Implementation of complex adaptive chronic care: the Patient Journey Record system (PaJR)

Understanding clinical complexity in organ and organizational systems: Challenges local and global

INTRODUCTION

Complexity in healthcare systems has been arbitrarily defined as tasks or systems ranging from complicated to intractable, with a general view of these not being 'simple'. Complexity in healthcare systems in first-world countries has been well elucidated, however, data from third-world countries is still scant. MATERIALS AND METHODS: We present four cases each from three different organ systems-chronic kidney disease, alcohol use disorder, and heart failure-in the backdrop of our healthcare organization. We present our analysis of the complexities faced clinically and, in our local healthcare system which led to these events.

RESULTS

Analysis of these cases showed that patients with chronic kidney disease had vertebral-spinal pathologies due to poor infection control measures during haemodialysis. All these patients were young with a long history of secondary hypertension. In patients with alcohol use disorder, a common theme of how government regulations and peer pressure promote alcohol use is analysed. In the four patients with unexplained heart failure, vascular health is viewed as a fractal dimension and the various factors affecting vascular health are elaborated.

CONCLUSION

Complexities exist clinically in making a diagnosis, and organizationally, in the variables and nodes dictating patient outcomes. Clinical complexities cannot be simplified but have to be navigated in an optimized way to improve clinical outcomes.

Development and validation pathways of artificial intelligence tools evaluated in randomised clinical trials

Objective

Given the complexities of testing the translational capability of new artificial intelligence (AI) tools, we aimed to map the pathways of training/validation/testing in development process and external validation of AI tools evaluated in dedicated randomised controlled trials (AI-RCTs).

Methods

We searched for peer-reviewed protocols and completed AI-RCTs evaluating the clinical effectiveness of AI tools and identified development and validation studies of AI tools. We collected detailed information, and evaluated patterns of development and external validation of AI tools.

Results

We found 23 AI-RCTs evaluating the clinical impact of 18 unique AI tools (2009–2021). Standard-of-care interventions were used in the control arms in all but one AI-RCT. Investigators did not provide access to the software code of the AI tool in any of the studies. Considering the primary outcome, the results were in favour of the AI intervention in 82% of the completed AI-RCTs (14 out of 17). We identified significant variation in the patterns of development, external validation and clinical evaluation approaches among different AI tools. A published development study was found only for 10 of the 18 AI tools. Median time from the publication of a development study to the respective AI-RCT was 1.4 years (IQR 0.2–2.2).

Conclusions

We found significant variation in the patterns of development and validation for AI tools before their evaluation in dedicated AI-RCTs. Published peer-reviewed protocols and completed AI-RCTs were also heterogeneous in design and reporting. Upcoming guidelines providing guidance for the development and clinical translation process aim to improve these aspects.

Improving quality in a complex primary care system-An example of refugee care and literature review

RATIONALE, AIMS AND OBJECTIVES

Applying traditional industrial quality improvement (QI) methodologies to primary care is often inappropriate because primary care and its relationship to the healthcare macrosystem has many features of a complex adaptive system (CAS) that is particularly responsive to bottom-up rather than top-down management approaches. We report on a demonstration case study of improvements made in the Family Health Center (FHC) of the JPS Health Network in a refugee patient population that illustrate features of QI in a CAS framework as opposed to a traditional QI approach.

METHODS

We report on changes in health system utilization by new refugee patients of the FHC from 2016 to 2017. We review the literature and summarize relevant theoretical understandings of quality management in complex adaptive systems as it applies to this case example.

RESULTS

Applying CAS principles in the FHC, utilization of the Emergency Department and Urgent Care Center by newly arrived refugee patients before their first clinic visit was reduced by more than half (total visits decreased from 31%-14% of the refugee patients). Our review of the literature demonstrates that traditional algorithmic top-down QI processes are most often unsuccessful in improving even a few single-disease metrics, and increases clinician burnout and penalizes clinicians who care for vulnerable patients. Improvement in a CAS occurs when front-line clinicians identify care gaps and are given the flexibility to learn and self-organize to enable new care processes to emerge, which are created from bottom-up leadership that utilize existing interdependencies and interact with the top levels of the organization through intelligent top-down causation. We give examples of early adapters who are better applying the principles of CAS change to their QI efforts.

CONCLUSIONS

Meaningful improvement in primary care is more likely achieved when the impetus to implement change shifts from top-down to bottom-up.

Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review

BACKGROUND

Artificial intelligence (AI) applications are growing at an unprecedented pace in health care, including disease diagnosis, triage or screening, risk analysis, surgical operations, and so forth. Despite a great deal of research in the development and validation of health care AI, only few applications have been actually implemented at the frontlines of clinical practice.

OBJECTIVE

The objective of this study was to systematically review AI applications that have been implemented in real-life clinical practice.

METHODS

We conducted a literature search in PubMed, Embase, Cochrane Central, and CINAHL to identify relevant articles published between January 2010 and May 2020. We also hand searched premier computer science journals and conferences as well as registered clinical trials. Studies were included if they reported AI applications that had been implemented in real-world clinical settings.

RESULTS

We identified 51 relevant studies that reported the implementation and evaluation of AI applications in clinical practice, of which 13 adopted a randomized controlled trial design and eight adopted an experimental design. The AI applications targeted various clinical tasks, such as screening or triage (n=16), disease diagnosis (n=16), risk analysis (n=14), and treatment (n=7). The most commonly addressed diseases and conditions were sepsis (n=6), breast cancer (n=5), diabetic retinopathy (n=4), and polyp and adenoma (n=4). Regarding the evaluation outcomes, we found that 26 studies examined the performance of AI applications in clinical settings, 33 studies examined the effect of AI applications on clinician outcomes, 14 studies examined the effect on patient outcomes, and one study examined the economic impact associated with AI implementation.

CONCLUSIONS

This review indicates that research on the clinical implementation of AI applications is still at an early stage despite the great potential. More research needs to assess the benefits and challenges associated with clinical AI applications through a more rigorous methodology.

Potential value and impact of data mining and machine learning in clinical diagnostics

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.

Capitated Telehealth Coaching Hospital Readmission Service in Australia: Pragmatic Controlled Evaluation

Background

MonashWatch is a telehealth public hospital outreach pilot service as a component of the Government of Victoria’s statewide redesign initiative called HealthLinks: Chronic Care. Rather than only paying for hospitalizations, projected funding is released earlier to hospitals to allow them to reduce hospitalization costs. MonashWatch introduced a web-based app, Patient Journey Record System, to assess the risk of the journeys of a cohort of patients identified as frequent admitters. Telecare guides call patients using the Patient Journey Record System to flag potential deterioration. Health coaches (nursing and allied health staff) triage risk and adapt care for individuals.

Objective

The aim was a pragmatic controlled evaluation of the impact of MonashWatch on the primary outcome of bed days for acute nonsurgical admissions in the intention-to-treat group versus the usual care group. The secondary outcome was hospital admission rates. The net promoter score was used to gauge satisfaction.

Methods

Patients were recruited into an intention-to-treat group, which included active telehealth and declined/lost/died groups, versus a systematically sampled (4:1) usual care group. A rolling sample of 250-300 active telehealth patients was maintained from December 23, 2016 to June 23, 2019. The outcome—mean bed days in intervention versus control—was adjusted using analysis of covariance for age, gender, admission type, and effective days active in MonashWatch. Time-series analysis tested for trends in change patterns.

Results

MonashWatch recruited 1373 suitable patients who were allocated into the groups: usual care (n=293) and intention-to-treat (n=1080; active telehealth: 471/1080, 43.6%; declined: 485, 44.9%; lost to follow-up: 178 /1080, 10.7%; died: 8/1080, 0.7%). Admission frequency of intention-to-treat compared to that of the usual care group did not significantly improve (P=.05), with a small number of very frequent admitters in the intention-to-treat group. Age, MonashWatch effective days active, and treatment group independently predicted bed days. The analysis of covariance demonstrated a reduction in bed days of 1.14 (P<.001) in the intention-to-treat group compared with that in the usual care group, with 1236 bed days estimated savings. Both groups demonstrated regression-to-the-mean. The downward trend in improved bed days was significantly greater (P<.001) in the intention-to-treat group (Sen slope –406) than in the usual care group (Sen slope –104). The net promoter score was 95% in the active telehealth group compared with typical hospital scores of 77%.

Conclusions

Clinically and statistically meaningful reductions in acute hospital bed days in the intention-to-treat group when compared to that of the usual care group were demonstrated (P<.001), although admission frequency was unchanged with more short stay admissions in the intention-to-treat group. Nonrandomized control selection was a limitation. Nonetheless, MonashWatch was successful in the context of the HealthLinks: Chronic Care capitation initiative and is expanding.

Big data analysis techniques to address polypharmacy in patients - a scoping review

Background

Polypharmacy is a key challenge in healthcare especially in older and multimorbid patients. The use of multiple medications increases the potential for drug interactions and for prescription of potentially inappropriate medications. eHealth solutions are increasingly recommended in healthcare, with big data analysis techniques as a major component. In the following we use the term analysis of big data as referring to the computational analysis of large data sets to find patterns, trends, and associations in large data sets collected from a wide range of sources in contrast to using classical statistics programs. It is hypothesized that big data analysis is able to reveal patterns in patient data that would not be identifiable using conventional methods of data analysis. The aim of this review was to evaluate whether there are existing big data analysis techniques that can help to identify patients consuming multiple drugs and to assist in the reduction of polypharmacy in patients.

Methods

A computerized search was conducted in February 2019 and updated in May 2020, using the PubMed, Web of Science and Cochrane Library databases. The search strategy was defined by the principles of a systematic search, using the PICO scheme. All studies evaluating big data analytics about patients consuming multiple drugs were considered. Two researchers assessed all search results independently to identify eligible studies. The data was then extracted into standardized tables.

Results

A total of 327 studies were identified through the database search. After title and abstract screening, 302 items were removed. Only three studies were identified as addressing big data analysis techniques in patients with polypharmacy. One study extracted antipsychotic polypharmacy data, the second introduced a decision support system to evaluate side-effects in patients with polypharmacy and the third evaluated a decision support system to identify polypharmacy-related problems in individuals.

Conclusions

There are few studies to date which have used big data analysis techniques for identification and management of polypharmacy. There may be a need to further explore interdisciplinary collaboration between computer scientists and healthcare professionals, to develop and evaluate big data analysis techniques that can be implemented to manage polypharmacy.

Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective

Mobile health (m-health) is the term of monitoring the health using mobile phones and patient monitoring devices etc. It has been often deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) and big data analytics have been applied within the m-health for providing an effective healthcare system. Various types of data such as electronic health records (EHRs), medical images, and complicated text which are diversified, poorly interpreted, and extensively unorganized have been used in the modern medical research. This is an important reason for the cause of various unorganized and unstructured datasets due to emergence of mobile applications along with the healthcare systems. In this paper, a systematic review is carried out on application of AI and the big data analytics to improve the m-health system. Various AI-based algorithms and frameworks of big data with respect to the source of data, techniques used, and the area of application are also discussed. This paper explores the applications of AI and big data analytics for providing insights to the users and enabling them to plan, using the resources especially for the specific challenges in m-health, and proposes a model based on the AI and big data analytics for m-health. Findings of this paper will guide the development of techniques using the combination of AI and the big data as source for handling m-health data more effectively.

The Evidence Base for an Ideal Care Pathway for Frail Multimorbid Elderly: Combined Scoping and Systematic Intervention Review

Background

There is a call for bold and innovative action to transform the current care systems to meet the needs of an increasing population of frail multimorbid elderly. International health organizations propose complex transformations toward digitally supported (1) Person-centered, (2) Integrated, and (3) Proactive care (Digi-PIP care). However, uncertainty regarding both the design and effects of such care transformations remain. Previous reviews have found favorable but unstable impacts of each key element, but the maturity and synergies of the combination of elements are unexplored.

Objective

This study aimed to describe how the literature on whole system complex transformations directed at frail multimorbid elderly reflects (1) operationalization of intervention, (2) maturity, (3) evaluation methodology, and (4) effect on outcomes.

Methods

We performed a systematic health service and electronic health literature review of care transformations targeting frail multimorbid elderly. Papers including (1) Person-centered, integrated, and proactive (PIP) care; (2) at least 1 digital support element; and (3) an effect evaluation of patient health and/ or cost outcomes were eligible. We used a previously published ideal for the quality of care to structure descriptions of each intervention. In a secondary deductive-inductive analysis, we collated the descriptions to create an outline of the generic elements of a Digi-PIP care model. The authors then reviewed each intervention regarding the presence of critical elements, study design quality, and intervention effects.

Results

Out of 927 potentially eligible papers, 10 papers fulfilled the inclusion criteria. All interventions idealized Person-centered care, but only one intervention made what mattered to the person visible in the care plan. Care coordinators responsible for a whole-person care plan, shared electronically in some instances, was the primary integrated care strategy. Digitally supported risk stratification and management were the main proactive strategies. No intervention included workflow optimization, monitoring of care delivery, or patient-reported outcomes. All interventions had gaps in the chain of care that threatened desired outcomes. After evaluation of study quality, 4 studies remained. They included outcome analyses on patient satisfaction, quality of life, function, disease process quality, health care utilization, mortality, and staff burnout. Only 2 of 24 analyses showed significant effects.

Conclusions

Despite a strong common-sense belief that the Digi-PIP ingredients are key to sustainable care in the face of the silver tsunami, research has failed to produce evidence for this. We found that interventions reflect a reductionist paradigm, which forces care workers into standardized narrowly focused interventions for complex problems. There is a paucity of studies that meet complex needs with digitally supported flexible and adaptive teamwork. We predict that consistent results from care transformations for frail multimorbid elderly hinges on an individual care pathway, which reflects a synergetic PIP approach enabled by digital support.

Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature

Background

Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals.

Objective

Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain.

Methods

We searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction).

Results

Our literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes.

Conclusions

This review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice.

Are big data analytics helpful in caring for multimorbid patients in general practice? - A scoping review

BACKGROUND

The treatment of multimorbid patients is one crucial task in general practice as multimorbidity is highly prevalent in this setting. However, there is little evidence how to treat these patients and consequently there are but a few guidelines that focus primarily on multimorbidity. Big data analytics are defined as a method that obtains results for high volume data with high variety generated at high velocity. Yet, the explanatory power of these results is not completely understood. Nevertheless, addressing multimorbidity as a complex condition might be a promising field for big data analytics. The aim of this scoping review was to evaluate whether applying big data analytics on patient data does already contribute to the treatment of multimorbid patients in general practice.

METHODS

In January 2018, a review searching the databases PubMed, The Cochrane Library, and Web of Science, using defined search terms for "big data analytics" and "multimorbidity", supplemented by a search of grey literature with Google Scholar, was conducted. Studies were not filtered by type of study, publication year or language. Validity of studies was evaluated independently by two researchers.

RESULTS

In total, 2392 records were identified for screening. After title and abstract screening, six articles were included in the full-text analysis. Of those articles, one reported on a model generated with big data techniques to help caring for one group of multimorbid patients. The other five articles dealt with the analysis of multimorbidity clusters. No article defined big data analytics explicitly.

CONCLUSIONS

Although the usage of the phrase "Big Data" is growing rapidly, there is nearly no practical use case for big data analysis techniques in the treatment of multimorbidity in general practice yet. Furthermore, in publications addressing big data analytics, the term is rarely defined. However, possible models and algorithms to address multimorbidity in the future are already published.

Anticipatory Care in Potentially Preventable Hospitalizations: Making Data Sense of Complex Health Journeys

Purpose: Potentially preventable hospitalizations (PPH) are minimized when adults (usually with multiple morbidities ± frailty) benefit from alternatives to emergency hospital use. A complex systems and anticipatory journey approach to PPH, the Patient Journey Record System (PaJR) is proposed.

Application: PaJR is a web-based service supporting ≥weekly telephone calls by trained lay Care Guides (CG) to individuals at risk of PPH. The Victorian HealthLinks Chronic Care algorithm provides case finding from hospital big data. Prediction algorithms on call data helps optimize emergency hospital use through adaptive and anticipatory care. MonashWatch deployment incorporating PaJR is conducted by Monash Health in its Dandenong urban catchment area, Victoria, Australia.

Theory: A Complex Adaptive Systems (CAS) framework underpins PaJR, and recognizes unique individual journeys, their dependence on historical and biopsychosocial influences, and difficult to predict tipping points. Rosen's modeling relationship and anticipation theory additionally informed the CAS framework with data sense-making and care delivery. PaJR uses perceptions of current and future health (interoception) through ongoing conversations to anticipate possible tipping points. This allows for possible timely intervention in trajectories in the biopsychosocial dimensions of patients as “particulars” in their unique trajectories.

Evaluation: Monash Watch is actively monitoring 272 of 376 intervention patients, with 195 controls over 22 months (ongoing). Trajectories of poor health (SRH) and anticipation of worse/uncertain health (AH), and CG concerns statistically shifted at a tipping point, 3 days before admission in the subset who experienced ≥1 acute admission. The −3 day point was generally consistent across age and gender. Three randomly selected case studies demonstrate the processes of anticipatory and reactive care. PaJR-supported services achieved higher than pre-set targets—consistent reduction in acute bed days (20–25%) vs. target 10% and high levels of patient satisfaction.

Discussion: Anticipatory care is an emerging trajectory data analytic approach that uses human sense-making as its core metric demonstrates improvements in processes and outcomes. Multiple sources can provide big data to inform trajectory care, however simple tailored data collections may prove effective if they embrace human interoception and anticipation. Admission risk may be addressed with a simple data collections including SRH, AH, and CG perceptions, where practical.

Conclusion: Anticipatory care, as operationalized through PaJR approaches applied in MonashWatch, demonstrates processes and outcomes that successfully ameliorate PPH.

What matters in "multimorbidity"? Arguably resilience and personal health experience are central to quality of life and optimizing survival

RATIONALE

Much is written about "multimorbidity" as it is a difficult problem for health systems, as it reflects a complex phenomenon unique to each individual health journey and health service context. This paper proposes the adoption of 2 constructs or knowledge streams into mainstream "multimorbidity" care which are arguably most important to person-centered care-personal health perceptions and resilience.

ANALYSIS

"Multimorbidity" is the manifestation of multiple nonlinear physical, psychosocial, and environmental phenomena in an individual health journey. Multimorbidity encompasses very stable states for the most part together with highly unstable phases that are difficult to manage. Averting or controlling the underlying loss of resilience in instability can be challenging without early warning signals pointing towards tipping points. Monitoring resilience and early warning signals for tipping points is new to health care. Yet what should we monitor in the complexity of multimorbidity? There are multiple and competing health service features and biometrics that can be measured. However, an expanding of literature endorses importance of simply asking a person about their self-rated health in order to provide predictions of their resilience and survival. Interoception, exemplified as self-rated health, arises from internal neurocognitive self-monitoring functions of different internal and external phenomena. Interoception is being to be recognized as predictors and barometers of resilience and survival.

CONCLUSIONS

Two phenomena of human systems-interoception and resilience-can guide care in the complex nature of multimorbidity in unstable health journeys and should be incorporated into clinical practice.

Resilience, health perceptions, (QOL), stressors, and hospital admissions-Observations from the real world of clinical care of unstable health journeys in Monash Watch (MW), Victoria, Australia

RATIONALE, AIMS, AND OBJECTIVES

Monash Watch (MW) aims to reduce potentially preventable hospitalisations in a cohort above a risk "threshold" identified by Health Links Chronic Care (HLCC) algorithms using personal, diagnostic, and service data. MW conducted regular patient monitoring through outbound phone calls using the Patient Journey Record System (PaJR). PaJR alerts are intended to act as a self-reported barometer of stressors, resilience, and health perceptions with more alerts per call indicating greater risk.

AIMS

To describe predictors of PaJR alerts (self-reported from outbound phone calls) and predictors of acute admissions based upon a Theoretical Model for Static and Dynamic Indicators of Acute Admissions.

METHODS

Participants: HLCC cohort with predicted 3+ admissions/year in MW service arm for >40 days; n = 244. Baseline measures-Clinical Frailty Index (CFI); Connor Davis Resilience (CD-RISC): SF-12v2 Health Survey scores Mental (MSC) and Physical (PSC) and ICECAP-O. Dynamic measures: PaJR alerts/call in 10 869 MW records. Acute (non-surgical) admissions from Victorian Admitted Episode database.

ANALYSIS

Logistic regression, correlations, and timeseries homogeneity metrics using XLSTAT.

FINDINGS

Baseline indicators were significantly correlated except SF-12_MCS. SF12-MSC, SF12-PSC and ICECAP-O best predicted PaJR alerts/call (ROC: 0.84). CFI best predicted acute admissions (ROC: 0.66), adding CD-RISC, SF-12_MCS, SF-12_PCS and ICECAP-O with two-way interactions improved model (ROC: 0.70). PaJR alerts were higher ≤10 days preceding acute admissions and significantly correlated with admissions. Patterns in PaJR alerts in four case studies demonstrated dynamic variations signifying risk. Overall, all baseline indicators were explanatory supporting the theoretical model. Timing of PaJR alerts and acute admissions reflecting changing stressors, resilience, and health perceptions were not predicted from baseline indicators but provided a trigger for service interventions.

CONCLUSION

Both static and dynamic indicators representing stressors, resilience, and health perceptions have the potential to inform threshold models of admission risk in ways that could be clinically useful.

Resilience for health-an emergent property of the "health systems as a whole"

Resilience has become a popular term, and its meaning varies widely depending on the context of its use. Its Latin origin, resilire, means "bouncing back"-should bouncing back be understood literally or rather metaphorically in the context of health, illness, dis-ease, and disease? This essay examines ecological, physiological, personal, and health system perspectives inherent in the concept of resilience. It emerges that regardless of the level of aggregation, resilience is a systems property-it is as much a property of each of the subsystems of network physiology, the person, and the health care delivery system as it is a property of the health system as a whole. Given the interdependencies between people, their internal and external environments, and the health service system, strengthening resilience, ie, the ability to positively adapt to challenges and changing circumstances, will require a broad-based public discourse: "How can we strengthen resilience and health for the benefit of people and society at large".

Relevant models and elements of integrated care for multi-morbidity: Results of a scoping review

BACKGROUND

In order to provide adequate care for the growing group of persons with multi-morbidity, innovative integrated care programmes are appearing. The aims of the current scoping review were to i) identify relevant models and elements of integrated care for multi-morbidity and ii) to subsequently identify which of these models and elements are applied in integrated care programmes for multi-morbidity.

METHODS

A scoping review was conducted in the following scientific databases: Cochrane, Embase, PubMed, PsycInfo, Scopus, Sociological Abstracts, Social Services Abstracts, and Web of Science. A search strategy encompassing a) models, elements and programmes, b) integrated care, and c) multi-morbidity was used to identify both models and elements (aim 1) and implemented programmes of integrated care for multi-morbidity (aim 2). Data extraction was done by two independent reviewers. Besides general information on publications (e.g. publication year, geographical region, study design, and target group), data was extracted on models and elements that publications refer to, as well as which models and elements are applied in recently implemented programmes in the EU and US.

RESULTS

In the review 11,641 articles were identified. After title and abstract screening, 272 articles remained. Full text screening resulted in the inclusion of 92 articles on models and elements, and 50 articles on programmes, of which 16 were unique programmes in the EU (n=11) and US (n=5). Wagner's Chronic Care Model (CCM) and the Guided Care Model (GCM) were most often referred to (CCM n=31; GCM n=6); the majority of the other models found were only referred to once (aim 1). Both the CCM and GCM focus on integrated care in general and do not explicitly focus on multi-morbidity. Identified elements of integrated care were clustered according to the WHO health system building blocks. Most elements pertained to 'service delivery'. Across all components, the five elements referred to most often are person-centred care, holistic or needs assessment, integration and coordination of care services and/or professionals, collaboration, and self-management (aim 1). Most (n=10) of the 16 identified implemented programmes for multi-morbidity referred to the CCM (aim 2). Of all identified programmes, the elements most often included were self-management, comprehensive assessment, interdisciplinary care or collaboration, person-centred care and electronic information system (aim 2).

CONCLUSION

Most models and elements found in the literature focus on integrated care in general and do not explicitly focus on multi-morbidity. In line with this, most programmes identified in the literature build on the CCM. A comprehensive framework that better accounts for the complexities resulting from multi-morbidity is needed.

'Multimorbidity' as the manifestation of network disturbances

We argue that 'multimorbidity' is the manifestation of interconnected physiological network processes within an individual in his or her socio-cultural environment. Networks include genomic, metabolomic, proteomic, neuroendocrine, immune and mitochondrial bioenergetic elements, as well as social, environmental and health care networks. Stress systems and other physiological mechanisms create feedback loops that integrate and regulate internal networks within the individual. Minor (e.g. daily hassles) and major (e.g. trauma) stressful life experiences perturb internal and social networks resulting in physiological instability with changes ranging from improved resilience to unhealthy adaptation and 'clinical disease'. Understanding 'multimorbidity' as a complex adaptive systems response to biobehavioural and socio-environmental networks is essential. Thus, designing integrative care delivery approaches that more adequately address the underlying disease processes as the manifestation of a state of physiological dysregulation is essential. This framework can shape care delivery approaches to meet the individual's care needs in the context of his or her underlying illness experience. It recognizes 'multimorbidity' and its symptoms as the end product of complex physiological processes, namely, stress activation and mitochondrial energetics, and suggests new opportunities for treatment and prevention. The future of 'multimorbidity' management might become much more discerning by combining the balancing of physiological dysregulation with targeted personalized biotechnology interventions such as small molecule therapeutics targeting specific cellular components of the stress response, with community-embedded interventions that involve addressing psycho-socio-cultural impediments that would aim to strengthen personal/social resilience and enhance social capital.

Living with chronicity and complexity: Lessons for redesigning case management from patients' life stories - A qualitative study

RATIONALE, AIMS AND OBJECTIVES

Case management is commonly used to provide health care for patients with multiple chronic conditions. However, the most effective form of team organization and the necessary support structures need to be identified. In this respect, patients' views could provide a valuable contribution to improving the design of these services. To analyse the experiences of patients with chronic diseases and of caregivers, in relation to health care services and mechanisms, and to identify means of modelling case management services.

METHODS

The method used was a qualitative study based on life stories, and semi-structured interviews with 18 patients with complex chronic diseases and with their family caregivers, selected by purposeful sampling in primary health care centres in Andalusia (southern Spain) from 2009 to 2011.

RESULTS

Three transition points were clearly identified: the onset and initial adaptation, the beginning of quality-of-life changes, and the final stage, in which the patients' lives are governed by the complexity of their condition. Health care providers have a low level of proactivity with respect to undertaking early measures for health promotion and self-care education. Care is fragmented into a multitude of providers and services, with treatments aimed at specific problems.

CONCLUSIONS

Many potentially valuable interventions in case management, such as information provision, self-care education and coordination between services and providers, are still not provided.

Factors influencing the implementation of chronic care models: A systematic literature review

Background

The increasing prevalence of chronic disease faced by both developed and developing countries is of considerable concern to a number of international organisations. Many of the interventions to address this concern within primary healthcare settings are based on the chronic care model (CCM). The implementation of complex interventions such as CCMs requires careful consideration and planning. Success depends on a number of factors at the healthcare provider, team, organisation and system levels.

Methods

The aim of this systematic review was to systematically examine the scientific literature in order to understand the facilitators and barriers to implementing CCMs within a primary healthcare setting. This review focused on both quantitative and qualitative studies which included patients with chronic disease (cardiovascular disease, chronic kidney disease, chronic respiratory disease, type 2 diabetes mellitus, depression and HIV/AIDS) receiving care in primary healthcare settings, as well as primary healthcare providers such as doctors, nurses and administrators. Papers were limited to those published in English between 1998 and 2013.

Results

The search returned 3492 articles. The majority of these studies were subsequently excluded based on their title or abstract because they clearly did not meet the inclusion criteria for this review. A total of 226 full text articles were obtained and a further 188 were excluded as they did not meet the criteria. Thirty eight published peer-reviewed articles were ultimately included in this review. Five primary themes emerged. In addition to ensuring appropriate resources to support implementation and sustainability, the acceptability of the intervention for both patients and healthcare providers contributed to the success of the intervention. There was also a need to prepare healthcare providers for the implementation of a CCM, and to support patients as the way in which they receive care changes.

Conclusion

This systematic review demonstrated the importance of considering human factors including the influence that different stakeholders have on the success or otherwise of the implementing a CCM.

Electronic supplementary material

The online version of this article (doi:10.1186/s12875-015-0319-5) contains supplementary material, which is available to authorized users.

Effectiveness of chronic care models: opportunities for improving healthcare practice and health outcomes: a systematic review

Background

The increasing prevalence of chronic disease and even multiple chronic diseases faced by both developed and developing countries is of considerable concern. Many of the interventions to address this within primary healthcare settings are based on a chronic care model first developed by MacColl Institute for Healthcare Innovation at Group Health Cooperative.

Methods

This systematic literature review aimed to identify and synthesise international evidence on the effectiveness of elements that have been included in a chronic care model for improving healthcare practices and health outcomes within primary healthcare settings. The review broadens the work of other similar reviews by focusing on effectiveness of healthcare practice as well as health outcomes associated with implementing a chronic care model. In addition, relevant case series and case studies were also included.

Results

Of the 77 papers which met the inclusion criteria, all but two reported improvements to healthcare practice or health outcomes for people living with chronic disease. While the most commonly used elements of a chronic care model were self-management support and delivery system design, there were considerable variations between studies regarding what combination of elements were included as well as the way in which chronic care model elements were implemented. This meant that it was impossible to clearly identify any optimal combination of chronic care model elements that led to the reported improvements.

Conclusions

While the main argument for excluding papers reporting case studies and case series in systematic literature reviews is that they are not of sufficient quality or generalizability, we found that they provided a more detailed account of how various chronic care models were developed and implemented. In particular, these papers suggested that several factors including supporting reflective healthcare practice, sending clear messages about the importance of chronic disease care and ensuring that leaders support the implementation and sustainability of interventions may have been just as important as a chronic care model’s elements in contributing to the improvements in healthcare practice or health outcomes for people living with chronic disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-015-0854-8) contains supplementary material, which is available to authorized users.

Person-centred health care: a critical assessment of current and emerging research approaches

RATIONALE, AIMS AND OBJECTIVES

Person-centred health care is prominent in international health care reforms. A shift to understanding and improving personal care at the point of delivery has generated debates about the nature of the person-centred research agenda. This paper purviews research paradigms that influence current person-centred research approaches and traditions that influence knowledge foundations in the field. It presents a synthesis of the emergent approaches and methodologies and highlights gaps between static academic research and the increasing accessibility of evaluation, informatics and big data from health information systems.

FINDINGS

Paradigms in health services research range from theoretical to atheoretical, including positivist, interpretive, postmodern and pragmatic. Interpretivist (subjective) and positivist (objectivist) paradigms have been historically polarized. Yet, integrative and pragmatic approaches have emerged. Nevertheless, there is a tendency to reductionism, and to reduce personal experiences to metrics in the positivist paradigm. Integrating personalized information into clinical systems is increasingly driven by the pervasive health information technology, which raises many issues about the asymmetry and uncertainty in the flow of information to support personal health journeys. The flux and uncertainty of knowledge between and within paradigmatic or pragmatic approaches highlights the uncertainty and the 'unorder and disorder' in what is known and what it means. Transdisciplinary, complex adaptive systems theory with multi-ontology sense making provides an overarching framework for making sense of the complex dynamics in research progress.

CONCLUSION

A major challenge to current research paradigms is focus on the individualizing of care and enhancing experiences of persons in health settings. There is an urgent need for person-centred research to address this complex process. A transdisciplinary and complex systems approach provides a sense-making framework.

Self-rated health: patterns in the journeys of patients with multi-morbidity and frailty

RATIONALE, AIMS AND OBJECTIVES

Self-rated health (SRH) is a single measure predictor of hospital utilization and health outcomes in epidemiological studies. There have been few studies of SRH in patient journeys in clinical settings. Reduced resilience to stressors, reflected by SRH, exposes older people (complex systems) to the risk of hospitalization. It is proposed that SRH reflects rather than predicts deteriorations and hospital use; with low SRH autocorrelation in time series. The aim was to investigate SRH fluctuations in regular outbound telephone calls (average biweekly) to patients by Care Guides.

METHODS

Descriptive case study using quantitative autoregressive techniques and qualitative case analysis on SRH time series. Fourteen participants were randomly selected from the Patient Journey Record System (PaJR) database. The PaJR database recorded 198 consecutively sampled older multi-morbid patients journeys in three primary care settings. Analysis consisted of triangulation of SRH (0 very poor - 6 excellent) patterns from three analyses: SRH graduations associations with service utilization; time series modelling (autocorrelation, and step ahead forecast); and qualitative categorization of deteriorations.

RESULTS

Fourteen patients reported mean SRH 2.84 (poor-fair) in 818 calls over 13 ± 6.4 months of follow-up. In 24% calls, SRH was poor-fair and significantly associated with hospital use. SRH autocorrelation was low in 14 time series (-0.11 to 0.26) with little difference (χ(2)  = 6.46, P = 0.91) among them. Fluctuations between better and worse health were very common and poor health was associated with hospital use. It is not clear why some patients continued on a downward trajectory, whereas others who destabilized appeared to completely recover, and even improved over time.

CONCLUSION

SRH reflects an individual's complex health trajectory, but as a single measure does not predict when and how deteriorations will occur in this study. Individual patients appear to behave as complex adaptive systems. The dynamics of SRH and its influences in destabilizations warrant further research.

What weekday? How acute? An analysis of reported planned and unplanned GP visits by older multi-morbid patients in the Patient Journey Record System database

RATIONALE, AIMS AND OBJECTIVES

Timely access to general practitioner (GP) care is a recognized strategy to address avoidable hospitalization. Little is known about patients seeking planned (decided ahead) and unplanned (decided on day) GP visits. The Patient Journey Record System (PaJR) provides a biopsychosocial real-time monitoring and support service to chronically ill and older people over 65 who may be at risk of an avoidable hospital admission. This study aims to describe reported profiles associated with planned and unplanned GP visits during the week in the PaJR database of regular outbound phone calls made by Care Guides to multi-morbid older patients.

METHODS

One hundred fifty consecutive patients with one or more chronic condition (including chronic obstructive pulmonary disease, heart/vascular disease, heart failure and/or diabetes), one or more hospital admission in previous year, and consecutively recruited from hospital discharge, out-of-hour care and GP practices comprised the study sample. Using a semistructured script, Care Guides telephoned the patients approximately every 3 week days, and entered call data into the PaJR database in 2011. The PaJR project identified and prompted unplanned visits according to its algorithms. Logistic regression modelling and descriptive statistics identified significant predictors of planned and unplanned visits and patterns of GP visits on weekdays reported in calls.

RESULTS

In 5096 telephone calls, unplanned versus planned GP visits were predicted by change in health state, significant symptom concerns, poor self-rated health, bodily pain and concerns about caregiver or intimates. Calls not reporting visits had significantly fewer of these features. Planned visits were associated with general and medication concerns, reduced social participation and feeling down. Planned visits were highest on Monday and trended downwards to Fridays. Unplanned visits were reported at the same rate each weekday and more frequently when the interval between calls was ≥3 days. The PaJR project Care Guides advised patients to make unplanned visits in 6.3% of calls and advised planned GP visits in 2.5% of calls.

CONCLUSION

Unplanned GP visits consistently indicated a significant change to worse health with planned visits presenting less acuity in this study of older multi-morbid patients in general practice, when monitored by regular calls at about every 3 days. The PaJR study actively prompted GP visits according to its algorithms. Assessing and predicting acuity in older multi-morbid patients appears to be a promising strategy to improve access to primary care, and thus to reducing avoidable hospital utilization. Further research is needed to investigate the topic on a wider scale.

Standardization meets stories: contrasting perspectives on the needs of frail individuals at a rehabilitation unit

BACKGROUND

Repeated encounters over time enable general practitioners (GPs) to accumulate biomedical and biographical knowledge about their patients. A growing body of evidence documenting the medical relevance of lifetime experiences indicates that health personnel ought to appraise this type of knowledge and consider how to incorporate it into their treatment of patients. In order to explore the interdisciplinary communication of such knowledge within Norwegian health care, we conducted a research project at the interface between general practice and a nursing home.

METHODS

In the present study, nine Norwegian GPs were each interviewed about one of their patients who had recently been admitted to a nursing home for short-term rehabilitation. A successive interview conducted with each of these patients aimed at both validating the GP's information and exploring the patient's life story. The GP's treatment opinions and the patient's biographical information and treatment preferences were condensed into a biographical record presented to the nursing home staff. The transcripts of the interviews and the institutional treatment measures were compared and analysed, applying a phenomenological-hermeneutical framework. In the present article, we compare and discuss: (1) the GPs' specific recommendations for their patients; (2) the patients' own wishes and perceived needs; and (3) if and how this information was integrated into the institution's interventions and priorities.

RESULTS

Each GP made rehabilitation recommendations, which included statements regarding both the patient's personality and life circumstances. The nursing home staff individualized their selection of therapeutic interventions based on defined standardized treatment approaches, without personalizing them.

CONCLUSION

We found that the institutional voice of medicine consistently tends to override the voice of the patient's lifeworld. Thus, despite the institution's best intentions, their efforts to provide appropriate rehabilitation seem to have been jeopardized to some extent.

Caring for people with chronic disease: is 'muddling through' the best way to handle the multiple complexities?

It is stated everywhere that chronic care poses one of the biggest challenges for the future of medicine. Critical analysis however suggests that these statements are oversimplistic and based on limited, and at times, spurious assumptions. This paper highlights some basic realities: epidemiology shows that at any time, 80% of people experience 'good enough health', and that only 0.8% require tertiary medical care; most people with chronic conditions experience a stable illness trajectory; 'true' multi-morbidity is a pattern of advanced age; ageing and the physiological decline of our organ systems is a slow and steady process starting at the age of 30; and, as our health declines in a variety of patterns with disease and ageing, our psycho-socio-semiotic care needs increase dramatically. I argue that managing the complexities associated with chronic disease care successfully requires an equally complex management approach, 'muddling through', defined by Lindblom as making decisions based on successive limited comparisons. Our patients - rightly - expect that we make these decisions in their best interest. Individual health care professionals and health care policy makers firmly need to put the patient at the centre of the health care system.