It is complicated! - misunderstanding the complexities of 'complex'

How family physicians in Singapore recognise complexity during consultations: a qualitative study

BACKGROUND

The prevalence of persons with complex needs in Singapore is rising. Poor understanding of what constitutes complexity impedes the identification of care gaps and development of interventions to improve care for these individuals. We aim to identify the characteristics contributing to complexity in primary care, from the Family Physicians' (FP) perspectives.

METHODS

Focus group discussions (FGDs) were conducted from January to September 2021 with experienced FPs across 14 study sites, employing a qualitative descriptive approach based on a complexity framework. Data were coded independently and categorised using thematic analysis by two independent investigators.

RESULTS

Five FGDs were conducted with 18 FPs aged 32 to 57 years old working in different primary care settings, with a mean of 13.5 years of primary care experience. Participants emphasised the need for a unified definition of complexity. Complexity is characterised by the presence of issues spanning across two or more domains (medical, psychological, social or behavioural) that adversely impact medical care and outcomes. Persons with complex needs contrast with persons with medically difficult issues. Medical domain issues include the number of active medical problems, poor chronic disease control, treatment interactions, ill-defined symptoms, management of end-of-life conditions and functional impairment. Psychological domain issues include the presence of mental health conditions or cognitive impairment. Social domain issues include the lack of social support, competing social responsibilities and financial issues, while behavioural domain issues include a lack of trust in healthcare workers, fixed health beliefs and poor health literacy.

CONCLUSION

Recognising the medical, psychological, social and behavioural factors that contribute to complexity aids in discerning the diverse needs of individuals with complex needs. This underscores the need for additional support in these pertinent areas.

Stakeholder-driven development and implementation of CRICIT: an app to support high-quality data capture and protocol monitoring for outpatient clinical trials with vulnerable populations

Introduction

Choosing an appropriate electronic data capture system (EDC) is a critical decision for all randomized controlled trials (RCT). In this paper, we document our process for developing and implementing an EDC for a multisite RCT evaluating the efficacy and implementation of an enhanced primary care model for individuals with opioid use disorder who are returning to the community from incarceration.

Methods

Informed by the Knowledge-to-Action conceptual framework and user-centered design principles, we used Claris Filemaker software to design and implement CRICIT, a novel EDC that could meet the varied needs of the many stakeholders involved in our study.

Results

CRICIT was deployed in May 2021 and has been continuously iterated and adapted since. CRICIT's features include extensive participant tracking capabilities, site-specific adaptability, integrated randomization protocols, and the ability to generate both site-specific and study-wide summary reports.

Conclusions

CRICIT is highly customizable, adaptable, and secure. Its implementation has enhanced the quality of the study's data, increased fidelity to a complicated research protocol, and reduced research staff's administrative burden. CRICIT and similar systems have the potential to streamline research activities and contribute to the efficient collection and utilization of clinical research data.

Key characteristics of patient complexity and patient complexity conceptual models/measurement tools: a scoping review protocol

INTRODUCTION

The social determinants of health have been gaining recognition, confirming that multidimensional biopsychosocial assessment is essential to improving the health of individuals. This trend has led to the development of conceptual models and measurement tools assessing 'patient complexity', understood as a complex interplay of biopsychosocial factors, to improve the efficiency and effectiveness of care; however, the variety of meanings encompassed by the term has led to confusion in the interpretation of patient complexity such that there is no consensus regarding the definition or conceptualisation of patient complexity. The primary objective of this scoping review is to identify and map what is known about the key characteristics of patient complexity through multiple database searches.

METHODS AND ANALYSIS

This study will follow an established framework for conducting scoping reviews. The data will be extracted through searches of MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, The Cochrane Library and Google Scholar. Included articles will have: investigated participants aged 19 years or older, with any health condition; described patient complexity, a model for patient complexity, or a measurement tool for patient complexity; and been published in English from 1 January 1970 to April 2022. Article selection and data extraction will be conducted independently by two reviewers and if necessary for consensus, a third reviewer. A descriptive summary will be prepared to explain how the results apply to the scoping review questions. The findings will be a detailed mapping of the health dimensions that emerge from the classification of the extracted data. Subsequently, a definition of patient complexity will be developed.

ETHICS AND DISSEMINATION

This review does not require ethical approval, as we will use publicly available data. The study findings will be disseminated through a relevant conference presentation and a peer-reviewed journal. This protocol is registered on the Open Science Framework (www.osf.io/hpa3c).

Shared decision-making in the realm of uncertainty: The example of coronary artery disease through an EBM and complexity science lens

Patients look to their clinicians for explanations and treatments that achieve predictable cures with certainty. Clinicians usually respond accordingly. Acknowledging uncertainty, while necessary, is difficult, anxiety-provoking and at times overwhelming for patients and clinicians alike. We here present three case studies to illustrate the uncertainties of managing patients with potentially life-threatening illnesses. Research aims to provide answers to clinical problems. But, conducting research almost inevitably entails a reduction of real-world complexities. Research ultimately can only provide 'partial or in general answers' mostly revealing new questions. Due to the complexity of clinical care, research cannot really achieve certainty and predictability for an individual within his specific living context and values. In an unavoidably uncertain environment, instead of oversimplifying, clinicians like patients-as far as possible-ought to better embrace a complexity thinking frame. This provides a deeper understanding how living bodies function as-a-whole within their living contexts. Uncertainty and unpredictability, being inherent elements of complexity thinking, cannot be overcome. However, it may be made easier to cope with uncertainty by at least adopting the thinking in probabilities for benefits and harms of patient related outcomes as introduced in Sackett's Evidence-Based Medicine framework. Through the lenses of evidence-based medicine and complexity sciences this paper critically explores the clinical management of three patients diagnosed as having coronary artery disease. They all received the same treatment even though they presented with very different clinical complaints arising from different disease manifestations. Looking at these case studies the authors reflect on the reasons behind this astonishing, but widely seen medical behaviour of 'one size fits all'. They critically reflect the importance of research and evidence in view of a person-centred solution.

Association of a housing based individual socioeconomic status measure with diabetic control in primary care practices

AIMS

Socioeconomic status (SES) is an important variable that impacts healthcare outcomes. However, grouped SES data is not always representative of all members and it is difficult to obtain individual level data. A validated individual housing-based measure termed HOUSES is available, but has not been studied in diabetes. We hypothesize that patients in the lowest HOUSES quartile are associated with worse diabetic control as measured by the D5.

METHODS

A retrospective cohort study of 5463 patients with diabetes in 5 patient centered medical home practices in southeast Minnesota was conducted. HOUSES is a validated, standardized housing-based SES measure constructed from publicly available county assessor's office data. Diabetic control was assessed by the D5 (HgbA1c < 8, BP < 140/90, statin use, nonsmoking status, and antiplatelet therapy).

RESULTS

In the lowest HOUSES quartile, more patients had an uncontrolled D5 (56.4%) than any of the other quartiles (49.2%, 49.8%, 49.6% respectively, p < 0.001). A multivariate analysis shows the adjusted odds of D5 control for patients in the 2nd, 3rd or 4th HOUSES quartiles as opposed to the 1st quartile are 1.28, 1.21, and 1.20, respectively.

CONCLUSION

Lower SES as represented by the first quartile of HOUSES index, is associated with lower odds of D5 control and thus worse diabetic outcomes. Using the HOUSES index to identify these individuals in a patient centered medical home might prove useful in deciding where to focus diabetic control efforts.

A Fully Collaborative, Noteless Electronic Medical Record Designed to Minimize Information Chaos: Software Design and Feasibility Study

Background

Clinicians spend large amounts of their workday using electronic medical records (EMRs). Poorly designed documentation systems contribute to the proliferation of out-of-date information, increased time spent on medical records, clinician burnout, and medical errors. Beyond software interfaces, examining the underlying paradigms and organizational structures for clinical information may provide insights into ways to improve documentation systems. In particular, our attachment to the note as the major organizational unit for storing unstructured medical data may be a cause of many of the problems with modern clinical documentation. Notes, as currently understood, systematically incentivize information duplication and information scattering, both within a single clinician’s notes over time and across multiple clinicians’ notes. Therefore, it is worthwhile to explore alternative paradigms for unstructured data organization.

Objective

The aim of this study is to demonstrate the feasibility of building an EMR that does not use notes as the core organizational unit for unstructured data and which is designed specifically to disincentivize information duplication and information scattering.

Methods

We used specific design principles to minimize the incentive for users to duplicate and scatter information. By default, the majority of a patient’s medical history remains the same over time, so users should not have to redocument that information. Clinicians on different teams or services mostly share the same medical information, so all data should be collaboratively shared across teams and services (while still allowing for disagreement and nuance). In all cases where a clinician must state that information has remained the same, they should be able to attest to the information without redocumenting it. We designed and built a web-based EMR based on these design principles.

Results

We built a medical documentation system that does not use notes and instead treats the chart as a single, dynamically updating, and fully collaborative workspace. All information is organized by clinical topic or problem. Version history functionality is used to enable granular tracking of changes over time. Our system is highly customizable to individual workflows and enables each individual user to decide which data should be structured and which should be unstructured, enabling individuals to leverage the advantages of structured templating and clinical decision support as desired without requiring programming knowledge. The system is designed to facilitate real-time, fully collaborative documentation and communication among multiple clinicians.

Conclusions

We demonstrated the feasibility of building a non–note-based, fully collaborative EMR system. Our attachment to the note as the only possible atomic unit of unstructured medical data should be reevaluated, and alternative models should be considered.

Development and validation of a score to assess complexity of general internal medicine patients at hospital discharge: a prospective cohort study

OBJECTIVE

We aimed to develop and validate a score to assess inpatient complexity and compare its performance with two currently used but not validated tools to estimate complexity (ie, Charlson Comorbidity Index (CCI), patient clinical complexity level (PCCL)).

METHODS

Consecutive patients discharged from the department of medicine of a tertiary care hospital were prospectively included into a derivation cohort from 1 October 2016 to 16 February 2017 (n=1407), and a temporal validation cohort from 17 February 2017 to 31 March 2017 (n=482). The physician in charge assessed complexity. Potential predictors comprised 52 parameters from the electronic health record such as health factors and hospital care usage. We fit a logistic regression model with backward selection to develop a prediction model and derive a score. We assessed and compared performance of model and score in internal and external validation using measures of discrimination and calibration.

RESULTS

Overall, 447 of 1407 patients (32%) in the derivation cohort, and 116 of 482 patients (24%) in the validation cohort were identified as complex. Eleven variables independently associated with complexity were included in the score. Using a cut-off of ≥24 score points to define high-risk patients, specificity was 81% and sensitivity 57% in the validation cohort. The score's area under the receiver operating characteristic (AUROC) curve was 0.78 in both the derivation and validation cohort. In comparison, the CCI had an AUROC between 0.58 and 0.61, and the PCCL between 0.64 and 0.69, respectively.

CONCLUSIONS

We derived and internally and externally validated a score that reflects patient complexity in the hospital setting, performed better than other tools and could help monitoring complex patients.

[Complexity theory and the hypertensive patient]

Hypertension is the main cause of death worldwide and the approach that the Family Physician makes of hypertensive patients, given his or her key role as a gateway to the health system, is a crucial determinant in their evolution. On the other hand, Complexity theory contributes to the understanding on how systems grow, adapt and evolve. The hypertensive patient, given his character of biological and social being, can be understood and approached as a complex system. Understanding the characteristics of these systems contributes to considering the patient from another perspective, more satisfactory both for himself and for the professional who assists him. This review analyzes the characteristics of the complex system «hypertensive patient» and the tools that allow us to account for and interact with this complexity. An approach from multiple perspectives, migrating from the classic reductionist models to others that take into account the dynamic interrelationships that are at stake, would be a useful strategy for the Family Physician in the challenge of achieving adequate control of blood pressure in his or her patients.

Bringing complexity into clinical practice: An internistic approach

Modern medicine, still largely focused on single diseases, is unprepared for managing clinical complexity (CC), which is an emerging issue. Ageing of the general population has favoured the occurrence of chronic diseases, which generate multimorbidity that has been considered for many years the main feature of CC. However, more recent studies have shown that CC is something more and different and originates from the dynamic interaction among the patient's intrinsic factors (age, gender, multimorbidity, frailty) as well as contextual factors (socioeconomic, behavioural, cultural, and environmental). The result of these interactions is non-linear and unpredictable behaviour, which is difficult to manage both in clinical practice and in the organisation of care. Up to now, the prevalent approach has consisted of breaking down and separately analysing each CC component. Consequently, only incomplete strategies to improve health outcomes have been developed, such as limited patient-centred algorithms, deprescription of therapies, and local clinical governance interventions. Medical education has a pivotal role in transmitting the knowledge of complexity, making it realistically understandable and manageable. Future research should aim at implementing our knowledge of CC, developing new tools for its quantitation, and finding new solutions to improve important health outcomes at a sustainable cost.

Capturing complexity in clinician case-mix: classification system development using GP and physician associate data

Background

There are limited case-mix classification systems for primary care settings which are applicable when considering the optimal clinical skill mix to provide services.

Aim

To develop a case-mix classification system (CMCS) and test its impact on analyses of patient outcomes by clinician type, using example data from physician associates' (PAs) and GPs' consultations with same-day appointment patients.

Design & setting

Secondary analysis of controlled observational data from six general practices employing PAs and six matched practices not employing PAs in England.

Method

Routinely-collected patient consultation records (PA n = 932, GP n = 1154) were used to design the CMCS (combining problem codes, disease register data, and free text); to describe the case-mix; and to assess impact of statistical adjustment for the CMCS on comparison of outcomes of consultations with PAs and with GPs.

Results

A CMCS was developed by extending a system that only classified 18.6% (213/1147) of the presenting problems in this study's data. The CMCS differentiated the presenting patient's level of need or complexity as: acute, chronic, minor problem or symptom, prevention, or process of care, applied hierarchically. Combination of patient and consultation-level measures resulted in a higher classification of acuity and complexity for 639 (30.6%) of patient cases in this sample than if using consultation level alone. The CMCS was a key adjustment in modelling the study's main outcome measure, that is rate of repeat consultation.

Conclusion

This CMCS assisted in classifying the differences in case-mix between professions, thereby allowing fairer assessment of the potential for role substitution and task shifting in primary care, but it requires further validation.

Complexity in internal medicine wards: A novel screening method and implications for management

RATIONALE

Complexity is increasingly recognized as a critical variable in health care. However, there is still lack of practical tools to assess it and tackle the challenges that stem from it, particularly within hospitals.

AIMS AND OBJECTIVE

To validate a simple novel screening method based on both objective and subjective criteria to identify patients with clinically complex hospitalization events. To evaluate the prevalence of patients with complex events, identify their features, and compare them with those of the other patients and to those of patients with multimorbidities.

METHOD

We monitored the level of complexity of the hospitalization events of 240 patients admitted to an internal medicine ward in Tuscany over the course of 56 days. We compared the demographic features, the length of stay, and the prognosis of patients with and without complex events.

RESULTS

Sixty-nine patients (28.8% of the sample) had a complex episode during their stay, and 115 (47.9%) had phases of low complexity. Patients with complex episodes were younger and more comorbid than patients without. They stayed longer in-hospital (+4.5 days; 95% CI: 2.5-6.5) and had higher mortality (OR: 24.93; 95% CI: 6.97-171.63) and a lower probability of home discharge (OR: 0.25; 95% CI: 0.13-0.48).

CONCLUSIONS

The results show that using a simple screening method is possible to identify complex patients within IM wards and that every day, about one-third of the patients are complex. The results are discussed in implications for the dynamic management of patients with complex and simple phases during hospitalization.