The effect of technology-supported, multidisease care management on the mortality and hospitalization of seniors

Care models for individuals with chronic multimorbidity: lessons for low- and middle-income countries

BACKGROUND

Patients with multiple long-term conditions requires understanding the existing care models to address their complex and multifaceted health needs. However, current literature lacks a comprehensive overview of the essential components, impacts, challenges, and facilitators of these care models, prompting this scoping review.

METHODS

A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-analysis Extension for Scoping Reviews guideline. Our search encompassed articles from PubMed, Web of Science, EMBASE, SCOPUS, and Google Scholar. The World Health Organization's health system framework was utilized to synthesis the findings. This framework comprises six building blocks (service delivery, health workforce, health information systems, access to essential medicines, financing, and leadership/governance) and eight key characteristics of good service delivery models (access, coverage, quality, safety, improved health, responsiveness, social and financial risk protection, and improved efficiency). Findings were synthesized qualitatively to identify components, impacts, barriers, and facilitators of care models.

RESULTS

A care model represents various collective interventions in the healthcare delivery aimed at achieving desired outcomes. The names of these care models are derived from core activities or major responsibilities, involved healthcare teams, diseases conditions, eligible clients, purposes, and care settings. Notable care models include the Integrated, Collaborative, Integrated-Collaborative, Guided, Nurse-led, Geriatric, and Chronic care models, as well as All-inclusive Care Model for the Elderly, IMPACT clinic, and Geriatric Patient-Aligned Care Teams (GeriPACT). Other care models (include Care Management Plus, Value Stream Mapping, Preventive Home Visits, Transition Care, Self-Management, and Care Coordination) have supplemented the main ones. Care models improved quality of care (such as access, patient-centeredness, timeliness, safety, efficiency), cost of care, and quality of life for patients that were facilitated by presence of shared mission, system and function integration, availability of resources, and supportive tools.

CONCLUSIONS

Care models were implemented for the purpose of enhancing quality of care, health outcomes, cost efficiency, and patient satisfaction by considering careful recruitment of eligible clients, appropriate selection of service delivery settings, and robust organizational arrangements involving leadership roles, healthcare teams, financial support, and health information systems. The distinct team compositions and their roles in service provision processes differentiate care models.

Critical Elements of Care Coordination for Older Persons in Rural Communities: An Evaluation of the Iowa Return to Community Service Demonstration

We evaluated the Iowa Return to Community, a service demonstration designed to coordinate care and reduce preventable healthcare utilization among at-risk older persons living at home in rural communities. During 2021, 262 older persons elected to participate in the IRTC program. Individuals who participated were more likely to live in micropolitan areas (OR = 2.30, 95% CI 1.34-3.95) relative to metropolitan locations. Individuals who used recommended services were more likely to be men (OR 3.65, 95% CI 1.16-11.51) and more likely to live in rural (OR 17.48, 95% CI 1.37-223.68) and micropolitan areas (OR 3.17, 95% CI 1.00-10.05). However, prevention of health care use corresponded more with consistent and prolonged IRTC program engagement rather than volume of service use. The IRTC constitutes a population aging and rural health strategy to reduce unnecessary health care use while supporting individual preferences to remain at home.

Longer Multimorbidity Intervals Are Associated With Lower Mortality in Diabetes: A Whole-Population Nested Case-Control Study

BACKGROUND

Approximately two-thirds of diabetes patients develop multimorbidity, which is associated with increased mortality. We aimed to examine whether, and to what extent, the time interval between pre-existing diabetes and a second chronic disease may be associated with the risk of mortality.

METHODS

We carried out a territory-wide nested case-control study using incidence density sampling, utilizing electronic health records from Hong Kong's public healthcare facilities. Among 158 732 patients first diagnosed with diabetes from January 1, 2010 to December 31, 2012 and subsequently developed multimorbidity as of December 31, 2019, we extracted those who died before December 31, 2019 as case participants. For each participant, we randomly matched with up to 4 people of the same sex, multimorbidity age, and second chronic condition who had not died after going through the same survival period of the case participant. Multimorbidity interval was included as a continuous variable. We used conditional logistic regression to estimate adjusted odds ratios (aOR) for mortality.

RESULTS

In total, 3508 case participants were matched with 14 032 control participants. Conditional logistic regression showed there were 19%-reduced odds of mortality following the extension of multimorbidity interval by 1 year. Similar associations were observed in men, women, people aged 64 years or younger, and older people aged 65 years or more.

CONCLUSIONS

Delayed multimorbidity among patients living with diabetes may be related to a lower risk of mortality. This study suggests that we should focus on mitigating and lowering the risk of multimorbidity in clinical management of diabetes to reduce further complication and mortality.

Assessment of the Effectiveness, Socio-Economic Impact and Implementation of a Digital Solution for Patients with Advanced Chronic Diseases: The ADLIFE Study Protocol

Due to population ageing and medical advances, people with advanced chronic diseases (ACD) live longer. Such patients are even more likely to face either temporary or permanent reduced functional reserve, which typically further increases their healthcare resource use and the burden of care on their caregiver(s). Accordingly, these patients and their caregiver(s) may benefit from integrated supportive care provided via digitally supported interventions. This approach may either maintain or improve their quality of life, increase their independence, and optimize the healthcare resource use from early stages. ADLIFE is an EU-funded project, aiming to improve the quality of life of older people with ACD by providing integrated personalized care via a digitally enabled toolbox. Indeed, the ADLIFE toolbox is a digital solution which provides patients, caregivers, and health professionals with digitally enabled, integrated, and personalized care, supporting clinical decisions, and encouraging independence and self-management. Here we present the protocol of the ADLIFE study, which is designed to provide robust scientific evidence on the assessment of the effectiveness, socio-economic, implementation, and technology acceptance aspects of the ADLIFE intervention compared to the current standard of care (SoC) when applied in real-life settings of seven different pilot sites across six countries. A quasi-experimental trial following a multicenter, non-randomized, non-concurrent, unblinded, and controlled design will be implemented. Patients in the intervention group will receive the ADLIFE intervention, while patients in the control group will receive SoC. The assessment of the ADLIFE intervention will be conducted using a mixed-methods approach.

Multimorbidity, disease count, mortality and emergency care use in persons attending the emergency department: a cross-sectional data-linkage study

Background

Multimorbidity (two or more concurrent chronic conditions) is associated with poorer health outcomes and increased healthcare utilisation in primary care and general populations. Less is known about the prevalence of multimorbidity in emergency department attenders, or its association with poor outcomes in this population.

Aim

This study sought to explore the relationship between multimorbidity, mortality and health-care utilisation in a large urban cohort of persons attending emergency departments.

Methods

Validated algorithms for the identification of 28 chronic conditions from ICD-10 codes were deployed on a cross-sectional sample of patients attending emergency departments in Glasgow, Scotland between April 2019 and March 2020. Analysis was conducted on complete cases (n=63,328) and compared with results from data with imputed missing values (n=75,723). Models adjusted for age, sex, deprivation and ethnicity were fitted to test for the association between (i) multimorbidity, (ii) complex multimorbidity, (iii) disease count and the following outcomes: admission to hospital, reattendance at 30 and 90 days, and death during admission.

Results

Multimorbidity, complex multimorbidity and disease count were significantly associated with hospital admission and emergency department reattendance. Those with 1-3 conditions were at increased risk of inpatient mortality.

Conclusion

This study further evidences the impact of multimorbidity and disease burden on health-care use, and mortality to a lesser extent. Deployed algorithms were sufficiently sensitive to detect associations, despite limited access (21 months) to secondary-care data. This should allow for the construction of more robust models to prospectively identify persons at risk of poor outcomes in similar populations.

Predicting Health Risks of Adult Asthmatics Susceptible to Indoor Air Quality Using Improved Logistic and Quantile Regression Models

The increasing global patterns for asthma disease and its associated fiscal burden to healthcare systems demand a change to healthcare processes and the way asthma risks are managed. Patient-centered health care systems equipped with advanced sensing technologies can empower patients to participate actively in their health risk control, which results in improving health outcomes. Despite having data analytics gradually emerging in health care, the path to well established and successful data driven health care services exhibit some limitations. Low accuracy of existing predictive models causes misclassification and needs improvement. In addition, lack of guidance and explanation of the reasons of a prediction leads to unsuccessful interventions. This paper proposes a modeling framework for an asthma risk management system in which the contributions are three fold: First, the framework uses a deep learning technique to improve the performance of logistic regression classification models. Second, it implements a variable sliding window method considering spatio-temporal properties of the data, which improves the quality of quantile regression models. Lastly, it provides a guidance on how to use the outcomes of the two predictive models in practice. To promote the application of predictive modeling, we present a use case that illustrates the life cycle of the proposed framework. The performance of our proposed framework was extensively evaluated using real datasets in which results showed improvement in the model classification accuracy, approximately 11.5–18.4% in the improved logistic regression classification model and confirmed low relative errors ranging from 0.018 to 0.160 in quantile regression model.

Prediction of Future Health Care Utilization Through Note-extracted Psychosocial Factors

BACKGROUND

Persons with multimorbidity (≥2 chronic conditions) face an increased risk of poor health outcomes, especially as they age. Psychosocial factors such as social isolation, chronic stress, housing insecurity, and financial insecurity have been shown to exacerbate these outcomes, but are not routinely assessed during the clinical encounter. Our objective was to extract these concepts from chart notes using natural language processing and predict their impact on health care utilization for patients with multimorbidity.

METHODS

A cohort study to predict the 1-year likelihood of hospitalizations and emergency department visits for patients 65+ with multimorbidity with and without psychosocial factors. Psychosocial factors were extracted from narrative notes; all other covariates were extracted from electronic health record data from a large academic medical center using validated algorithms and concept sets. Logistic regression was performed to predict the likelihood of hospitalization and emergency department visit in the next year.

RESULTS

In all, 76,479 patients were eligible; the majority were White (89%), 54% were female, with mean age 73. Those with psychosocial factors were older, had higher baseline utilization, and more chronic illnesses. The 4 psychosocial factors all independently predicted future utilization (odds ratio=1.27-2.77, C -statistic=0.63). Accounting for demographics, specific conditions, and previous utilization, 3 of 4 of the extracted factors remained predictive (odds ratio=1.13-1.86) for future utilization. Compared with models with no psychosocial factors, they had improved discrimination. Individual predictions were mixed, with social isolation predicting depression and morbidity; stress predicting atherosclerotic cardiovascular disease onset; and housing insecurity predicting substance use disorder morbidity.

DISCUSSION

Psychosocial factors are known to have adverse health impacts, but are rarely measured; using natural language processing, we extracted factors that identified a higher risk segment of older adults with multimorbidity. Combining these extraction techniques with other measures of social determinants may help catalyze population health efforts to address psychosocial factors to mitigate their health impacts.

The Impact of Ambulatory Dementia Care Models on Hospitalization of Persons Living With Dementia: A Systematic Review

This systematic review presents an overview of the existing dementia care models in various ambulatory care settings under three categories (i.e., home- and community-based care models, partnership between health systems and community-based resources, and consultation models) and their impact on hospitalization among Persons Living with Dementia (PLWD). PRISMA guidelines were applied, and our search resulted in a total of 13 studies focusing on 11 care models. Seven studies reported that utilization of dementia care models was associated with a modest reduction in hospitalization among community-residing PLWD. Only two studies reported statistically significant results. Dementia care models that were utilized in specialty ambulatory care settings such as memory care showed more promising results than traditional primary care. To develop a better understanding of how dementia care models can be improved, future studies should explore how confounders (e.g., stage of dementia) influence hospitalization.

A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma

In the United States, ~9% of people have asthma. Each year, asthma incurs high health care cost and many hospital encounters covering 1.8 million emergency room visits and 439,000 hospitalizations. A small percentage of patients with asthma use most health care resources. To improve outcomes and cut resource use, many health care systems use predictive models to prospectively find high-risk patients and enroll them in care management for preventive care. For maximal benefit from costly care management with limited service capacity, only patients at the highest risk should be enrolled. However, prior models built by others miss >50% of true highest-risk patients and mislabel many low-risk patients as high risk, leading to suboptimal care and wasted resources. To address this issue, 3 site-specific models were recently built to predict hospital encounters for asthma, gaining up to >11% better performance. However, these models do not generalize well across sites and patient subgroups, creating 2 gaps before translating these models into clinical use. This paper points out these 2 gaps and outlines 2 corresponding solutions: (1) a new machine learning technique to create cross-site generalizable predictive models to accurately find high-risk patients and (2) a new machine learning technique to automatically raise model performance for poorly performing subgroups while maintaining model performance on other subgroups. This gives a roadmap for future research.

The Transforming Outcomes for Patients Through Medical Home Evaluation and reDesign (TOPMED) Cluster Randomized Controlled Trial: Cost and Utilization Results

BACKGROUND

Primary Care Medical Home (PCMH) redesign efforts are intended to enhance primary care's ability to improve population health and well-being. PCMH transformation that is focused on "high-value elements" (HVEs) for cost and utilization may improve effectiveness.

OBJECTIVES

The objective of this study was to determine if a focus on achieving HVEs extracted from successful primary care transformation models would reduce cost and utilization as compared with a focus on achieving PCMH quality improvement goals.

RESEARCH DESIGN

A stratified, cluster randomized controlled trial with 2 arms. All practices received equal financial incentives, health information technology support, and in-person practice facilitation. Analyses consisted of multivariable modeling, adjusting for the cluster, with difference-in-difference results.

SUBJECTS

Eight primary care clinics that were engaged in PCMH reform.

MEASURES

We examined: (1) total claims payments; (2) emergency department (ED) visits; and (3) hospitalizations among patients during baseline and intervention years.

RESULTS

In total, 16,099 patients met the inclusion criteria. Intervention clinics had significantly lower baseline ED visits (P=0.02) and claims paid (P=0.01). Difference-in-difference showed a decrease in ED visits greater in control than intervention (ED per 1000 patients: +56; 95% confidence interval: +96, +15) with a trend towards decreased hospitalizations in intervention (-15; 95% confidence interval: -52, +21). Costs were not different. In modeling monthly outcome means, the generalized linear mixed model showed significant differences for hospitalizations during the intervention year (P=0.03).

DISCUSSION

The trial had a trend of decreasing hospitalizations, increased ED visits, and no change in costs in the HVE versus quality improvement arms.

The success of behavioral economics in improving patient retention within an intensive primary care practice

Background

A minority of the U.S. population comprises a majority of health care expenses. Health system interventions for high-cost populations aim to improve patient outcomes while reducing costly over-utilization. Missed and inconsistent appointments are associated with poor patient outcomes and increased health care utilization. PEAK Health— Mount Sinai’s intensive primary care clinic for high-cost patients— employed a novel behavioral economics-based intervention to reduce the rate of missed appointments at the practice. Behavioral economics has accomplished numerous successes across the health care field; the effect of a clinic-based behavioral economics intervention on reducing missed appointments has yet to be assessed.

Methods

This was a single-arm, pre-post trial conducted over 1 year involving all active patients at PEAK Health. The intervention consisted of: a) clinic signage, and b) appointment reminder cards containing behavioral economics messaging designed to increase the likelihood patients would complete their subsequent visit; appointment cards (t1) were transitioned to an identical EMR template (t2) at 6 months to boost provider utilization. The primary objective, the success of scheduled appointments, was assessed with visit adherence: the proportion of successful over all scheduled appointments, excluding those cancelled or rescheduled. The secondary objective, the consistency of appointments, was assessed with a 2-month visit constancy rate: the percentage of patients with at least one successful visit every 2 months for 1 year. Both metrics were assessed via a χ² analysis and together define patient retention.

Results

The visit adherence rate increased from 74.7% at baseline to 76.5% (p = .22) during t1 and 78.0% (p = .03) during t2. The 2-month visit constancy rate increased from 59.5% at baseline to 74.3% (p = .01) post-intervention.

Conclusions

A low-resource, clinic-based behavioral economics intervention was capable of improving patient retention within a traditionally high-cost population. A renewed focus on patient retention— employing the metrics described here— could bolster chronic care efforts and significantly improve the outcomes of high-cost programs by reducing the deleterious effects of missed and inconsistent appointments.

Using a Constraint-Based Method to Identify Chronic Disease Patients Who Are Apt to Obtain Care Mostly Within a Given Health Care System: Retrospective Cohort Study

BACKGROUND

For several major chronic diseases including asthma, chronic obstructive pulmonary disease, chronic kidney disease, and diabetes, a state-of-the-art method to avert poor outcomes is to use predictive models to identify future high-cost patients for preemptive care management interventions. Frequently, an American patient obtains care from multiple health care systems, each managed by a distinct institution. As the patient's medical data are spread across these health care systems, none has complete medical data for the patient. The task of building models to predict an individual patient's cost is currently thought to be impractical with incomplete data, which limits the use of care management to improve outcomes. Recently, we developed a constraint-based method to identify patients who are apt to obtain care mostly within a given health care system. Our method was shown to work well for the cohort of all adult patients at the University of Washington Medicine for a 6-month follow-up period. It is unknown how well our method works for patients with various chronic diseases and over follow-up periods of different lengths, and subsequently, whether it is reasonable to perform this predictive modeling task on the subset of patients pinpointed by our method.

OBJECTIVE

To understand our method's potential to enable this predictive modeling task on incomplete medical data, this study assesses our method's performance at the University of Washington Medicine on 5 subgroups of adult patients with major chronic diseases and over follow-up periods of 2 different lengths.

METHODS

We used University of Washington Medicine data for all adult patients who obtained care at the University of Washington Medicine in 2018 and PreManage data containing usage information from all hospitals in Washington state in 2019. We evaluated our method's performance over the follow-up periods of 6 months and 12 months on 5 patient subgroups separately-asthma, chronic kidney disease, type 1 diabetes, type 2 diabetes, and chronic obstructive pulmonary disease.

RESULTS

Our method identified 21.81% (3194/14,644) of University of Washington Medicine adult patients with asthma. Around 66.75% (797/1194) and 67.13% (1997/2975) of their emergency department visits and inpatient stays took place within the University of Washington Medicine system in the subsequent 6 months and in the subsequent 12 months, respectively, approximately double the corresponding percentage for all University of Washington Medicine adult patients with asthma. The performance for adult patients with chronic kidney disease, adult patients with chronic obstructive pulmonary disease, adult patients with type 1 diabetes, and adult patients with type 2 diabetes was reasonably similar to that for adult patients with asthma.

CONCLUSIONS

For each of the 5 chronic diseases most relevant to care management, our method can pinpoint a reasonably large subset of patients who are apt to obtain care mostly within the University of Washington Medicine system. This opens the door to building models to predict an individual patient's cost on incomplete data, which was formerly deemed impractical.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/13783.

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

Background

Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well.

Objective

To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions.

Methods

We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation.

Results

We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years.

Conclusions

Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources.

International Registered Report Identifier (IRRID)

PRR1-10.2196/27065

Higher Medicare Expenditures Are Associated With Better Integrated Care as Perceived by Patients

BACKGROUND

Integrated care that is continuous, coordinated and patient-centered is vital for Medicare beneficiaries, but its relationship to health care expenditures remains unclear.

RESEARCH OBJECTIVE

This study explores-for the first time-the relationship between integrated care, as measured from the patient's perspective, and health care expenditures.

METHODS

Subjects include a sample of continuously eligible fee-for-service Medicare beneficiaries (n=8807) in 2015. Analyses draw on 7 previously validated measures of patient-perceived integrated care from the 2015 Medicare Current Beneficiary Survey. These data are combined with 2015 administrative utilization data that measure health care expenditures. Relationships between patient-perceived integrated care and costs are assessed using generalized linear models with comprehensive control measures.

RESULTS

Patients who perceive more integrated care have higher expenditures for many, but not all, cost categories examined. Aspects of integrated care pertaining to primary provider and specialist care are associated with higher costs in several areas (particularly inpatient costs associated with specialist knowledge of the patient). Office staff members' knowledge of the patient's medical history is associated with lower home health costs.

CONCLUSIONS

Patients who experience their care as more integrated may have higher expenditures on average. Thoughtful policy choices, further research, and innovations that enable patients to perceive integrated care at lower or neutral cost are needed.

Strong and sustainable primary healthcare is associated with a lower risk of hospitalization in high risk patients

In 2004, Germany introduced a program based on voluntary contracting to strengthen the role of general practice care in the healthcare system. Key components include structured management of chronic diseases, coordinated access to secondary care, data-driven quality improvement, computerized clinical decision-support, and capitation-based reimbursement. Our aim was to determine the long-term effects of this program on the risk of hospitalization of specific categories of high-risk patients. Based on insurance claims data, we conducted a longitudinal observational study from 2011 to 2018 in Baden-Wuerttemberg, Germany. Patients were assigned to one or more of four open cohorts (in 2011, elderly, n = 575,363; diabetes mellitus, n = 163,709; chronic heart failure, n = 82,513; coronary heart disease, n = 125,758). Adjusted for key patient characteristics, logistic regression models were used to compare the hospitalization risk of the enrolled patients (intervention group) with patients receiving usual primary care (control group). At the start of the study and throughout long-term follow-up, enrolled patients in the four cohorts had a lower risk of all-cause hospitalization and ambulatory, care-sensitive hospitalization. Among patients with chronic heart failure and coronary heart disease, the program was associated with significantly reduced risk of cardiovascular-related hospitalizations across the eight observed years. The effect of the program also increased over time. Over the longer term, the results indicate that strengthening primary care could be associated with a substantial reduction in hospital utilization among high-risk patients.

Interventions for improving outcomes in patients with multimorbidity in primary care and community settings

BACKGROUND

Many people with chronic disease have more than one chronic condition, which is referred to as multimorbidity. The term comorbidity is also used but this is now taken to mean that there is a defined index condition with other linked conditions, for example diabetes and cardiovascular disease. It is also used when there are combinations of defined conditions that commonly co-exist, for example diabetes and depression. While this is not a new phenomenon, there is greater recognition of its impact and the importance of improving outcomes for individuals affected. Research in the area to date has focused mainly on descriptive epidemiology and impact assessment. There has been limited exploration of the effectiveness of interventions to improve outcomes for people with multimorbidity.

OBJECTIVES

To determine the effectiveness of health-service or patient-oriented interventions designed to improve outcomes in people with multimorbidity in primary care and community settings. Multimorbidity was defined as two or more chronic conditions in the same individual.

SEARCH METHODS

We searched MEDLINE, EMBASE, CINAHL and seven other databases to 28 September 2015. We also searched grey literature and consulted experts in the field for completed or ongoing studies.

SELECTION CRITERIA

Two review authors independently screened and selected studies for inclusion. We considered randomised controlled trials (RCTs), non-randomised clinical trials (NRCTs), controlled before-after studies (CBAs), and interrupted time series analyses (ITS) evaluating interventions to improve outcomes for people with multimorbidity in primary care and community settings. Multimorbidity was defined as two or more chronic conditions in the same individual. This includes studies where participants can have combinations of any condition or have combinations of pre-specified common conditions (comorbidity), for example, hypertension and cardiovascular disease. The comparison was usual care as delivered in that setting.

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted data from the included studies, evaluated study quality, and judged the certainty of the evidence using the GRADE approach. We conducted a meta-analysis of the results where possible and carried out a narrative synthesis for the remainder of the results. We present the results in a 'Summary of findings' table and tabular format to show effect sizes across all outcome types.

MAIN RESULTS

We identified 17 RCTs examining a range of complex interventions for people with multimorbidity. Nine studies focused on defined comorbid conditions with an emphasis on depression, diabetes and cardiovascular disease. The remaining studies focused on multimorbidity, generally in older people. In 11 studies, the predominant intervention element was a change to the organisation of care delivery, usually through case management or enhanced multidisciplinary team work. In six studies, the interventions were predominantly patient-oriented, for example, educational or self-management support-type interventions delivered directly to participants. Overall our confidence in the results regarding the effectiveness of interventions ranged from low to high certainty. There was little or no difference in clinical outcomes (based on moderate certainty evidence). Mental health outcomes improved (based on high certainty evidence) and there were modest reductions in mean depression scores for the comorbidity studies that targeted participants with depression (standardized mean difference (SMD) -0.41, 95% confidence interval (CI) -0.63 to -0.2). There was probably a small improvement in patient-reported outcomes (moderate certainty evidence). The intervention may make little or no difference to health service use (low certainty evidence), may slightly improve medication adherence (low certainty evidence), probably slightly improves patient-related health behaviours (moderate certainty evidence), and probably improves provider behaviour in terms of prescribing behaviour and quality of care (moderate certainty evidence). Cost data were limited.

AUTHORS' CONCLUSIONS

This review identifies the emerging evidence to support policy for the management of people with multimorbidity and common comorbidities in primary care and community settings. There are remaining uncertainties about the effectiveness of interventions for people with multimorbidity in general due to the relatively small number of RCTs conducted in this area to date, with mixed findings overall. It is possible that the findings may change with the inclusion of large ongoing well-organised trials in future updates. The results suggest an improvement in health outcomes if interventions can be targeted at risk factors such as depression in people with co-morbidity.

Performance improvement of machine learning techniques predicting the association of exacerbation of peak expiratory flow ratio with short term exposure level to indoor air quality using adult asthmatics clustered data

Large-scale data sources, remote sensing technologies, and superior computing power have tremendously benefitted to environmental health study. Recently, various machine-learning algorithms were introduced to provide mechanistic insights about the heterogeneity of clustered data pertaining to the symptoms of each asthma patient and potential environmental risk factors. However, there is limited information on the performance of these machine learning tools. In this study, we compared the performance of ten machine-learning techniques. Using an advanced method of imbalanced sampling (IS), we improved the performance of nine conventional machine learning techniques predicting the association between exposure level to indoor air quality and change in patients’ peak expiratory flow rate (PEFR). We then proposed a deep learning method of transfer learning (TL) for further improvement in prediction accuracy. Our selected final prediction techniques (TL1_IS or TL2-IS) achieved a balanced accuracy median (interquartile range) of 66(56~76) % for TL1_IS and 68(63~78) % for TL2_IS. Precision levels for TL1_IS and TL2_IS were 68(62~72) % and 66(62~69) % while sensitivity levels were 58(50~67) % and 59(51~80) % from 25 patients which were approximately 1.08 (accuracy, precision) to 1.28 (sensitivity) times increased in terms of performance outcomes, compared to NN_IS. Our results indicate that the transfer machine learning technique with imbalanced sampling is a powerful tool to predict the change in PEFR due to exposure to indoor air including the concentration of particulate matter of 2.5 μm and carbon dioxide. This modeling technique is even applicable with small-sized or imbalanced dataset, which represents a personalized, real-world setting.

Typology of organizational innovation components: building blocks to improve access to primary healthcare for vulnerable populations

BACKGROUND

Achieving equity of access to primary healthcare requires organizations to implement innovations tailored to the specific needs and abilities of vulnerable populations. However, designing pro-vulnerable innovations is challenging without knowledge of the range of possible innovations tailored to vulnerable populations' needs. To better support decision-makers, we aimed to develop a typology of pro-vulnerable organizational innovation components - akin to "building blocks" that could be combined in different ways into new complex innovations or added to existing organizational processes to improve access to primary healthcare.

METHODS

To develop the typology, we used data from a previously conducted a) scoping review (2000-2014, searched Medline, Embase, CINAHL, citation tracking, n = 90 articles selected), and b) environmental scan (2014, online survey via social networks, n = 240 innovations). We conducted a typological analysis of the data. Our initial typology yielded 48 components, classified according to accessibility dimensions from the Patient-Centred Accessibility Framework. The initial typology was then field-tested for relevance and usability by health system stakeholders and refined from 2014 to 2018 (e.g., combined similar components, excluded non-organizational components).

RESULTS

The selected articles (n = 90 studies) and survey responses (n = 240 innovations) were mostly from the USA, Canada, Australia and the UK. Innovations targeted populations with various vulnerabilities (e.g., low income, chronic illness, Indigenous, homeless, migrants, refugees, ethnic minorities, uninsured, marginalized groups, mental illness, etc.). Our final typology had 18 components of organizational innovations, which principally addressed Availability & Accommodation (7/18), Approachability (6/18), and Acceptability (3/18). Components included navigation & information, community health worker, one-stop-shop, case management, group visits, defraying costs, primary healthcare brokerage, etc. CONCLUSIONS: This typology offers a comprehensive menu of potential components that can help inform the design of pro-vulnerable organizational innovations. Component classification according to the accessibility dimensions of the Patient-Centred Accessibility Framework is useful to help target access needs. Components can be combined into complex innovations or added to existing organizational processes to meet the access needs of vulnerable populations in specific contexts.

The Effectiveness of Patient-Centred Medical Home-Based Models of Care versus Standard Primary Care in Chronic Disease Management: A Systematic Review and Meta-Analysis of Randomised and Non-Randomised Controlled Trials

Patient-centred care by a coordinated primary care team may be more effective than standard care in chronic disease management. We synthesised evidence to determine whether patient-centred medical home (PCMH)-based care models are more effective than standard general practitioner (GP) care in improving biomedical, hospital, and economic outcomes. MEDLINE, CINAHL, Embase, Cochrane Library, and Scopus were searched to identify randomised (RCTs) and non-randomised controlled trials that evaluated two or more principles of PCMH among primary care patients with chronic diseases. Study selection, data extraction, quality assessment using Joanna Briggs Institute (JBI) appraisal tools, and grading of evidence using Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach were conducted independently. A quantitative synthesis, where possible, was pooled using random effects models and the effect size estimates of standardised mean differences (SMDs) and odds ratios (ORs) with 95% confidence intervals were reported. Of the 13,820 citations, we identified 78 eligible RCTs and 7 quasi trials which included 60,617 patients. The findings suggested that PCMH-based care was associated with significant improvements in depression episodes (SMD -0.24; 95% CI -0.35, -0.14; I2 = 76%) and increased odds of remission (OR 1.79; 95% CI 1.46, 2.21; I2 = 0%). There were significant improvements in the health-related quality of life (SMD 0.10; 95% CI 0.04, 0.15; I2 = 51%), self-management outcomes (SMD 0.24; 95% CI 0.03, 0.44; I2 = 83%), and hospital admissions (OR 0.83; 95% CI 0.70, 0.98; I2 = 0%). In terms of biomedical outcomes, with exception to total cholesterol, PCMH-based care led to significant improvements in blood pressure, glycated haemoglobin, and low-density lipoprotein cholesterol outcomes. The incremental cost of PCMH care was identified to be small and significantly higher than standard care (SMD 0.17; 95% CI 0.08, 0.26; I2 = 82%). The quality of individual studies ranged from "fair" to "good" by meeting at least 60% of items on the quality appraisal checklist. Additionally, moderate to high heterogeneity across studies in outcomes resulted in downgrading the included studies as moderate or low grade of evidence. PCMH-based care has been found to be superior to standard GP care in chronic disease management. Results of the review have important implications that may inform patient, practice, and policy-level changes.

Health-care use and cost for multimorbid persons with dementia in the National Health and Aging Trends Study

BACKGROUND

Most persons with dementia have multiple chronic conditions; however, it is unclear whether co-existing chronic conditions contribute to health-care use and cost.

METHODS

Persons with dementia and ≥2 chronic conditions using the National Health and Aging Trends Study and Medicare claims data, 2011 to 2014.

RESULTS

Chronic kidney disease and ischemic heart disease were significantly associated with increased adjusted risk ratios of annual hospitalizations, hospitalization costs, and direct medical costs. Depression, hypertension, and stroke or transient ischemic attack were associated with direct medical and societal costs, while atrial fibrillation was associated with increased hospital and direct medical costs. No chronic condition was associated with informal care costs.

CONCLUSIONS

Among older adults with dementia, proactive and ambulatory care that includes informal caregivers along with primary and specialty providers, may offer promise to decrease use and costs for chronic kidney disease, ischemic heart disease, atrial fibrillation, depression, and hypertension.

Proactive Health Support: Exploring Face-to-Face Start-Up Sessions Between Participants and Registered Nurses at the Onset of Telephone-Based Self-Management Support

Proactive Health Support (PaHS) is a large-scale intervention in Denmark carried out by registered nurses (RNs) who provide self-management support to people at risk of hospital admission to enhance their health, coping, and quality of life. PaHS is initiated with a face-to-face session followed by telephone conversations. We aimed to explore the start-up sessions, including if and how the relationship between participants and RNs developed at the onset of PaHS. We used an ethnographic design including observations and informal interviews. Data were analyzed using a phenomenological–hermeneutical approach. The study showed that contexts such as hospitals and RNs legitimized the intervention. Face-to-face communication contributed to credibility, just as the same RN throughout the intervention ensured continuity. We conclude that start-up sessions before telephone-based self-management support enable a trust-based relationship between participants and RNs. Continuous contact with the same RNs throughout the session promoted participation in the intervention.

Core components and impact of nurse-led integrated care models for home-dwelling older people: A systematic review and meta-analysis

BACKGROUND

Integrated care models are highly recommended to overcome care fragmentation in the multimorbid older population. Nurses are potentially ideally situated to fulfil the role as care coordinator to guide integrated care. No systematic review has been conducted specifically focusing on the impact of nurse-led integrated care models for older people in community settings.

OBJECTIVES

To identify core components of nurse-led integrated care models for the home-dwelling older population; to describe patient, service and process outcomes; and to evaluate the impact of these care models on quality of life, activities of daily living, hospitalisation, emergency department visits, nursing home admissions and mortality.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

English, Dutch, French, German and Spanish articles selected from PubMed and CINAHL, hand-search of reference lists of the included articles and grey literature.

REVIEW METHODS

A systematic search was conducted to identify prospective experimental or quasi-experimental studies detailing nurse-led integrated care models in the older home-dwelling population. Study characteristics and reported outcomes were tabulated. The core components of the models were mapped using the Sustainable intEgrated chronic care modeLs for multi-morbidity: delivery, FInancing, and performancE (SELFIE) framework. A random effects meta-analysis was conducted to study the overall effectiveness of the included care models on health-related quality of life, activities of daily living, hospitalisation, emergency department visits, nursing home admissions or mortality. Risk of bias was appraised using the revised Cochrane risk-of-bias tool for randomized trials and ROBINS-I tool for non-randomized studies.

RESULTS

Nineteen studies were included studying a total of 22,168 patients. Core components of integrated care for multimorbid patients such as the involvement of a multidisciplinary team, high risk screening, tailored holistic assessment and an individualized care plan, were performed in a vast majority of the studies; however variability was observed in their operationalisation. Twenty-seven different patient, provider and service outcomes were reported, ranging from 1 to 13 per study. The meta-analyses could not demonstrate a beneficial impact on any of the predefined outcomes. Most included studies were of high risk for several biases.

CONCLUSION

The summarized evidence on nurse-led integrated care models in home-dwelling older people is inconclusive and of low quality. Future studies should include key components of implementation research, such as context analyses, process evaluations and proximal outcomes, to strengthen the evidence-base of nurse-led integrated care.

Interventions for involving older patients with multi-morbidity in decision-making during primary care consultations

BACKGROUND

Older patients with multiple health problems (multi-morbidity) value being involved in decision-making about their health care. However, they are less frequently involved than younger patients. To maximise quality of life, day-to-day function, and patient safety, older patients require support to identify unmet healthcare needs and to prioritise treatment options.

OBJECTIVES

To assess the effects of interventions for older patients with multi-morbidity aiming to involve them in decision-making about their health care during primary care consultations.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL; all years to August 2018), in the Cochrane Library; MEDLINE (OvidSP) (1966 to August 2018); Embase (OvidSP) (1988 to August 2018); PsycINFO (OvidSP) (1806 to August 2018); the Cumulative Index to Nursing and Allied Health Literature (CINAHL) (Ovid) (1982 to September 2008), then in Ebsco (2009 to August 2018); Centre for Reviews and Dissemination Databases (Database of Abstracts and Reviews of Effects (DARE)) (all years to August 2018); the Health Technology Assessment (HTA) Database (all years to August 2018); the Ongoing Reviews Database (all years to August 2018); and Dissertation Abstracts International (1861 to August 2018).

SELECTION CRITERIA

We sought randomised controlled trials (RCTs), cluster-RCTs, and quasi-RCTs of interventions to involve patients in decision-making about their health care versus usual care/control/another intervention, for patients aged 65 years and older with multi-morbidity in primary care.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methodological procedures. Meta-analysis was not possible; therefore we prepared a narrative synthesis.

MAIN RESULTS

We included three studies involving 1879 participants: two RCTs and one cluster-RCT. Interventions consisted of: · patient workshop and individual coaching using behaviour change techniques; · individual patient coaching utilising cognitive-behavioural therapy and motivational interviewing; and · holistic patient review, multi-disciplinary practitioner training, and organisational change. No studies reported the primary outcome 'patient involvement in decision-making' or the primary adverse outcome 'less patient involvement as a result of the intervention'. Comparing interventions (patient workshop and individual coaching, holistic patient review plus practitioner training, and organisational change) to usual care: we are uncertain whether interventions had any effect on patient reports of high self-rated health (risk ratio (RR) 1.40, 95% confidence interval (CI) 0.36 to 5.49; very low-certainty evidence) or on patient enablement (mean difference (MD) 0.60, 95% CI -9.23 to 10.43; very low-certainty evidence) compared with usual care. Interventions probably had no effect on health-related quality of life (adjusted difference in means 0.00, 95% CI -0.02 to 0.02; moderate-certainty evidence) or on medication adherence (MD 0.06, 95% CI -0.05 to 0.17; moderate-certainty evidence) but probably improved the number of patients discussing their priorities (adjusted odds ratio 1.85, 95% CI 1.44 to 2.38; moderate-certainty evidence) and probably increased the number of nurse consultations (incident rate ratio from adjusted multi-level Poisson model 1.37, 95% CI 1.17 to 1.61; moderate-certainty evidence) compared with usual care. Practitioner outcomes were not measured. Interventions were not reported to adversely affect rates of participant death or anxiety, emergency department attendance, or hospital admission compared with usual care. Comparing interventions (patient workshop and coaching, individual patient coaching) to attention-control conditions: we are uncertain whether interventions affect patient-reported high self-rated health (RR 0.38, 95% CI 0.15 to 1.00, favouring attention control, with very low-certainty evidence; RR 2.17, 95% CI 0.85 to 5.52, favouring the intervention, with very low-certainty evidence). We are uncertain whether interventions affect patient enablement and engagement by increasing either patient activation (MD 1.20, 95% CI -8.21 to 10.61; very low-certainty evidence) or self-efficacy (MD 0.29, 95% CI -0.21 to 0.79; very low-certainty evidence); or whether interventions affect the number of general practice visits (MD 0.51, 95% CI -0.34 to 1.36; very low-certainty evidence), compared to attention-control conditions. The intervention may however lead to more patient-reported changes in management of their health conditions (RR 1.82, 95% CI 1.35 to 2.44; low-certainty evidence). Practitioner outcomes were not measured. Interventions were not reported to adversely affect emergency department attendance nor hospital admission when compared with attention control. Comparing one form of intervention with another: not measured. There was 'unclear' risk across studies for performance bias, detection bias, and reporting bias; however, no aspects were 'high' risk. Evidence was downgraded via GRADE, most often because of 'small sample size' and 'evidence from a single study'.

AUTHORS' CONCLUSIONS

Limited available evidence does not allow a robust conclusion regarding the objectives of this review. Whilst patient involvement in decision-making is seen as a key mechanism for improving care, it is rarely examined as an intervention and was not measured by included studies. Consistency in design, analysis, and evaluation of interventions would enable a greater likelihood of robust conclusions in future reviews.

Person-centred, integrated and pro-active care for multi-morbid elderly with advanced care needs: a propensity score-matched controlled trial

BACKGROUND

Person-centred care (PCC) focusing on personalised goals and care plans derived from "What matters to you?" has an impact on single disease outcomes, but studies on multi-morbid elderly are lacking. Furthermore, the combination of PCC, Integrated Care (IC) and Pro-active care are widely recognised as desirable for multi-morbid elderly, yet previous studies focus on single components only, leaving synergies unexplored. The effect of a synergistic intervention, which implements 1) Person-centred goal-oriented care driven by "What matters to you?" with 2) IC and 3) pro-active care is unknown.

METHODS

Inspired by theoretical foundations, complexity science, previous health service research and a patient-driven evaluation of care quality, we designed the Patient-Centred Team (PACT) intervention across primary and secondary care. The PACT team collaborate with the patient to make and deliver a person-centred, integrated and proactive multi-morbidity care-plan. The control group receives conventional care. The study design is a pragmatic six months prospective, controlled clinical trial based on hospital electronic health record data of 439 multi-morbid frail elderly at risk for emergency (re) admissions referred to PACT and 779 propensity score matched controls in Norway, 2014-2016. Outcomes are emergency admissions, the sum of emergency inpatient bed days, 30-day readmissions, planned and emergency outpatient visits and mortality at three and six months follow-up.

RESULTS

The Rate Ratios (RR) for emergency admissions was 0,9 (95%CI: 0,82-0,99), for sum of emergency bed days 0,68 (95%CI:0,52-0,79) and for 30-days emergency readmissions 0,72 (95%CI: 0,41-1,24). RRs were 2,3 (95%CI: 2,02-2,55) and 0,9 (95%CI: 0,68-1,20) for planned and emergency outpatient visits respectively. The RR for death at 3 months was 0,39 (95% CI: 0,22-0,70) and 0,57 (95% CI: 0,34-0,94) at 6 months.

CONCLUSION

Compared with propensity score matched controls, the care process of frail multi-morbid elderly who received the PACT intervention had a reduced risk of high-level emergency care, increased use of low-level planned care, and substantially reduced mortality risk. Further study of process differences between groups is warranted to understand the genesis of these results better.

TRIAL REGISTRATION

ClinicalTrials.gov (identifier: NCT02541474 ), registered Sept 2015.

Effect of Intensive Interdisciplinary Transitional Care for High-Need, High-Cost Patients on Quality, Outcomes, and Costs: a Quasi-Experimental Study

BACKGROUND

Many health systems have implemented team-based programs to improve transitions from hospital to home for high-need, high-cost patients. While preliminary outcomes are promising, there is limited evidence regarding the most effective strategies.

OBJECTIVE

To determine the effect of an intensive interdisciplinary transitional care program emphasizing medication adherence and rapid primary care follow-up for high-need, high-cost Medicaid and Medicare patients on quality, outcomes, and costs.

DESIGN

Quasi-experimental study.

PATIENTS

Among 2235 high-need, high-cost Medicare and Medicaid patients identified during an index inpatient hospitalization in a non-profit health care system in a medically underserved area with complete administrative claims data, 285 participants were enrolled in the SafeMed care transition intervention, and 1950 served as concurrent controls.

INTERVENTIONS

The SafeMed team conducted hospital-based real-time screening, patient engagement, enrollment, enhanced discharge care coordination, and intensive home visits and telephone follow-up for at least 45 days.

MAIN MEASURES

Primary difference-in-differences analyses examined changes in quality (primary care visits, and medication adherence), outcomes (preventable emergency visits and hospitalizations, overall emergency visits, hospitalizations, 30-day readmissions, and hospital days), and medical expenditures.

KEY RESULTS

Adjusted difference-in-differences analyses demonstrated that SafeMed participation was associated with 7% fewer hospitalizations (- 0.40; 95% confidence interval (CI), - 0.73 to - 0.06), 31% fewer 30-day readmissions (- 0.34; 95% CI, - 0.61 to - 0.07), and reduced medical expenditures ($- 8690; 95% CI, $- 14,441 to $- 2939) over 6 months. Improvements were limited to Medicaid patients, who experienced large, statistically significant decreases of 39% in emergency department visits, 25% in hospitalizations, and 79% in 30-day readmissions. Medication adherence was unchanged (+ 2.6%; 95% CI, - 39.1% to 72.9%).

CONCLUSIONS

Care transition models emphasizing strong interdisciplinary patient engagement and rapid primary care follow-up can enable health systems to improve quality and outcomes while reducing costs among high-need, high-cost Medicaid patients.

The effect of computerized decision support systems on cardiovascular risk factors: a systematic review and meta-analysis

Background

Cardiovascular risk management (CVRM) is notoriously difficult because of multi-morbidity and the different phenotypes and severities of cardiovascular disease. Computerized decision support systems (CDSS) enable the clinician to integrate the latest scientific evidence and patient information into tailored strategies. The effect on cardiovascular risk factor management is yet to be confirmed.

Methods

We performed a systematic review and meta-analysis evaluating the effects of CDSS on CVRM, defined as the change in absolute values and attainment of treatment goals of systolic blood pressure (SBP), low density lipoprotein cholesterol (LDL-c) and HbA1c. Also, CDSS characteristics related to more effective CVRM were identified. Eligible articles were methodologically appraised using the Cochrane risk of bias tool. We calculated mean differences, relative risks, and if appropriate (I² < 70%), pooled the results using a random-effects model.

Results

Of the 14,335 studies identified, 22 were included. Four studies reported on SBP, 3 on LDL-c, 10 on CVRM in patients with type II diabetes and 5 on guideline adherence. The CDSSs varied considerably in technical performance and content. Heterogeneity of results was such that quantitative pooling was often not appropriate. Among CVRM patients, the results tended towards a beneficial effect of CDSS, but only LDL-c target attainment in diabetes patients reached statistical significance. Prompting, integration into the electronical health record, patient empowerment, and medication support were related to more effective CVRM.

Conclusion

We did not find a clear clinical benefit from CDSS in cardiovascular risk factor levels and target attainment. Some features of CDSS seem more promising than others. However, the variability in CDSS characteristics and heterogeneity of the results – emphasizing the immaturity of this research area - limit stronger conclusions. Clinical relevance of CDSS in CVRM might additionally be sought in the improvement of shared decision making and patient empowerment.

Electronic supplementary material

The online version of this article (10.1186/s12911-019-0824-x) contains supplementary material, which is available to authorized users.

Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis

Background

Both chronic obstructive pulmonary disease (COPD) and asthma incur heavy health care burdens. To support tailored preventive care for these 2 diseases, predictive modeling is widely used to give warnings and to identify patients for care management. However, 3 gaps exist in current modeling methods owing to rarely factoring in temporal aspects showing trends and early health change: (1) existing models seldom use temporal features and often give late warnings, making care reactive. A health risk is often found at a relatively late stage of declining health, when the risk of a poor outcome is high and resolving the issue is difficult and costly. A typical model predicts patient outcomes in the next 12 months. This often does not warn early enough. If a patient will actually be hospitalized for COPD next week, intervening now could be too late to avoid the hospitalization. If temporal features were used, this patient could potentially be identified a few weeks earlier to institute preventive therapy; (2) existing models often miss many temporal features with high predictive power and have low accuracy. This makes care management enroll many patients not needing it and overlook over half of the patients needing it the most; (3) existing models often give no information on why a patient is at high risk nor about possible interventions to mitigate risk, causing busy care managers to spend more time reviewing charts and to miss suited interventions. Typical automatic explanation methods cannot handle longitudinal attributes and fully address these issues.

Objective

To fill these gaps so that more COPD and asthma patients will receive more appropriate and timely care, we will develop comprehensible data-driven methods to provide accurate early warnings of poor outcomes and to suggest tailored interventions, making care more proactive, efficient, and effective.

Methods

By conducting a secondary data analysis and surveys, the study will: (1) use temporal features to provide accurate early warnings of poor outcomes and assess the potential impact on prediction accuracy, risk warning timeliness, and outcomes; (2) automatically identify actionable temporal risk factors for each patient at high risk for future hospital use and assess the impact on prediction accuracy and outcomes; and (3) assess the impact of actionable information on clinicians’ acceptance of early warnings and on perceived care plan quality.

Results

We are obtaining clinical and administrative datasets from 3 leading health care systems’ enterprise data warehouses. We plan to start data analysis in 2020 and finish our study in 2025.

Conclusions

Techniques to be developed in this study can boost risk warning timeliness, model accuracy, and generalizability; improve patient finding for preventive care; help form tailored care plans; advance machine learning for many clinical applications; and be generalized for many other chronic diseases.

International Registered Report Identifier (IRRID)

PRR1-10.2196/13783

Defining High Value Elements for Reducing Cost and Utilization in Patient-Centered Medical Homes for the TOPMED Trial

Introduction:

Like most patient-centered medical home (PCMH) models, Oregon’s program, the Patient-Centered Primary Care Home (PCPCH), aims to improve care while reducing costs; however, previous work shows that PCMH models do not uniformly achieve desired outcomes. Our objective was to describe a process for refining PCMH models to identify high value elements (HVEs) that reduce cost and utilization.

Methods:

We performed a targeted literature review of each PCPCH core attribute. Value-related concepts and their metrics were abstracted, and studies were assessed for relevance and strength of evidence. Focus groups were held with stakeholders and patients, and themes related to each attribute were identified; calculation of HVE attainment versus PCPCH criteria were completed on eight primary care clinics. Analyses consisted of descriptive statistics and criterion validity with stakeholder input.

Results:

2,126 abstracts were reviewed; 22 met inclusion criteria. From these articles and focus groups of stakeholders/experts (n = 49; 4 groups) and patients (n = 7; 1 group), 12 HVEs were identified that may reduce cost and utilization. At baseline, clinics achieved, on average, 31.3 percent HVE levels compared to an average of 87.9 percent of the 35 PCMH measures.

Discussion:

A subset of measures from the PCPCH model were identified as “high value” in reducing cost and utilization. HVE performance was significantly lower than standard measures, and may better calibrate clinic ability to reduce costs.

Conclusion:

Through literature review and stakeholder engagement, we created a novel set of high value elements for advanced primary care likely to be more related to cost and utilization than other models.

Defining Multiple Chronic Conditions for Quality Measurement

BACKGROUND/OBJECTIVE

Patients with multiple chronic conditions (MCCs) are a critical but undefined group for quality measurement. We present a generally applicable systematic approach to defining an MCC cohort of Medicare fee-for-service beneficiaries that we developed for a national quality measure, risk-standardized rates of unplanned admissions for Accountable Care Organizations.

RESEARCH DESIGN

To define the MCC cohort we: (1) identified potential chronic conditions; (2) set criteria for cohort conditions based on MCC framework and measure concept; (3) applied the criteria informed by empirical analysis, experts, and the public; (4) described "broader" and "narrower" cohorts; and (5) selected final cohort with stakeholder input.

SUBJECTS

Subjects were patients with chronic conditions. Participants included 21.8 million Medicare fee-for-service beneficiaries in 2012 aged 65 years and above with ≥1 of 27 Medicare Chronic Condition Warehouse condition(s).

RESULTS

In total, 10 chronic conditions were identified based on our criteria; 8 of these 10 were associated with notably increased admission risk when co-occurring. A broader cohort (2+ of the 8 conditions) included 4.9 million beneficiaries (23% of total cohort) with an admission rate of 70 per 100 person-years. It captured 53% of total admissions. The narrower cohort (3+ conditions) had 2.2 million beneficiaries (10%) with 100 admissions per 100 person-years and captured 32% of admissions. Most stakeholders viewed the broader cohort as best aligned with the measure concept.

CONCLUSIONS

By systematically narrowing chronic conditions to those most relevant to the outcome and incorporating stakeholder input, we defined an MCC admission measure cohort supported by stakeholders. This approach can be used as a model for other MCC outcome measures.

Engaging High-Need Patients in Intensive Outpatient Programs: A Qualitative Synthesis of Engagement Strategies

BACKGROUND

Intensive outpatient programs address the complex medical, social, and behavioral needs of individuals who account for disproportionate healthcare costs. Despite their promise, the impact of these programs is often diminished due to patient engagement challenges (i.e., low rates of patient participation and partnership in care).

OBJECTIVE

The objective of this study was to identify intensive outpatient program features and strategies that increase high-need patient engagement in these programs.

DESIGN

Qualitative study.

PARTICIPANTS

Twenty program leaders and clinicians from 12 intensive outpatient programs in academic, county, Veterans Affairs, community, and private healthcare settings.

APPROACH

A questionnaire and semi-structured interviews were used to identify common barriers to patient engagement in intensive outpatient programs and strategies employed by programs to address these challenges. We used content analysis methods to code patient engagement barriers and strategies and to identify program features that facilitate patient engagement.

KEY RESULTS

The most common barriers to patient engagement in intensive outpatient programs included physical symptoms/limitations, mental illness, care fragmentation across providers and services, isolation/lack of social support, financial insecurity, and poor social and neighborhood conditions. Patient engagement strategies included concrete services to support communication and use of recommended services, activities to foster patient trust and relationships with program staff, and counseling to build insight and problem-solving capabilities. Program features that were identified as enhancing engagement efforts included: 1) multidisciplinary teams with diverse skills, knowledge, and personalities to facilitate relationship building; 2) adequate staffing and resources to handle the demands of high-need patients; and 3) a philosophy that permitted flexibility and patient-centeredness.

CONCLUSIONS

Promising clinical, interpersonal, and population-based approaches to engaging high-need patients frequently deviate from standard practice and require creative and proactive staff with adequate time, resources, and flexibility to address patients' needs on patients' terms.

Understanding new models of integrated care in developed countries: a systematic review

Background

The NHS has been challenged to adopt new integrated models of service delivery that are tailored to local populations. Evidence from the international literature is needed to support the development and implementation of these new models of care.

Objectives

The study aimed to carry out a systematic review of international evidence to enhance understanding of the mechanisms whereby new models of service delivery have an impact on health-care outcomes.

Design

The study combined rigorous and systematic methods for identification of literature, together with innovative methods for synthesis and presentation of findings.

Setting

Any setting.

Participants

Patients receiving a health-care service and/or staff delivering services.

Interventions

Changes to service delivery that increase integration and co-ordination of health and health-related services.

Main outcome measures

Outcomes related to the delivery of services, including the views and perceptions of patients/service users and staff.

Study design

Empirical work of a quantitative or qualitative design.

Data sources

We searched electronic databases (between October 2016 and March 2017) for research published from 2006 onwards in databases including MEDLINE, EMBASE, PsycINFO, Cumulative Index to Nursing and Allied Health Literature, Science Citation Index, Social Science Citation Index and The Cochrane Library. We also searched relevant websites, screened reference lists and citation searched on a previous review.

Review methods

The identified evidence was synthesised in three ways. First, data from included studies were used to develop an evidence-based logic model, and a narrative summary reports the elements of the pathway. Second, we examined the strength of evidence underpinning reported outcomes and impacts using a comparative four-item rating system. Third, we developed an applicability framework to further scrutinise and characterise the evidence.

Results

We included 267 studies in the review. The findings detail the complex pathway from new models to impacts, with evidence regarding elements of new models of integrated care, targets for change, process change, influencing factors, service-level outcomes and system-wide impacts. A number of positive outcomes were reported in the literature, with stronger evidence of perceived increased patient satisfaction and improved quality of care and access to care. There was stronger UK-only evidence of reduced outpatient appointments and waiting times. Evidence was inconsistent regarding other outcomes and system-wide impacts such as levels of activity and costs. There was an indication that new models have particular potential with patients who have complex needs.

Limitations

Defining new models of integrated care is challenging, and there is the potential that our study excluded potentially relevant literature. The review was extensive, with diverse study populations and interventions that precluded the statistical summary of effectiveness.

Conclusions

There is stronger evidence that new models of integrated care may enhance patient satisfaction and perceived quality and increase access; however, the evidence regarding other outcomes is unclear. The study recommends factors to be considered during the implementation of new models.

Future work

Links between elements of new models and outcomes require further study, together with research in a wider variety of populations.

Study registration

This study is registered as PROSPERO CRD37725.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

What are the effective elements in patient-centered and multimorbidity care? A scoping review

BACKGROUND

Interventions to improve patient-centered care for persons with multimorbidity are in constant growth. To date, the emphasis has been on two separate kinds of interventions, those based on a patient-centered care approach with persons with chronic disease and the other ones created specifically for persons with multimorbidity. Their effectiveness in primary healthcare is well documented. Currently, none of these interventions have synthesized a patient-centered care approach for care for multimorbidity. The objective of this project is to determine the particular elements of patient-centered interventions and interventions for persons with multimorbidity that are associated with positive health-related outcomes for patients.

METHOD

A scoping review was conducted as the method supports the rapid mapping of the key concepts underpinning a research area and the main sources and types of evidence available. A five-stage approach was adopted: (1) identifying the research question; (2) identifying relevant studies; (3) selecting studies; (4) charting the data; and (5) collating, summarizing and reporting results. We searched for interventions for persons with multimorbidity or patient-centered care in primary care. Relevant studies were identified in four systematic reviews (Smith et al. (2012;2016), De Bruin et al. (2012), and Dwamena et al. (2012)). Inductive analysis was performed.

RESULTS

Four systematic reviews and 98 original studies were reviewed and analysed. Elements of interventions can be grouped into three main types and clustered into seven categories of interventions: 1) Supporting decision process and evidence-based practice; 2) Providing patient-centered approaches; 3) Supporting patient self-management; 4) Providing case/care management; 5) Enhancing interdisciplinary team approach; 6) Developing training for healthcare providers; and 7) Integrating information technology. Providing patient-oriented approaches, self-management support interventions and developing training for healthcare providers were the most frequent categories of interventions with the potential to result in positive impact for patients with chronic diseases.

CONCLUSION

This scoping review provides evidence for the adaption of patient-centered interventions for patients with multimorbidity. Findings from this scoping review will inform the development of a toolkit to assist chronic disease prevention and management programs in reorienting patient care.

The Comprehensive Primary Care Initiative: Effects On Spending, Quality, Patients, And Physicians

The Comprehensive Primary Care Initiative (CPC), a health care delivery model developed by the Centers for Medicare and Medicaid Services (CMS), tested whether multipayer support of 502 primary care practices across the country would improve primary care delivery, improve care quality, or reduce spending. We evaluated the initiative's effects on care delivery and outcomes for fee-for-service Medicare beneficiaries attributed to initiative practices, relative to those attributed to matched comparison practices. CPC practices reported improvements in primary care delivery, including care management for high-risk patients, enhanced access, and improved coordination of care transitions. The initiative slowed growth in emergency department visits by 2 percent in CPC practices, relative to comparison practices. However, it did not reduce Medicare spending enough to cover care management fees or appreciably improve physician or beneficiary experience or practice performance on a limited set of Medicare claims-based quality measures. As CMS and other payers increasingly use alternative payment models that reward quality and value, CPC provides important lessons about supporting practices in transforming care.

The effects of integrated care: a systematic review of UK and international evidence

BACKGROUND

Healthcare systems around the world have been responding to the demand for better integrated models of service delivery. However, there is a need for further clarity regarding the effects of these new models of integration, and exploration regarding whether models introduced in other care systems may achieve similar outcomes in a UK national health service context.

METHODS

The study aimed to carry out a systematic review of the effects of integration or co-ordination between healthcare services, or between health and social care on service delivery outcomes including effectiveness, efficiency and quality of care. Electronic databases including MEDLINE; Embase; PsycINFO; CINAHL; Science and Social Science Citation Indices; and the Cochrane Library were searched for relevant literature published between 2006 to March 2017. Online sources were searched for UK grey literature, and citation searching, and manual reference list screening were also carried out. Quantitative primary studies and systematic reviews, reporting actual or perceived effects on service delivery following the introduction of models of integration or co-ordination, in healthcare or health and social care settings in developed countries were eligible for inclusion. Strength of evidence for each outcome reported was analysed and synthesised using a four point comparative rating system of stronger, weaker, inconsistent or limited evidence.

RESULTS

One hundred sixty seven studies were eligible for inclusion. Analysis indicated evidence of perceived improved quality of care, evidence of increased patient satisfaction, and evidence of improved access to care. Evidence was rated as either inconsistent or limited regarding all other outcomes reported, including system-wide impacts on primary care, secondary care, and health care costs. There were limited differences between outcomes reported by UK and international studies, and overall the literature had a limited consideration of effects on service users.

CONCLUSIONS

Models of integrated care may enhance patient satisfaction, increase perceived quality of care, and enable access to services, although the evidence for other outcomes including service costs remains unclear. Indications of improved access may have important implications for services struggling to cope with increasing demand.

TRIAL REGISTRATION

Prospero registration number: 42016037725 .

Understanding What Is Most Important to Individuals with Multiple Chronic Conditions: A Qualitative Study of Patients' Perspectives

BACKGROUND

To improve care for individuals living with multiple chronic conditions, patients and providers must align care planning with what is most important to patients in their daily lives. We have a limited understanding of how to effectively encourage communication about patients' personal values during clinical care.

OBJECTIVE

To identify what patients with multiple chronic conditions describe as most important to their well-being and health.

DESIGN

We interviewed individuals with multiple chronic conditions in their homes and analyzed results qualitatively, guided by grounded theory.

PARTICIPANTS

A total of 31 patients (mean age 68.7 years) participated in the study, 19 of which included the participation of family members. Participants were from Kaiser Permanente Washington, an integrated health care system in Washington state.

APPROACH

Qualitative analysis of home visits, which consisted of semi-structured interviews aided by photo elicitation.

KEY RESULTS

Analysis revealed six domains of what patients described as most important for their well-being and health: principles, relationships, emotions, activities, abilities, and possessions. Personal values were interrelated and rarely expressed as individual values in isolation.

CONCLUSIONS

The domains describe the range and types of personal values multimorbid older adults deem important to well-being and health. Understanding patients' personal values across these domains may be useful for providers when developing, sharing, and following up on care plans.

A Roadmap for Optimizing Asthma Care Management via Computational Approaches

Asthma affects 9% of Americans and incurs US $56 billion in cost, 439,000 hospitalizations, and 1.8 million emergency room visits annually. A small fraction of asthma patients with high vulnerabilities, severe disease, or great barriers to care consume most health care costs and resources. An effective approach is urgently needed to identify high-risk patients and intervene to improve outcomes and to reduce costs and resource use. Care management is widely used to implement tailored care plans for this purpose, but it is expensive and has limited service capacity. To maximize benefit, we should enroll only patients anticipated to have the highest costs or worst prognosis. Effective care management requires correctly identifying high-risk patients, but current patient identification approaches have major limitations. This paper pinpoints these limitations and outlines multiple machine learning techniques to address them, providing a roadmap for future research.

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

Background

To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient’s weight kept rising in the past year). This process becomes infeasible with limited budgets.

Objective

This study’s goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data.

Methods

This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care management allocation and pilot one model with care managers; and (3) perform simulations to estimate the impact of adopting Auto-ML on US patient outcomes.

Results

We are currently writing Auto-ML’s design document. We intend to finish our study by around the year 2022.

Conclusions

Auto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes.

Increasing the Capacity of Primary Care Through Enabling Technology

Primary care is the foundation of effective and high-quality health care. The role of primary care clinicians has expanded to encompass coordination of care across multiple providers and management of more patients with complex conditions. Enabling technology has the potential to expand the capacity for primary care clinicians to provide integrated, accessible care that channels expertise to the patient and brings specialty consultations into the primary care clinic. Furthermore, technology offers opportunities to engage patients in advancing their health through improved communication and enhanced self-management of chronic conditions. This paper describes enabling technologies in four domains (the body, the home, the community, and the primary care clinic) that can support the critical role primary care clinicians play in the health care system. It also identifies challenges to incorporating these technologies into primary care clinics, care processes, and workflow.

Organizational interventions improving access to community-based primary health care for vulnerable populations: a scoping review

Access to community-based primary health care (hereafter, 'primary care') is a priority in many countries. Health care systems have emphasized policies that help the community 'get the right service in the right place at the right time'. However, little is known about organizational interventions in primary care that are aimed to improve access for populations in situations of vulnerability (e.g., socioeconomically disadvantaged) and how successful they are. The purpose of this scoping review was to map the existing evidence on organizational interventions that improve access to primary care services for vulnerable populations. Scoping review followed an iterative process. Eligibility criteria: organizational interventions in Organisation for Economic Cooperation and Development (OECD) countries; aiming to improve access to primary care for vulnerable populations; all study designs; published from 2000 in English or French; reporting at least one outcome (avoidable hospitalization, emergency department admission, or unmet health care needs).

SOURCES

Main bibliographic databases (Medline, Embase, CINAHL) and team members' personal files.

STUDY SELECTION

One researcher selected relevant abstracts and full text papers. Theory-driven synthesis: The researcher classified included studies using (i) the 'Patient Centered Access to Healthcare' conceptual framework (dimensions and outcomes of access to primary care), and (ii) the classification of interventions of the Cochrane Effective Practice and Organization of Care. Using pattern analysis, interventions were mapped in accordance with the presence/absence of 'dimension-outcome' patterns. Out of 8,694 records (title/abstract), 39 studies with varying designs were included. The analysis revealed the following pattern. Results of 10 studies on interventions classified as 'Formal integration of services' suggested that these interventions were associated with three dimensions of access (approachability, availability and affordability) and reduction of hospitalizations (four/four studies), emergency department admissions (six/six studies), and unmet healthcare needs (five/six studies). These 10 studies included seven non-randomized studies, one randomized controlled trial, one quantitative descriptive study, and one mixed methods study. Our results suggest the limited breadth of research in this area, and that it will be feasible to conduct a full systematic review of studies on the effectiveness of the formal integration of services to improve access to primary care services for vulnerable populations.

Risk-standardized Acute Admission Rates Among Patients With Diabetes and Heart Failure as a Measure of Quality of Accountable Care Organizations: Rationale, Methods, and Early Results

BACKGROUND

Population-based measures of admissions among patients with chronic conditions are important quality indicators of Accountable Care Organizations (ACOs), yet there are challenges in developing measures that enable fair comparisons among providers.

METHODS

On the basis of consensus standards for outcome measure development and with expert and stakeholder input on methods decisions, we developed and tested 2 models of risk-standardized acute admission rates (RSAARs) for patients with diabetes and heart failure using 2010-2012 Medicare claims data. Model performance was assessed with deviance R; score reliability was tested with intraclass correlation coefficient. We estimated RSAARs for 114 Shared Savings Program ACOs in 2012 and we assigned ACOs to 3 performance categories: no different, worse than, and better than the national rate.

RESULTS

The diabetes and heart failure cohorts included 6.5 and 2.6 million Medicare Fee-For-Service beneficiaries aged 65 years and above, respectively. Risk-adjustment variables were age, comorbidities, and condition-specific severity variables, but not socioeconomic status or other contextual factors. We selected hierarchical negative binomial models with the outcome of acute, unplanned hospital admissions per 100 person-years. For the diabetes and heart failure measures, respectively, the models accounted for 22% and 12% of the deviance in outcomes and score reliability was 0.89 and 0.81. For the diabetes measure, 51 (44.7%) ACOs were no different, 45 (39.5%) were better, and 18 (15.8%) were worse than the national rate. The distribution of performance for the heart failure measure was 61 (53.5%), 37 (32.5%), and 16 (14.0%), respectively.

CONCLUSION

Measures of RSAARs for patients with diabetes and heart failure meet criteria for scientific soundness and reveal important variation in quality across ACOs.

Interventions for improving outcomes in patients with multimorbidity in primary care and community settings

BACKGROUND

Many people with chronic disease have more than one chronic condition, which is referred to as multimorbidity. The term comorbidity is also used but this is now taken to mean that there is a defined index condition with other linked conditions, for example diabetes and cardiovascular disease. It is also used when there are combinations of defined conditions that commonly co-exist, for example diabetes and depression. While this is not a new phenomenon, there is greater recognition of its impact and the importance of improving outcomes for individuals affected. Research in the area to date has focused mainly on descriptive epidemiology and impact assessment. There has been limited exploration of the effectiveness of interventions to improve outcomes for people with multimorbidity.

OBJECTIVES

To determine the effectiveness of health-service or patient-oriented interventions designed to improve outcomes in people with multimorbidity in primary care and community settings. Multimorbidity was defined as two or more chronic conditions in the same individual.

SEARCH METHODS

We searched MEDLINE, EMBASE, CINAHL and seven other databases to 28 September 2015. We also searched grey literature and consulted experts in the field for completed or ongoing studies.

SELECTION CRITERIA

Two review authors independently screened and selected studies for inclusion. We considered randomised controlled trials (RCTs), non-randomised clinical trials (NRCTs), controlled before-after studies (CBAs), and interrupted time series analyses (ITS) evaluating interventions to improve outcomes for people with multimorbidity in primary care and community settings. Multimorbidity was defined as two or more chronic conditions in the same individual. This includes studies where participants can have combinations of any condition or have combinations of pre-specified common conditions (comorbidity), for example, hypertension and cardiovascular disease. The comparison was usual care as delivered in that setting.

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted data from the included studies, evaluated study quality, and judged the certainty of the evidence using the GRADE approach. We conducted a meta-analysis of the results where possible and carried out a narrative synthesis for the remainder of the results. We present the results in a 'Summary of findings' table and tabular format to show effect sizes across all outcome types.

MAIN RESULTS

We identified 18 RCTs examining a range of complex interventions for people with multimorbidity. Nine studies focused on defined comorbid conditions with an emphasis on depression, diabetes and cardiovascular disease. The remaining studies focused on multimorbidity, generally in older people. In 12 studies, the predominant intervention element was a change to the organisation of care delivery, usually through case management or enhanced multidisciplinary team work. In six studies, the interventions were predominantly patient-oriented, for example, educational or self-management support-type interventions delivered directly to participants. Overall our confidence in the results regarding the effectiveness of interventions ranged from low to high certainty. There was little or no difference in clinical outcomes (based on moderate certainty evidence). Mental health outcomes improved (based on high certainty evidence) and there were modest reductions in mean depression scores for the comorbidity studies that targeted participants with depression (standardized mean difference (SMD) -2.23, 95% confidence interval (CI) -2.52 to -1.95). There was probably a small improvement in patient-reported outcomes (moderate certainty evidence) although two studies that specifically targeted functional difficulties in participants had positive effects on functional outcomes with one of these studies also reporting a reduction in mortality at four year follow-up (Int 6%, Con 13%, absolute difference 7%). The intervention may make little or no difference to health service use (low certainty evidence), may slightly improve medication adherence (low certainty evidence), probably slightly improves patient-related health behaviours (moderate certainty evidence), and probably improves provider behaviour in terms of prescribing behaviour and quality of care (moderate certainty evidence). Cost data were limited.

AUTHORS' CONCLUSIONS

This review identifies the emerging evidence to support policy for the management of people with multimorbidity and common comorbidities in primary care and community settings. There are remaining uncertainties about the effectiveness of interventions for people with multimorbidity in general due to the relatively small number of RCTs conducted in this area to date, with mixed findings overall. It is possible that the findings may change with the inclusion of large ongoing well-organised trials in future updates. The results suggest an improvement in health outcomes if interventions can be targeted at risk factors such as depression, or specific functional difficulties in people with multimorbidity.

Implementation of an Interdisciplinary, Team-Based Complex Care Support Health Care Model at an Academic Medical Center: Impact on Health Care Utilization and Quality of Life

INTRODUCTION

The Geriatric Resources for the Assessment and Care of Elders (GRACE) program has been shown to decrease acute care utilization and increase patient self-rated health in low-income seniors at community-based health centers.

AIMS

To describe adaptation of the GRACE model to include adults of all ages (named Care Support) and to evaluate the process and impact of Care Support implementation at an urban academic medical center.

SETTING

152 high-risk patients (≥5 ED visits or ≥2 hospitalizations in the past 12 months) enrolled from four medical clinics from 4/29/2013 to 5/31/2014.

PROGRAM DESCRIPTION

Patients received a comprehensive in-home assessment by a nurse practitioner/social worker (NP/SW) team, who then met with a larger interdisciplinary team to develop an individualized care plan. In consultation with the primary care team, standardized care protocols were activated to address relevant key issues as needed.

PROGRAM EVALUATION

A process evaluation based on the Consolidated Framework for Implementation Research identified key adaptations of the original model, which included streamlining of standardized protocols, augmenting mental health interventions and performing some assessments in the clinic. A summative evaluation found a significant decline in the median number of ED visits (5.5 to 0, p = 0.015) and hospitalizations (5.5 to 0, p<0.001) 6 months before enrollment in Care Support compared to 6 months after enrollment. In addition, the percent of patients reporting better self-rated health increased from 31% at enrollment to 64% at 9 months (p = 0.002). Semi-structured interviews with Care Support team members identified patients with multiple, complex conditions; little community support; and mild anxiety as those who appeared to benefit the most from the program.

DISCUSSION

It was feasible to implement GRACE/Care Support at an academic medical center by making adaptations based on local needs. Care Support patients experienced significant reductions in acute care utilization and significant improvements in self-rated health.

Key ingredients for implementing intensive outpatient programs within patient-centered medical homes: A literature review and qualitative analysis

BACKGROUND

Intensive outpatient programs aim to transform care while conserving resources for high-need, high-cost patients, but little is known about factors that influence their implementation within patient-centered medical homes (PCMHs).

METHODS

In this mixed-methods study, we reviewed the literature to identify factors affecting intensive outpatient program implementation, then used semi-structured interviews to determine how these factors influenced the implementation of an intensive outpatient program within the Veterans Affairs' (VA) PCMH. Interviewees included facility leadership and clinical staff who were involved in a pilot Intensive Management Patient Aligned Care Team (ImPACT) intervention for high-need, high-cost VA PCMH patents. We classified implementation factors in the literature review and qualitative analysis using the Consolidated Framework for Implementation Research (CFIR).

RESULTS

The literature review (n=9 studies) and analyses of interviews (n=15) revealed key implementation factors in three CFIR domains. First, the Inner Setting (i.e., the organizational and PCMH environment), mostly enabled implementation through a culture of innovation, good networks and communication, and positive tension for change. Second, Characteristics of Individuals, including creativity, flexibility, and interpersonal skills, allowed program staff to augment existing PCMH services. Finally, certain Intervention Characteristics (e.g., adaptability) enabled implementation, while others (e.g., complexity) generated implementation barriers.

CONCLUSIONS

Resources and structural features common to PCMHs can facilitate implementation of intensive outpatient programs, but program success is also dependent on staff creativity and flexibility, and intervention adaptations to meet patient and organizational needs.

IMPLICATIONS

Established PCMHs likely provide resources and environments that permit accelerated implementation of intensive outpatient programs.

LEVEL OF EVIDENCE

V.

An institutional ethnography of chronic pain management in family medicine (COPE) study protocol

BACKGROUND

Patients with chronic conditions and multiple comorbidities represent a growing challenge for health care globally. Improved coordination of care is considered essential for providing more effective and cost-efficient care for these patients with complex needs. Osteoarthritis is one of the most common and debilitating chronic conditions, is the most frequent cause of chronic pain yet osteoarthritis care is often poorly-coordinated. Primary care is usually the first contact for patients requiring relief from chronic pain. Our previous work suggests discordance between the policy goals of improving patient care and the experience of osteoarthritis patients. We plan to investigate the empirical context of the primary care setting by focusing on primary physicians' conceptualizations and performance of their work in treating complex patients with chronic pain. This will allow for an exploration of how primary health care is - or could be - integrated with other services that play an important role in health care delivery.

METHODS

Our study is an Institutional Ethnography of pain management in family medicine, to be carried out in three phases over 3 years from 2014/15 to 2018. Over the first year we will undertake approximately 80 key informant interviews with primary care physicians, other health care providers, policymakers and clinical experts. In the second year we will focus on mobilizing our networks from year one to assist in the collection of key texts which shape the current context of care. These texts will be analyzed by the research team. In the final year of the study we will focus on synthesizing our findings in order to map the social relations informing care. As is standard and optimal in qualitative research, analysis will be concurrent with data collection.

DISCUSSION

Our study will allow us to identify how the work of coordinating care across multiple settings is accomplished, in practice as well as discursively and textually. Ultimately, we will identify links between everyday experience of care for patients with chronic pain, and broader discourses related to health care system inefficiencies, integration and patient-centred care. An expected outcome of this study will be the development of new, or augmentation of existing, models of care, that are based in the local realities of primary care practice.

Community Oncology Medical Homes: Physician-Driven Change to Improve Patient Care and Reduce Costs

Although the patient-centered medical home is a well-established model of care for primary care providers, adoption by specialty providers has been relatively limited. Recently, there has been particular interest in developing specialty medical homes in medical oncology because of practice variation, care fragmentation, and high overall costs of care. In 2012, the Center for Medicare and Medicaid Innovation awarded Innovative Oncology Business Solutions a 3-year grant for their Community Oncology Medical Home (COME HOME) program to implement specialty medical homes in seven oncology practices across the country. We report our early experience and lessons learned.Through September 30, 2014, COME HOME has touched 16,353 unique patients through triage encounters, patient education visits, or application of clinical pathways. We describe the COME HOME model and implementation timeline, profile use of key services, and report patient satisfaction. Using feedback from practice sites, we highlight patient-centered innovations and overall lessons learned.COME HOME incorporates best practices care driven by triage and clinical pathways, team-based care, active disease management, enhanced access and care, as well as financial support for the medical home infrastructure. Information technology plays a central role, supporting both delivery of care and performance monitoring. Volume of service use has grown steadily over time, leveling out in second quarter 2014. The program currently averages 1,265 triage encounters, 440 extended hours visits, and 655 patient education encounters per month.COME HOME offers a patient-centered model of care to improve quality and continuity of care.

Effectiveness of Case Management for 'At Risk' Patients in Primary Care: A Systematic Review and Meta-Analysis

BACKGROUND

An ageing population with multimorbidity is putting pressure on health systems. A popular method of managing this pressure is identification of patients in primary care 'at-risk' of hospitalisation, and delivering case management to improve outcomes and avoid admissions. However, the effectiveness of this model has not been subjected to rigorous quantitative synthesis.

METHODS AND FINDINGS

We carried out a systematic review and meta-analysis of the effectiveness of case management for 'at-risk' patients in primary care. Six bibliographic databases were searched using terms for 'case management', 'primary care', and a methodology filter (Cochrane EPOC group). Effectiveness compared to usual care was measured across a number of relevant outcomes: Health--self-assessed health status, mortality; Cost--total cost of care, healthcare utilisation (primary and non-specialist care and secondary care separately), and; Satisfaction--patient satisfaction. We conducted secondary subgroup analyses to assess whether effectiveness was moderated by the particular model of case management, context, and study design. A total of 15,327 titles and abstracts were screened, 36 unique studies were included. Meta-analyses showed no significant differences in total cost, mortality, utilisation of primary or secondary care. A very small significant effect favouring case management was found for self-reported health status in the short-term (0.07, 95% CI 0.00 to 0.14). A small significant effect favouring case management was found for patient satisfaction in the short- (0.26, 0.16 to 0.36) and long-term (0.35, 0.04 to 0.66). Secondary subgroup analyses suggested the effectiveness of case management may be increased when delivered by a multidisciplinary team, when a social worker was involved, and when delivered in a setting rated as low in initial 'strength' of primary care.

CONCLUSIONS

This was the first meta-analytic review which examined the effects of case management on a wide range of outcomes and considered also the effects of key moderators. Current results do not support case management as an effective model, especially concerning reduction of secondary care use or total costs. We consider reasons for lack of effect and highlight key research questions for the future.

REVIEW PROTOCOL

The review protocol is available as part of the PROSPERO database (registration number: CRD42014010824).

Mental Health Recovery in the Patient-Centered Medical Home

OBJECTIVES

We examined the impact of transitioning clients from a mental health clinic to a patient-centered medical home (PCMH) on mental health recovery.

METHODS

We drew data from a large US County Behavioral Health Services administrative data set. We used propensity score analysis and multilevel modeling to assess the impact of the PCMH on mental health recovery by comparing PCMH participants (n = 215) to clients receiving service as usual (SAU; n = 22,394) from 2011 to 2013 in San Diego County, California. We repeatedly assessed mental health recovery over time (days since baseline assessment range = 0-1639; mean = 186) with the Illness Management and Recovery (IMR) scale and Recovery Markers Questionnaire.

RESULTS

For total IMR (log-likelihood ratio χ(2)[1] = 4696.97; P < .001) and IMR Factor 2 Management scores (log-likelihood ratio χ(2)[1] = 7.9; P = .005), increases in mental health recovery over time were greater for PCMH than SAU participants. Increases on all other measures over time were similar for PCMH and SAU participants.

CONCLUSIONS

Greater increases in mental health recovery over time can be expected when patients with severe mental illness are provided treatment through the PCMH. Evaluative efforts should be taken to inform more widespread adoption of the PCMH.