examined the prevalence, outcomes, costs, and patient management challenges associated with multiple chronic conditions.
We first selected all articles the Mesh terms ‘chronic disease’ and ‘comorbidity,’ and limited our search to articles on adults published in English between January 2000 and March 2007 (n=643). This set was further paired down using 2 different strategies. The first strategy used a set of specific Mesh terms related to prevalence, quality, access, delivery of care, patterns of care, morbidity, mortality, and expenditures. To ensure that we did not overlook any important articles in the original set, we also limited the original set to articles published in core journals. The final set of 123 articles was the union of abstracts gained from these 2 approaches. Articles without abstracts or whose author was anonymous were not reviewed. The remaining abstracts were reviewed by the first author and abstracts that did not mention at least 1 specific somatic chronic illness, abstracts that did not examine specific comorbidities, and articles that focused on an acute illness or procedure were removed. Information summarized in this review stem from the remaining articles and prior publications cited by these articles.
Vogeli C., Shields A.E., Lee T.A., Gibson T.B., Marder W.D., Weiss K.B, & Blumenthal D. (2007). Multiple Chronic Conditions: Prevalence, Health Consequences, and Implications for Quality, Care Management, and Costs. Journal of General Internal Medicine, 22(Suppl 3), 391-395.
Publication 2007
Adult Delivery of Health Care Diploid Cell Disease, Chronic Multiple Chronic Conditions Patients Stem, Plant
2
Development of Patient-Centered eHealth Tool
Phase 1 consisted of focus groups with patients and carers plus interviews with content experts (CEs) and PCPs from the practice. This phase of development was mainly concerned with ensuring a user focus and capturing work-centeredness dimensions of user-centered design in that we sought to understand users' needs and the tasks needed to address those needs. Focus group findings in Phase 1, along with a literature review [34 (link)], provided the initial building blocks for tool development. A total of 14 patients and carers participated in four focus groups in the fall of 2013. Of the 14 participants, 10 (71%) were patients, 2 (14%) were caregivers only, and 2 (14%) were both patients and caregivers. The participants' average age was 64 years (range 42-90), and 9 out of 14 (64%) participants were female. Patients participating in the focus groups reported multiple chronic illnesses, including diabetes, chronic pain, osteoarthritis, osteoporosis, anemia, cardiac conditions, glaucoma, and mental illness [35 (link)]. Initial findings from the focus groups and available resources suggested that the tool should support better communication between patients and PCPs around three key areas:
1. Information about symptoms and functional status (ie, pain, mobility, depression/anxiety, activities of daily living [eg, bathing, toileting], and social well-being).
2. Medication management support (ie, reminders, renewals, and reporting side effects).
3. Educational materials and/or trusted websites to support self-management.
Next, purposive sampling [36 ,37 (link)] was used to identify PCPs and CEs who could provide the feedback required to refine the tool. Semistructured interviews were conducted with the PCPs, as well as CEs, in at least one of the fields of development or utilization of eHealth and/or research or service delivery experience with CCDD patients. CEs were identified through their academic, clinical, and/or research networks.
PCPs were selected from the primary care practice where the tool would be piloted and tested. These PCPs had been engaged in the project from early stages and had attended several meetings to receive updates on the project. A patient advocate was also interviewed, as this individual is experiencing CCDD who has engaged with other eHealth technologies as part of their care, and has previously served as a patient representative in other research projects.
PCPs and CEs were given a summary of patient focus group findings and asked their perspectives on the following: (1) the value of ongoing monitoring of symptoms and functional status as part of usual care, (2) what types of information should be shared about those symptoms (ie, indicators, scales, and contextual information) and how it could best be shared, (3) the role of patients accessing appropriate educational materials, (4) how different communication methods would fit into provider workflows, and (5) other aspects of primary health care delivery that may be important to capture for managing patients with CCDD.
Steele Gray C., Khan A.I., Kuluski K., McKillop I., Sharpe S., Bierman A.S., Lyons R.F, & Cott C. (2016). Improving Patient Experience and Primary Care Quality for Patients With Complex Chronic Disease Using the Electronic Patient-Reported Outcomes Tool: Adopting Qualitative Methods Into a User-Centered Design Approach. JMIR Research Protocols, 5(1), e28.
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Publication 2016
Anemia Anxiety Chronic Pain Degenerative Arthritides Delivery of Health Care Diabetes Mellitus Glaucoma Heart Diseases Mental Disorders Multiple Chronic Conditions Obstetric Delivery Osteoporosis Pain Patient Representatives Patients Pharmaceutical Preparations Pneumocystosis Primary Health Care Range of Motion, Articular Self-Management Telehealth Woman
3
Chronic Conditions in Delivery Hospitalizations
We conducted a retrospective, serial cross-sectional analysis on a 20% sample of all delivery hospitalization discharges in the United States between 2005 and 2014 using the Healthcare Cost and Utilization Project’s National Inpatient Sample, compiled by the Agency for Healthcare Research and Quality. The National Inpatient Sample is the largest nationally-representative sample of hospital discharges in the United States.13 The dataset contains clinical and non-clinical data for each hospitalization, including diagnostic and procedure codes, patient demographic characteristics, and expected payment source. Deliveries were identified by ICD-9 codes using previously described methods.14 (link) Because the analysis was of de-identified national data, our study was deemed exempt from review by the Institutional Review Board at the University of Michigan Medical School.
We examined the prevalence of eight common, chronic conditions known to impact obstetric morbidity and mortality: chronic respiratory disease, chronic hypertension, substance use disorders, pre-existing diabetes, chronic heart disease, chronic renal disease, chronic liver disease, and HIV. We focused our analyses on pre-existing conditions rather than pregnancy-related conditions. Chronic conditions were defined using the ICD-9 codes listed in Table 1, which were chosen for inclusion based on review of literature13 ,14 (link) and author consensus. Sub-group analysis of ICD-9-CM codes included in the variable for chronic respiratory disease revealed that this variable was comprised almost entirely of observations with diagnosis codes for asthma (493.x) and will be referred to as asthma for the remainder of this report.
We controlled for several covariates in our analyses, including age, rural vs. urban residence, median household income quartile for the patient’s ZIP Code, and primary payer. Given that a number of hospitals and HCUP partners do not report data on race or ethnicity, we were unable to include these co-variates.13 Location of residence was defined as rural or urban using the National Center for Health Statistics Classification and Urban Influence Codes.15 Payment sources were grouped into public insurance (Medicaid and Medicare), private insurance, and uninsured or self-pay. Given that fewer than 0.6% of the delivery hospitalizations were funded by Medicare, public sources are referred to as Medicaid throughout the study. The number of observations with missing values was approximately 2% of all delivery hospitalizations, which was considered sufficient for analysis.
We used multivariable logistic regression models with predictive margins to obtain estimates of disease-specific prevalence and to estimate the rates at which any one and multiple chronic conditions were identified per 1,000 delivery hospitalizations. Data was pooled into two-year periods to increase the precision of our estimates. We estimated disease-specific prevalence by key socio-economic predictors for the four most prevalent conditions by interacting rural vs. urban residence, income, and payer with time in adjusted multivariable logistic regression models. Predictive margins were used in all sub-group analyses to generate adjusted prevalence estimates. We examined differences in prevalence for each condition over time by subgroup. We compared changes in prevalence over time across subgroups using a difference-in-differences framework.
We utilized National Inpatient Sample trend weights to allow for comparisons across years. Results are weighted to allow for nationally-representative inferences unless otherwise noted. Full details about sampling and weighting procedures are available at the Healthcare Cost and Utilization Project website.13 Two-sided P values <.05 were considered statistically significant. All analyses were conducted using STATA version 14.2 (StataCorp, College Station, TX).
Admon L.K., Winkelman T.N., Moniz M.H., Davis M.M., Heisler M, & Dalton V.K. (2017). Disparities in Chronic Conditions Among Women Hospitalized for Delivery in the United States, 2005–2014. Obstetrics and gynecology, 130(6), 1319-1326.
Publication 2017
Asthma Chronic Condition Chronic Kidney Diseases Diagnosis Disease, Chronic Ethics Committees, Research Ethnicity Heart Heart Diseases High Blood Pressures Hospitalization Households Inpatient Liver Liver Diseases Multiple Chronic Conditions Obstetric Delivery Patients PER1 protein, human Pregnancy Respiration Disorders Respiratory Rate States, Prediabetic Substance Use Disorders
4
Frailty Measurement and Mortality Risk
Frailty is a physiological state of non-specific vulnerability to stressors resulting from decreased physiological reserves and the deregulation of multiple physiological systems associated with advancing age.1
3 (link)
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6 (link)
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37 Conceptually, frailty is not just an association with specific diseases or disabilities but rather a systemic manifestation of physical and cognitive deficits, including the signs, symptoms, illnesses, and impairments that accumulate over the life course.8
37
Empirically, a variety of methods have been used to quantify frailty, although the most common applications are the phenotypic approach and the frailty index.38
39 (link) The phenotypic approach defines frailty on the basis of several items, such as weight loss, exhaustion, weakness, slowness, or low physical activity, and considers any three conditions as an indication of frailty.5 Alternatively, a frailty index focuses less on the specific deficits of people and more on the cumulative number of health deficiencies.8
15 Despite the similarities between these approaches, the choice of measurement is often dictated by the clinical outcome under investigation. Accordingly, recent research shows that frailty indices are more applicable for studying mortality than are phenotypic methods.8
38
39 (link) In practice, most studies compute a frailty index as the proportion of cumulative health deficits to all possible deficits for a given individual.17 (link)
Following earlier research, we constructed a frailty index using 39 variables that included objective, subjective, and proxy reports of cognitive functioning, disability, auditory and visual ability, depression, heart rhythm, and numerous chronic diseases (details available on request). Each item was assigned a value of 1 in the presence of a deficit (otherwise 0), and a value of 2 was assigned for people with two or more serious conditions that led to admission to hospital or a period of confinement in bed.13 We then constructed a frailty index by summarising all deficits and dividing by the total number of possible deficits.
Although studies have shown that a frailty index does not require the same number or type of items to estimate accurate proportions of frailty levels,38 the items comprising our index are similar to those used in studies from Canada,15 the United States,8 and Hong Kong.13 We tested the validity and sensitivity of the frailty index by analysing several indices on the basis of differing combinations of variables. These results showed that as long as we included variables characterising each of the major domains of health (activities of daily living, instrumental activities of daily living, chronic illnesses, and cognitive functioning), the pattern of frailty with age remained consistent. In the Chinese longitudinal healthy longevity survey, levels of frailty increased exponentially from ages 65 to 100 and then levelled off (results not shown); therefore we split the frailty index into fourths for men and for women to account for non-linear relations between levels of frailty and type of death.
To obtain robust estimates we also adjusted analyses for several previously identified confounding factors.40 (link) Various coding strategies were assessed for each measure and the results were similar; therefore, we dichotomised all of the confounding variables (except age). Measures of demographic background included age categorisations of 65-79 (reference group), 80-89, 90-99, and 100 and older, people from non-Han ethnic minorities, and those living in urban areas. Measures of socioeconomic status included education (any formal education), primary lifetime occupation as a white collar worker, economic independence (primary financial source from own work or pension), family in good economic standing (self rated as rich compared with other families in the community), and being in receipt of adequate drugs for any illnesses. Social contact and support measures included current marital status, close proximity to children (coresiding with biological or adopted children, including a spouse’s child, or having one or more biological children living in the same village or street block), and religious activity almost every day or sometimes. Measures for health practices included exercising on a regular basis and having ever smoked in the past five years.
Dupre M.E., Gu D., Warner D.F, & Yi Z. (2009). Frailty and type of death among older adults in China: prospective cohort study. The BMJ, 338, b1175.
Publication 2009
Asthenia Auditory Perception Biopharmaceuticals Child Child, Adopted Chinese Disabled Persons Disease, Chronic Disorders, Cognitive Ethnic Minorities Heart Hypersensitivity Multiple Chronic Conditions Pharmaceutical Preparations Phenotype Physical Examination physiology Spouse Woman Workers
5
Identifying Prevalent Comorbidities in the UK
We selected 56 comorbidities that are considered clinically significant and are most prevalent in the UK (Table 1). The comorbidities were selected based on 3 sources: (1) the Quality and Outcomes Framework (QOF), an incentive scheme for general practitioners in the UK [18 ], (2) the Charlson comorbidity index, the most commonly used comorbidity index, originally designed to predict inpatient hospital mortality [19 (link)], and (3) the multiple chronic conditions list of the US Department of Health and Human Services Initiative on Multiple Chronic Conditions [20 ]. For each comorbidity, a list of diagnostic codes from hospital (ICD-10) and primary care (Read) coding schemes was used to identify diagnoses. The codes were compiled from online code repositories, including Cardiovascular Disease Research Using Linked Bespoke Studies and Electronic Health Records (CALIBER) [21 (link)], and medical dictionary keyword searches (S1 Table). Diagnosis of a prevalent comorbidity was defined as the recording of a diagnostic code for that comorbidity before the date of CVD incidence. We did not include diagnoses after the date of diagnosis of CVD.
Tran J., Norton R., Conrad N., Rahimian F., Canoy D., Nazarzadeh M, & Rahimi K. (2018). Patterns and temporal trends of comorbidity among adult patients with incident cardiovascular disease in the UK between 2000 and 2014: A population-based cohort study. PLoS Medicine, 15(3), e1002513.
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Publication 2018
Cardiovascular Diseases Diagnosis General Practitioners Inpatient Multiple Chronic Conditions Primary Health Care

Most recents protocols related to «Multiple Chronic Conditions»

1
Self-Management Intervention for Older Adults with Multimorbidity
Research staff will establish study eligibility, consent, and gather demographic and clinical characteristics (age, chronic medical conditions, years of education, marital status, race/ethnicity, medications). Baseline assessment interviews will be conducted by the PI.
The primary outcome is independence in health self-management activities, as measured by the COPM and the Self-Management Assessment Scale (SMASc).30 (link) The COPM was designed to facilitate goal setting and detect change in self-report performance of daily activity.36 The COPM is valid, reliable, clinically useful, and responsive in community-dwelling older adults.29 (link) The SMASc, an assessment for measuring effective self-management of chronic conditions, is valid, reliable, and sensitive to change in a sample with T2D and other chronic conditions.30 (link)Given that MCC and depression often coexist,37 (link) improvements in mood will be assessed using the Behavioral Activation for Depression Scale (BADS).38 The BADS has good factor structure, internal consistency, construct validity, and test-retest reliability in community-dwelling adults.39 A major determinant of success in chronic condition self-management is self-efficacy.40 (link) The PROMIS© Measures for Self-Efficacy for Managing Chronic Conditions are highly validated and reliable with good internal consistency in multiple samples with chronic conditions.40 (link),41 (link)Research staff will collect quantitative feasibility measures including recruitment rates, proportions of participants who initiate and complete the intervention, and adherence to study procedures. In addition, qualitative information regarding suitability and acceptability of the study procedures and intervention will be collected by the unbiased study evaluator at 10 weeks and 22 weeks. Fidelity of the PI-delivered intervention will be ensured using established procedures for checklist assessment methods.22 (link)
Klinedinst T.C., Ciro C.A, & Kendzor D.E. (2023). A pilot, randomized, feasibility study to improve health self-management behaviors in older adults with multiple chronic conditions and functional limitations: Protocol for the Behavioral Activation and Occupational Therapy Trial (BA+OT). Journal of Multimorbidity and Comorbidity, 13, 26335565231163037.
Publication 2023
Adult Aged Chronic Condition Disease, Chronic Eligibility Determination Ethnicity Mood Multiple Chronic Conditions Pharmaceutical Preparations Self-Assessment Self-Management
2
Comprehensive Team-Based Chronic Care Model
SMHCVH implemented the PHT in 2017, building upon an established patient-centered medical home team that included CDEs and nurse care managers. Between 2017 and 2021, the PHT expanded upon existing roles and includes 3 registered nurses involved in CCM and other clinical services, 2 clinical dietitians, 6 CHWs, 2 CDEs, 3 behavioral health therapists, and a population health director.
CCM at SMHCVH relies on the essential elements of the Chronic Care Model (e.g., community resources, health system, self-management support, delivery system design, decision support, clinical information systems) to provide care coordination and medical case management to patients, especially for those with multiple chronic conditions. In addition to direct support from CDEs and CCM nurses, SMHCVH CHWs offer community-based chronic disease self-management and chronic pain self-management sessions based on the Stanford self-management program curricula.11 (link),12 (link)The PHT sustained existing CDEs and offered individual DSME and group chronic disease education programs. CHWs conduct outreach through one-on-one visits or community-wide events and are conduits to other PHT services.13 ,14 The IBH model at SMHCVH integrates behavioral health providers directly into primary care clinics, and PHT team members provide warm hand-offs to ensure patient preferences and concerns are considered. Registered dietitians improve the integration of Medical Nutrition Therapy into existing diabetes education. The dietitians work directly with patients, providers, and community partners to support both patient-level and population-level nutrition goals.
Tanumihardjo J.P., Kuther S., Wan W., Gunter K.E., McGrath K., O’Neal Y., Wilkinson C., Zhu M., Packer C., Petersen V, & Chin M.H. (2023). New Frontiers in Diabetes Care: Quality Improvement Study of a Population Health Team in Rural Critical Access Hospitals. Journal of General Internal Medicine, 38(Suppl 1), 56-64.
Publication 2023
2-chloro-1,1-difluoroethane Case Management CDE protocol Chronic Pain Diabetes Mellitus Dietitian Disease, Chronic Long-Term Care Medical Nutrition Therapy Multiple Chronic Conditions Nurse Managers Nurses Obstetric Delivery Patients Population Health Programmed Learning Registered Nurse Self-Management
3
Taiwan Biobank Study on Type 2 Diabetes
This cross-sectional study was conducted on a total of 2400 study subjects from the Taiwan Biobank with type 2 diabetes aged 30–70 and self-reported as being of Taiwanese Han Chinese ancestry. Those who had a history of cancer were excluded. In early 2005, the ‘Taiwan Biobank’ has been implemented as a part of Taiwan’s strategic development in promoting the country as an island of biomedicine.36 (link) The Taiwan Biobank project plans to conduct a large-scale community-based cohort and several patient cohorts of local chronic diseases from medical centers (the hospital-based cohorts) and then track health-related status and lifestyle behaviors of these participants for at least 10 years. The 2400 participants with type 2 diabetes were randomly selected from participants with type 2 diabetes in the Taiwan Biobank. Meanwhile, 78 individuals were excluded due to extreme heterozygosity rate (n=33), closely related individuals (n=17), withdrew from study (n=2), or lack of basic sociodemographic information (n=16), resulting in the inclusion of 2332 individuals in MR analysis (figure 1).
Lin C.C., Li C.I., Liu C.S., Liao L.N., Yang C.W., Lin C.H., Yang S.Y, & Li T.C. (2023). Association of high-sensitivity C-reactive protein and diabetic nephropathy in patients with type 2 diabetes: a Mendelian randomization study. BMJ Open Diabetes Research & Care, 11(1), e003197.
Publication 2023
Chinese Diabetes Mellitus, Non-Insulin-Dependent Heterozygote Infantile Neuroaxonal Dystrophy Malignant Neoplasms Multiple Chronic Conditions Patients
4
Community-Academic Partnership for Collaborative Care
Our case definition is a community-academic partnership to build capacity for collaborative care between 2017 and 2020 (49 (link)). We used Kohrt's et al. (22 (link)) recommendations for mapping community-based mental health care to generate the case description: Our partnership included two applied research centers at the University of Washington (UW) and a social service community-based organization (El Sol Neighborhood Educational Center; “El Sol”) that trains promotores de salud [community health workers/promotores (CHWs/Ps)] to improve access to and quality of social service care for underserved communities in the Inland Empire region of California. El Sol, a CBO committed to community transformation and social change, prioritizes reaching mono-lingual Spanish speakers, immigrants, and other LEP residents. Two CHWs/Ps were trained to deliver PEARLS to older adults in their homes. The depression care management (implementation) team included the CHWs/Ps, a program manager/CHW/P supervisor at the community-based organization, and a licensed mental health therapist and a psychiatric assistant at local partner organizations. The research center role was to provide content and quality improvement expertise via practice coaching (“external facilitation”) (50 (link)) and project oversight. PEARLS participants were primarily Spanish-speaking Mexican-American immigrants who had lived in the U.S. for over 10 years. These older adults were living in poverty with multiple chronic conditions and poor access to quality health care. PEARLS participant engagement occurred through healthy aging presentations at low-income housing, social service agencies, and health care organizations, where participants received fresh fruit and vegetable boxes and completed brief depression screening to assess eligibility.
Steinman L.E., Gasca A., Hoeft T.J., Raue P.J., Henderson S., Perez R., Huerta A., Fajardo A., Vredevoogd M.A., James K., Hinton L., Rath L, & Unutzer J. (2023). “We are the sun for our community:” Partnering with community health workers/promotores to adapt, deliver and evaluate a home-based collaborative care model to improve equity in access to quality depression care for older U.S. Latino adults who are underserved. Frontiers in Public Health, 11, 1079319.
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Publication 2023
6H,8H-3,4-dihydropyrimido(4,5-c)(1,2)oxazin-7-one Aged Community Health Workers Eligibility Determination Fruit Hispanic or Latino Immigrants Mental Health Mexican Americans Multiple Chronic Conditions Quality of Health Care Tongue Vegetables
5
Feasibility of Multicomponent Intervention for Older Adults Polypharmacy
The primary objective of this pilot study is to explore the feasibility and acceptability of an evidence-based, multicomponent, interprofessional intervention program with the support of informal caregivers to decrease MRPs among polymedicated home-dwelling older adults with multiple chronic conditions. As per Eldridge et al [42 (link)], a feasibility study “asks whether something can be done, should we proceed with it, and if so, how.” Therefore, in line with the Medical Research Council’s framework guidance [39 (link)], our study will gather information about the feasibility of recruitment, participant retention, intervention adherence (adherence to instructions by participants), intervention fidelity, and intervention dose [36 (link),43 (link)] (Textbox 1 [44 ]). The quantitative cutoff point for our feasibility outcomes will be successful with at least 70% of participants [45 (link)]. Given the older adult participants’ advanced age and chronic health conditions, the number of withdrawals for health reasons may be higher than in other studies. Acceptability will be assessed by measuring participants’ satisfaction with each assessment instrument, home visits, and the targeted education plan. The secondary objective is to test the different data collection tools planned for use in the full study.
According to the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) recommendations [48 ], if the pilot study is conclusive, then the full-scale OptiMed study will adopt the standardized Core Outcome Set for its clinical trials of a medication review for multimorbid older adult patients with polypharmacy, as previously reported by Beuscart et al [49 (link)] in 2018 (Multimedia Appendix 4).
Pereira F., Dixe M.D., Gonçalves Pereira S., Meyer-Massetti C, & Verloo H. (2023). An Intervention Program to Reduce Medication-Related Problems Among Polymedicated Home-Dwelling Older Adults (OptiMed): Protocol for a Pre-Post, Multisite, Pilot, and Feasibility Study. JMIR Research Protocols, 12, e39130.
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Publication 2023
Aged Chronic Condition Informal Caregivers Medication Review Multiple Chronic Conditions Patients Polypharmacy Retention (Psychology) Satisfaction Visit, Home

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More about "Multiple Chronic Conditions"

Multimorbidity, Comorbidities, Polypharmacy, Chronic Disease Management, Complex Health Conditions, Geriatric Health, Disability, Quality of Life, Patient-Centered Care, Clinical Decision Support, Healthcare Utilization, Medication Adherence, Self-Management, Interdisciplinary Care, Precision Medicine, Big Data Analytics, SPSS, Stata, SAS, Statistical Analysis, Qualitative Research, NVivo.
Multiple Chronic Conditions (MCCs) refer to the coexistence of two or more long-term health problems within an individual.
These intricate health states present unique challenges in management and treatment, as the interactions and compounding effects of multiple conditions can significantly impact patient outcomes.
Researchers studying MCCs must navigate a vast landscape of research protocols to identify the most effective approaches.
PubCompare.ai, an innovative AI-powered platform, streamlines this process by helping researchers easily locate relevant protocols from literature, pre-prints, and patents, while leveraging AI-driven comparisons to identify the optimal protocols and products.
By elevating the research process, PubCompare.ai empowers researchers to uncover valuable insights and deliver impactful findings in the field of multi-chronic condition management.
Utilizing advanced statistical software like SPSS, Stata, SAS, and ChemStation, along with qualitative analysis tools like NVivo, researchers can gain deeper insights into the complexities of MCCs.
These technologies enable the integration of big data analytics, precision medicine, and patient-centered care approaches to develop more effective, personalized interventions.
By streamlining the research process and empowering researchers to identify the most promising protocols and products, PubCompare.ai is poised to revolutionize the field of multi-chronic condition management.
With its user-friendly interface and AI-driven capabilities, researchers can focus on delivering groundbreaking findings that improve the lives of individuals living with complex health conditions.