A qualitative study on clinicians' perceptions about the implementation of a population risk stratification tool in primary care practice of the Basque health service

A Predefined Rule-Based Multi-Factor Risk Stratification Is Associated With Improved Outcomes at a Rural Primary Care Practice

This study built a predefined rule-based risk stratification paradigm using 19 factors in a primary care setting that works with rural communities. The factors include medical and nonmedical variables. The nonmedical variables represent 3 demographic attributes and one other factor represents transportation availability. Medical variables represent major clinical variables such as blood pressure and BMI. Many risk stratification models are found in the literature but few integrate medical and nonmedical variables, and to our knowledge, no such model is designed specifically for rural communities. The data used in this study contain the associated variables of all medical visits in 2021. Data from 2022 were used to evaluate the model. After our risk stratification model and several interventions were adopted in 2022, the percentage of patients with high or medium risk of deteriorating health outcomes dropped from 34.9% to 24.4%, which is a reduction of 30%. The medium-complex patient population size, which had been 29% of all patients, decreased by about 4% to 5.7%. According to the analysis, the total risk score showed a strong correlation with 3 risk factors: dual diagnoses, the number of seen providers, and PHQ9 (0.63, 0.54, and 0.45 correlation coefficients, respectively).

The use of the PARIHS framework in implementation research and practice-a citation analysis of the literature

BACKGROUND

The Promoting Action on Research Implementation in Health Services (PARIHS) framework was developed two decades ago and conceptualizes successful implementation (SI) as a function (f) of the evidence (E) nature and type, context (C) quality, and the facilitation (F), [SI = f (E,C,F)]. Despite a growing number of citations of theoretical frameworks including PARIHS, details of how theoretical frameworks are used remains largely unknown. This review aimed to enhance the understanding of the breadth and depth of the use of the PARIHS framework.

METHODS

This citation analysis commenced from four core articles representing the key stages of the framework's development. The citation search was performed in Web of Science and Scopus. After exclusion, we undertook an initial assessment aimed to identify articles using PARIHS and not only referencing any of the core articles. To assess this, all articles were read in full. Further data extraction included capturing information about where (country/countries and setting/s) PARIHS had been used, as well as categorizing how the framework was applied. Also, strengths and weaknesses, as well as efforts to validate the framework, were explored in detail.

RESULTS

The citation search yielded 1613 articles. After applying exclusion criteria, 1475 articles were read in full, and the initial assessment yielded a total of 367 articles reported to have used the PARIHS framework. These articles were included for data extraction. The framework had been used in a variety of settings and in both high-, middle-, and low-income countries. With regard to types of use, 32% used PARIHS in planning and delivering an intervention, 50% in data analysis, 55% in the evaluation of study findings, and/or 37% in any other way. Further analysis showed that its actual application was frequently partial and generally not well elaborated.

CONCLUSIONS

In line with previous citation analysis of the use of theoretical frameworks in implementation science, we also found a rather superficial description of the use of PARIHS. Thus, we propose the development and adoption of reporting guidelines on how framework(s) are used in implementation studies, with the expectation that this will enhance the maturity of implementation science.

Population segmentation based on healthcare needs: a systematic review

BACKGROUND

Healthcare needs-based population segmentation is a promising approach for enabling the development and evaluation of integrated healthcare service models that meet healthcare needs. However, healthcare policymakers interested in understanding adult population healthcare needs may not be aware of suitable population segmentation tools available for use in the literature and barring better-known alternatives, may reinvent the wheel by creating and validating their own tools rather than adapting available tools in the literature. Therefore, we undertook a systematic review to identify all available tools which operationalize healthcare need-based population segmentation, to help inform policymakers developing population-level health service programmes.

METHODS

Using search terms reflecting concepts of population, healthcare need and segmentation, we systematically reviewed and included articles containing healthcare need-based adult population segmentation tools in PubMed, CINAHL and Web of Science databases. We included tools comprising mutually exclusive segments with prognostic value for clinically relevant outcomes. An updated secondary search on the PubMed database was also conducted as the last search was conducted 2 years ago. All identified tools were characterized in terms of segment formulation, segmentation base, whether they received peer-reviewed validation, requirement for comprehensive electronic medical records, proprietary status and number of segments.

RESULTS

A total of 16 unique tools were identified from systematically reviewing 9970 articles. Peer-reviewed validation studies were found for 9 of these tools.

DISCUSSION AND CONCLUSIONS

The underlying segmentation basis of most identified tools was found to be conceptually comparable to each other which suggests a broad recognition of archetypical patient overall healthcare need profiles. While many tools operate based on administrative record data, it is noted that healthcare systems without comprehensive electronic medical records would benefit from tools which segment populations through primary data collection. Future work could therefore include development and validation of such primary data collection-based tools. While this study is limited by exclusion of non-English literature, the identified and characterized tools will nonetheless facilitate efforts by policymakers to improve patient-centred care through development and evaluation of services tailored for specific populations segmented by these tools.

Impact of stratification on the effectiveness of a comprehensive patient-centered strategy for multimorbid patients

OBJECTIVE

The objective of this work was to assess the effectiveness of a population-level patient-centered intervention for multimorbid patients based on risk stratification for case finding in 2014 compared with the baseline scenario in 2012.

DATA SOURCE

Clinical and administrative databases.

STUDY DESIGN

This was an observational cohort study with an intervention group and a historical control group. A propensity score by a genetic matching approach was used to minimize bias. Generalized linear models were used to analyze relationships among variables.

DATA COLLECTION

We included all eligible patients at the beginning of the year and followed them until death or until the follow-up period concluded (end of the year). The control group (2012) totaled 3558 patients, and 4225 patients were in the intervention group (2014).

PRINCIPAL FINDING

A patient-centered strategy based on risk stratification for case finding and the implementation of an integrated program based on new professional roles and an extensive infrastructure of information and communication technologies avoided 9 percent (OR: 0.91, CI: 0.86-0.96) of hospitalizations. However, this effect was not found in nonprioritized groups whose probability of hospitalization increased (OR: 1.19, CI = 1.09-1.30).

CONCLUSIONS

In a before-and-after analysis using propensity score matching, a comprehensive, patient-centered, integrated care intervention was associated with a lower risk of hospital admission among prioritized patients, but not among patients who were not prioritized to receive the intervention.

FINGER (Forming and Identifying New Groups of Expected Risks): developing and validating a new predictive model to identify patients with high healthcare cost and at risk of admission

OBJECTIVE

Predictive statistical models used in population stratification programmes are complex and usually difficult to interpret for primary care professionals. We designed FINGER (Forming and Identifying New Groups of Expected Risks), a new model based on clinical criteria, easy to understand and implement by physicians. Our aim was to assess the ability of FINGER to predict costs and correctly identify patients with high resource use in the following year.

DESIGN

Cross-sectional study with a 2-year follow-up.

SETTING

The Basque National Health System.

PARTICIPANTS

All the residents in the Basque Country (Spain) ≥14 years of age covered by the public healthcare service (n=1 946 884).

METHODS

We developed an algorithm classifying diagnoses of long-term health problems into 27 chronic disease groups. The database was randomly divided into two data sets. With the calibration sample, we calculated a score for each chronic disease group and other variables (age, sex, inpatient admissions, emergency department visits and chronic dialysis). Each individual obtained a FINGER score for the year by summing their characteristics' scores. With the validation sample, we constructed regression models with the FINGER score for the first 12 months as the only explanatory variable.

RESULTS

The annual FINGER scores obtained by patients ranged from 0 to 57 points, with a mean of 2.06. The coefficient of determination for healthcare costs was 0.188 and the area under the receiver operating characteristic curve was 0.838 for identifying patients with high costs (>95th percentile); 0.875 for extremely high costs (>99th percentile); 0.802 for unscheduled admissions; 0.861 for prolonged hospitalisation (>15 days); and 0.896 for death.

CONCLUSION

FINGER presents a predictive power for high risks fairly close to other classification systems. Its simple and transparent architecture allows for immediate calculation by clinicians. Being easy to interpret, it might be considered for implementation in regions involved in population stratification programmes.

Are we ready for the challenge of implementing risk-based breast cancer screening and primary prevention?

BACKGROUND

Increased knowledge of breast cancer risk factors provides opportunities to shift from a one-size-fits-all screening programme to a personalised approach, where screening and prevention is based on a woman's risk of developing breast cancer. However, potential implementation of this new paradigm could present considerable challenges which the present review aims to explore.

METHODS

Bibliographic databases were searched to identify studies evaluating potential implications of the implementation of personalised risk-based screening and primary prevention for breast cancer. Identified themes were evaluated using thematic analysis.

RESULTS

The search strategy identified 5699 unique publications, of which 59 were selected for inclusion. Significant changes in policy and practice are warranted. The organisation of breast cancer screening spans several healthcare delivery systems and clinical settings. Feasibility of implementation depends on how healthcare is funded and arranged, and potentially varies between countries. Piloting risk assessment and prevention counselling in primary care settings has highlighted implications relating to the need for extensive additional training on risk (communication) and prevention, impact on workflow, and professionals' personal discomfort breaching the topic with women. Additionally, gaps in risk estimation, psychological, ethical and legal consequences will need to be addressed.

CONCLUSION

The present review identified considerable unresolved issues and challenges. Potential implementation will require a more complex framework, in which a country's healthcare regulations, resources, and preferences related to screening and prevention services are taken into account. However, with the insights gained from the present overview, countries expecting to implement risk-based screening and prevention can start to inventory and address the issues that were identified.

Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Background

With a higher proportion of older people in the UK population, new approaches are needed to reduce emergency hospital admissions, thereby shifting care delivery out of hospital when possible and safe.

Study aim

To evaluate the introduction of predictive risk stratification in primary care.

Objectives

To (1) measure the effects on service usage, particularly emergency admissions to hospital; (2) assess the effects of the Predictive RIsk Stratification Model (PRISM) on quality of life and satisfaction; (3) assess the technical performance of PRISM; (4) estimate the costs of PRISM implementation and its effects; and (5) describe the processes of change associated with PRISM.

Design

Randomised stepped-wedge trial with economic and qualitative components.

Setting

Abertawe Bro Morgannwg University Health Board, south Wales.

Participants

Patients registered with 32 participating general practices.

Intervention

PRISM software, which stratifies patients into four (emergency admission) risk groups; practice-based training; and clinical support.

Main outcome measures

Primary outcome – emergency hospital admissions. Secondary outcomes – emergency department (ED) and outpatient attendances, general practitioner (GP) activity, time in hospital, quality of life, satisfaction and costs.

Data sources

Routine anonymised linked health service use data, self-completed questionnaires and staff focus groups and interviews.

Results

Across 230,099 participants, PRISM implementation led to increased emergency admissions to hospital [ΔL = 0.011, 95% confidence interval (CI) 0.010 to 0.013], ED attendances (ΔL = 0.030, 95% CI 0.028 to 0.032), GP event-days (ΔL = 0.011, 95% CI 0.007 to 0.014), outpatient visits (ΔL = 0.055, 95% CI 0.051 to 0.058) and time spent in hospital (ΔL = 0.029, 95% CI 0.026 to 0.031). Quality-of-life scores related to mental health were similar between phases (Δ = –0.720, 95% CI –1.469 to 0.030); physical health scores improved in the intervention phase (Δ = 1.465, 95% CI 0.774 to 2.157); and satisfaction levels were lower (Δ = –0.074, 95% CI – 0.133 to –0.015). PRISM implementation cost £0.12 per patient per year and costs of health-care use per patient were higher in the intervention phase (Δ = £76, 95% CI £46 to £106). There was no evidence of any significant difference in deaths between phases (9.58 per 1000 patients per year in the control phase and 9.25 per 1000 patients per year in the intervention phase). PRISM showed good general technical performance, comparable with existing risk prediction tools (c-statistic of 0.749). Qualitative data showed low use by GPs and practice staff, although they all reported using PRISM to generate lists of patients to target for prioritised care to meet Quality and Outcomes Framework (QOF) targets.

Limitations

In Wales during the study period, QOF targets were introduced into general practice to encourage targeting care to those at highest risk of emergency admission to hospital. Within this dynamic context, we therefore evaluated the combined effects of PRISM and this contemporaneous policy initiative.

Conclusions

Introduction of PRISM increased emergency episodes, hospitalisation and costs across, and within, risk levels without clear evidence of benefits to patients.

Future research

(1) Evaluation of targeting of different services to different levels of risk; (2) investigation of effects on vulnerable populations and health inequalities; (3) secondary analysis of the Predictive Risk Stratification: A Trial in Chronic Conditions Management data set by health condition type; and (4) acceptability of predictive risk stratification to patients and practitioners.

Trial and study registration

Current Controlled Trials ISRCTN55538212 and PROSPERO CRD42015016874.

Funding

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

Key aspects related to implementation of risk stratification in health care systems-the ASSEHS study

BACKGROUND

The lack of proven efficacy of new healthcare interventions represents a problem for health systems globally. It is partly related to suboptimal implementation processes, leading to poor adoption of new interventions. Activation of Stratification Strategies and Results of the interventions on frail patients of Healthcare Services (ASSEHS) EU project (N° 2013 12 04) aims to study current existing health Risk Stratification (RS) strategies and tools on frail elderly patients. This paper aims at identifying variables that make the implementation of population RS tools feasible in different healthcare services.

METHODS

Two different methods have been used to identify the key elements in stratification implementation; i) a Scoping Review, in order to search and gather scientific evidence and ii) Semi-structured interviews with six key experts that had been actively involved in the design and/or implementation of RS strategies. It aims to focus the implementation construct on real-life contextual understandings, multi-level perspectives, and cultural influences.

RESULTS

A Feasibility Framework has been drawn. Two dimensions impact the feasibility of RS: (i) Planning, deployment and change management and (ii) Care intervention. The former comprises communication, training and mutual learning, multidisciplinarity of the team, clinicians' engagement, operational plan and ICT display and functionalities. The latter includes case finding and selection of the target population, pathway definition and quality improvement process.

CONCLUSIONS

The Feasibility Framework provides a list of key elements that should be considered for an effective implementation of population risk stratification interventions. It helps to identify, plan and consider relevant elements to ensure a proper RS implementation.

Implementing system-wide risk stratification approaches: A review of critical success and failure factors

Risk stratification has become a widely used tool for linking people identified at risk of health deterioration to the most appropriate evidence-based care. This article systematically reviews recent literature to determine key factors that have been identified as critical enablers and/or barriers to successful implementation of risk stratification tools at a system level. A systematic search found 23 articles and four promising protocols for inclusion in the review, covering the use to 20 different risk stratification tools. These articles reported on only a small fraction of the risk stratification tools used in health systems; suggesting that while the development and statistical validation of risk stratification algorithms is widely reported, there has been little published evaluation of how they are implemented in real-world settings. Controlled studies provided some evidence that the use of risk stratification tools in combination with a care management plan offer patient benefits and that the use of a risk stratification tool to determine components of a care management plan may contribute to reductions in hospital readmissions, patient satisfaction and improved patient outcomes. Studies with the strongest focus on implementation used qualitative and case study methods. Among these, the literature converged on four key areas of implementation that were found to be critical for overcoming barriers to success: the engagement of clinicians and safeguarding equity, both of which address barriers of acceptance; the health system context to address administrative, political and system design barriers; and data management and integration to address logistical barriers.

It could be a 'Golden Goose': a qualitative study of views in primary care on an emergency admission risk prediction tool prior to implementation

BACKGROUND

Rising demand for health care has prompted interest in new technologies to support a shift of care from hospital to community and primary care, which may require clinicians to undertake new working practices. A predictive risk stratification tool (Prism) was developed for use in primary care to estimate patients' risk of an emergency hospital admission. As part of an evaluation of Prism, we aimed to understand what might be needed to bring Prism into effective use by exploring clinicians and practice managers' attitudes and expectations about using it. We were informed by Normalisation Process Theory (NPT) which examines the work needed to bring an innovation into use.

METHODS

We conducted 4 focus groups and 10 interviews with a total of 43 primary care doctors and colleagues from 32 general practices. All were recorded and transcribed. Analysis focussed in particular on the construct of 'coherence' within NPT, which examines how people understand an innovation and its purpose.

RESULTS

Respondents were in agreement that Prism was a technological formalisation of existing practice, and that it would function as a support to clinical judgment, rather than replacing it. There was broad consensus about the role it might have in delivering new models of care based on active management, but there were doubts about the scope for making a difference to some patients and about whether Prism could identify at-risk patients not already known to the clinical team. Respondents did not expect using the tool to be onerous, but were concerned about the work which might follow in delivering care. Any potential value would not be of the tool in isolation, but would depend on the availability of support services.

CONCLUSIONS

Policy imperatives and the pressure of rising demand meant respondents were open to trying out Prism, despite underlying uncertainty about what difference it could make.

TRIAL REGISTRATION

Controlled Clinical Trials no. ISRCTN55538212 .