BackgroundAlthough local initiatives commonly express a wish to improve population health and wellbeing using a population health management (PHM) approach, implementation is challenging and... Show moreBackgroundAlthough local initiatives commonly express a wish to improve population health and wellbeing using a population health management (PHM) approach, implementation is challenging and existing tools have either a narrow focus or lack transparency. This has created demand for practice-oriented guidance concerning the introduction and requirements of PHM.MethodsExisting knowledge from scientific literature was combined with expert opinion obtained using an adjusted RAND UCLA appropriateness method, which consisted of six Dutch panels in three Delphi rounds, followed by two rounds of validation by an international panel.ResultsThe Dutch panels identified 36 items relevant to PHM, in addition to the 97 items across six elements of PHM derived from scientific literature. Of these 133 items, 101 were considered important and 32 ambiguous. The international panel awarded similar scores for 128 of 133 items, with only 5 items remaining unvalidated. Combining literature and expert opinion gave extra weight and validity to the items.DiscussionIn developing a maturity index to help assess the use and progress of PHM in health regions, input from experts counterbalanced a previous skewedness of item distribution across the PHM elements and the Rainbow Model of Integrated Care (RMIC). Participant expertise also improved our understanding of successful PHM implementation, as well as how the six PHM elements are best constituted in a first iteration of a maturity index. Limitations included the number of participants in some panels and ambiguity of language. Further development should focus on item clarity, adoption in practice and item interconnectedness.ConclusionBy employing scientific literature enriched with expert opinion, this study provides new insight for both science and practice concerning the composition of PHM elements that influence PHM implementation. This will help guide practices in their quest to implement PHM. Show less
Improving population health and reducing inequalities through better integrated health and social care services is high up on the agenda of policymakers internationally. In recent years, regional... Show moreImproving population health and reducing inequalities through better integrated health and social care services is high up on the agenda of policymakers internationally. In recent years, regional cross-domain partnerships have emerged in several countries, which aim to achieve better population health, quality of care and a reduction in the per capita costs. These cross-domain partnerships aim to have a strong data foundation and are committed to continuous learning in which data plays an essential role. This paper describes our approach towards the development of the regional integrative population-based data infrastructure Extramural LUMC (Leiden University Medical Center) Academic Network (ELAN), in which we linked routinely collected medical, social and public health data at the patient level from the greater The Hague and Leiden area. Furthermore, we discuss the methodological issues of routine care data and the lessons learned about privacy, legislation and reciprocities. The initiative presented in this paper is relevant for international researchers and policy-makers because a unique data infrastructure has been set up that contains data across different domains, providing insights into societal issues and scientific questions that are important for data driven population health management approaches. Show less
Improving population health and reducing inequalities through better integrated health and social care services is high up on the agenda of policymakers internationally. In recent years, regional... Show moreImproving population health and reducing inequalities through better integrated health and social care services is high up on the agenda of policymakers internationally. In recent years, regional cross-domain partnerships have emerged in several countries, which aim to achieve better population health, quality of care and a reduction in the per capita costs. These cross-domain partnerships aim to have a strong data foundation and are committed to continuous learning in which data plays an essential role. This paper describes our approach towards the development of the regional integrative population-based data infrastructure Extramural LUMC (Leiden University Medical Center) Academic Network (ELAN), in which we linked routinely collected medical, social and public health data at the patient level from the greater The Hague and Leiden area. Furthermore, we discuss the methodological issues of routine care data and the lessons learned about privacy, legislation and reciprocities. The initiative presented in this paper is relevant for international researchers and policy-makers because a unique data infrastructure has been set up that contains data across different domains, providing insights into societal issues and scientific questions that are important for data driven population health management approaches. Show less
Objective:As in many other countries, the Netherlands is facing challenges in the provision of healthcare to its population. To ensure the population remains in good health in coming decades, an... Show moreObjective:As in many other countries, the Netherlands is facing challenges in the provision of healthcare to its population. To ensure the population remains in good health in coming decades, an integrative approach to the many factors that influence health and health outcomes is needed. Population health management is gaining interest as a strategic framework for systems change in healthcare organisations. Based on population health management, the Dutch HealthKIC has developed the 'Plot model', which takes a regional perspective. The aim of this study was to detail the extent to which six prospective regions in the Netherlands were ready and willing to implement population health management using the Plot model, guided by the Five Lenses Model. Methods:Using an exploratory focus group reporting study, we involved stakeholders from six regions in the Netherlands. Thematic analysis followed the five predesigned dimensions of a validated cooperation model. Results:The study uncovered the potential for realisation of model aims, as assessed by an expert team, regarding shared ambition, mutual gains, relationship dynamics, organisational dynamics and process management. The exploratory questionnaire suggested that organisational dynamics is the least integrated topic in all areas, followed by process management, a finding confirmed in focus groups. Conclusion:The building themes of the Five Lenses Model all represent preconditions for the success of integration in the prospective regions. The present study showed that while some themes were reasonably represented in prospective regions, no region was satisfactory for all themes. Show less
Objective: The accessibility of acute care services is currently under pressure, and one way to improve services is better integration. Adequate methodology will be required to provide for a clear... Show moreObjective: The accessibility of acute care services is currently under pressure, and one way to improve services is better integration. Adequate methodology will be required to provide for a clear and accessible evaluation of the various intervention initiatives. The aim of this paper is to develop and propose a Population Health Management(PHM) methodology framework for evaluation of transitions in acute care services. Results: Our methodological framework is developed from several concepts found in literature, including Triple Aim, integrated care and PHM, and includes continuous monitoring of results at both project and population levels. It is based on a broad view of health rather than focusing on a specific illness and facilitates the evaluation of various intervention initiatives in acute care services in the Netherlands and distinctly explains every step of the evaluation process and can be applied to a heterogeneous group of patients. Show less
Girwar, S.A.M.; Verloop, J.C.; Fiocco, M.; Sutch, S.P.; Numans, M.E.; Bruijnzeels, M.A. 2022
OBJECTIVES: To produce an efficient and practically implementable method, based on primary care data exclusively, to identify patients with complex care needs who have problems in several health... Show moreOBJECTIVES: To produce an efficient and practically implementable method, based on primary care data exclusively, to identify patients with complex care needs who have problems in several health domains and are experiencing a mismatch of care. The Johns Hopkins ACG System was explored as a tool for identification, using its Aggregated Diagnosis Group (ADG) categories. STUDY DESIGN: Retrospective cross-sectional study using general practitioners' electronic health records combined with hospital data. METHODS: A prediction model for patients with complex care needs was developed using a primary care population of 105,345 individuals. Dependent variables in the model included age, sex, and the 32 ADGs. The prediction model was externally validated on 30,793 primary care patients. Discrimination and calibrations were assessed by computing C statistics and by visual inspection of the calibration plot, respectively. RESULTS: Our model was able to discriminate very well between complex and noncomplex patients (C statistic = 0.9; 95% CI, 0.88-0.92), whereas the calibration plot suggests that the model provides overestimates of complex patients. CONCLUSIONS: With this study, the ACG System has proven to be a useful tool in the identification of patients with complex care needs in primary care, opening up possibilities for tailored interventions of care management for this complex group of patients. Utilizing ADGs, the prediction model that we developed had a very good discriminatory ability to identify those complex patients. However, the of the model still needs improvement. Show less
Girwar, S.A.M.; Verloop, J.C.; Fiocco, M.; Sutch, S.P.; Numans, M.E.; Bruijnzeels, M.A. 2022
OBJECTIVES: To produce an efficient and practically implementable method, based on primary care data exclusively, to identify patients with complex care needs who have problems in several health... Show moreOBJECTIVES: To produce an efficient and practically implementable method, based on primary care data exclusively, to identify patients with complex care needs who have problems in several health domains and are experiencing a mismatch of care. The Johns Hopkins ACG System was explored as a tool for identification, using its Aggregated Diagnosis Group (ADG) categories. STUDY DESIGN: Retrospective cross-sectional study using general practitioners' electronic health records combined with hospital data. METHODS: A prediction model for patients with complex care needs was developed using a primary care population of 105,345 individuals. Dependent variables in the model included age, sex, and the 32 ADGs. The prediction model was externally validated on 30,793 primary care patients. Discrimination and calibrations were assessed by computing C statistics and by visual inspection of the calibration plot, respectively. RESULTS: Our model was able to discriminate very well between complex and noncomplex patients (C statistic = 0.9; 95% CI, 0.88-0.92), whereas the calibration plot suggests that the model provides overestimates of complex patients. CONCLUSIONS: With this study, the ACG System has proven to be a useful tool in the identification of patients with complex care needs in primary care, opening up possibilities for tailored interventions of care management for this complex group of patients. Utilizing ADGs, the prediction model that we developed had a very good discriminatory ability to identify those complex patients. However, the of the model still needs improvement. Show less
Girwar, S.A.M.; Verloop, J.C.; Fiocco, M.; Sutch, S.P.; Numans, M.E.; Bruijnzeels, M.A. 2022
OBJECTIVES: To produce an efficient and practically implementable method, based on primary care data exclusively, to identify patients with complex care needs who have problems in several health... Show moreOBJECTIVES: To produce an efficient and practically implementable method, based on primary care data exclusively, to identify patients with complex care needs who have problems in several health domains and are experiencing a mismatch of care. The Johns Hopkins ACG System was explored as a tool for identification, using its Aggregated Diagnosis Group (ADG) categories. STUDY DESIGN: Retrospective cross-sectional study using general practitioners' electronic health records combined with hospital data. METHODS: A prediction model for patients with complex care needs was developed using a primary care population of 105,345 individuals. Dependent variables in the model included age, sex, and the 32 ADGs. The prediction model was externally validated on 30,793 primary care patients. Discrimination and calibrations were assessed by computing C statistics and by visual inspection of the calibration plot, respectively. RESULTS: Our model was able to discriminate very well between complex and noncomplex patients (C statistic = 0.9; 95% CI, 0.88-0.92), whereas the calibration plot suggests that the model provides overestimates of complex patients. CONCLUSIONS: With this study, the ACG System has proven to be a useful tool in the identification of patients with complex care needs in primary care, opening up possibilities for tailored interventions of care management for this complex group of patients. Utilizing ADGs, the prediction model that we developed had a very good discriminatory ability to identify those complex patients. However, the of the model still needs improvement. Show less
Background: Acute care services are currently overstretched in many high income countries. Overcrowding also plays a major role in acute care in the Netherlands. In a region of the Netherlands, the... Show moreBackground: Acute care services are currently overstretched in many high income countries. Overcrowding also plays a major role in acute care in the Netherlands. In a region of the Netherlands, the general practice cooperative (GPC) and ambulance service have begun to integrate their care, and the rapid and complete transfer of information between these two care organisations is now the basis for delivering appropriate care. The primary aim of this mixed-methods study is to evaluate the Netherlands Triage System (NTS) merger project and answering the question: What is the added value of implementing a digital NTS merger in terms of healthcare use and healthcare costs? A secondary question is: What are the experiences of patients and care professionals in different acute healthcare organisations following implementation of the digital NTS merger?Methods: Patients who made an acute care request during the 12 months before the NTS merge intervention (control period) were compared with matched patients in the 12 months following the start of the NTS merge. Outcomes included difference in healthcare use 30 days after an acute event and patient' and care professional' experiences during the intervention period. To assess healthcare costs, we used reference prices updated to 2021.Results: Compared to patients in the control period, patients in the intervention period were hospitalized less often (52.9% vs 64.4%, p = 0.061) and had fewer emergency department (ED) visits (58.7% vs 69.3%, p = 0.074) in the 30 days following the acute care request. The ED costs were significantly lower during the intervention period compared to the control period (p = 0.042). Furthermore, patients in the intervention period were very satisfied overall with the acute care network (4.63 of 5) and care professionals were fairly satisfied with the cooperation to date (2.73 of 4).Conclusion: The Triple Aim for acute care can be met using relatively simple interventions, but medical data merging is a prerequisite for achieving more robust results covering on the various aspects of the Triple Aim. These successes should be communicated so that a common language can be developed that will support the successful further implementation of larger scale initiatives. Show less
Girwar, S.A.M.; Jabroer, R.; Fiocco, M.; Sutch, S.P.; Numans, M.E.; Bruijnzeels, M.A. 2021
Background and AimsIn our current healthcare situation, burden on healthcare services is increasing, with higher costs and increased utilization. Structured population health management has been... Show moreBackground and AimsIn our current healthcare situation, burden on healthcare services is increasing, with higher costs and increased utilization. Structured population health management has been developed as an approach to balance quality with increasing costs. This approach identifies sub-populations with comparable health risks, to tailor interventions for those that will benefit the most. Worldwide, the use of routine healthcare data extracted from electronic health registries for risk stratification approaches is increasing. Different risk stratification tools are used on different levels of the healthcare continuum. In this systematic literature review, we aimed to explore which tools are used in primary healthcare settings and assess their performance.MethodsWe performed a systematic literature review of studies applying risk stratification tools with health outcomes in primary care populations. Studies in Organisation for Economic Co-operation and Development countries published in English-language journals were included. Search engines were utilized with keywords, for example, “primary care,” “risk stratification,” and “model.” Risk stratification tools were compared based on different measures: area under the curve (AUC) and C-statistics for dichotomous outcomes and R2 for continuous outcomes.ResultsThe search provided 4718 articles. Specific election criteria such as primary care populations, generic health utilization outcomes, and routinely collected data sources identified 61 articles, reporting on 31 different models. The three most frequently applied models were the Adjusted Clinical Groups (ACG, n = 23), the Charlson Comorbidity Index (CCI, n = 19), and the Hierarchical Condition Categories (HCC, n = 7). Most AUC and C-statistic values were above 0.7, with ACG showing slightly improved scores compared with the CCI and HCC (typically between 0.6 and 0.7).ConclusionBased on statistical performance, the validity of the ACG was the highest, followed by the CCI and the HCC. The ACG also appeared to be the most flexible, with the use of different international coding systems and measuring a wider variety of health outcomes. Show less
Objectives Overcrowding in acute care services gives rise to major problems, such as reduced accessibility and delay in treatment. In order to be able to continue providing high-quality health care... Show moreObjectives Overcrowding in acute care services gives rise to major problems, such as reduced accessibility and delay in treatment. In order to be able to continue providing high-quality health care, it is important that organizations are well integrated at all organizational levels. The objective of this study was to to gain an understanding in which extent cooperation within an urban acute care network in the Netherlands (The Hague) improved because of the COVID-19 crisis. Methods Exploratory mixed-methods questionnaire and qualitative interview study. Semistructured interviews with stakeholders in the acute care network at micro (n = 10), meso (n = 9), and macro (n = 3) levels of organization. Thematic analysis took place along the lines of the 6 dimensions of the Rainbow Model of Integrated Care. Results In this study we identified themes that may act as barriers or facilitators to cooperation: communication, interaction, trust, leadership, interests, distribution of care, and funding. During the crisis many facilitators were identified at clinical, professional, and system level such as clear agreements about work processes, trust in each other's work, and different stakeholders growing closer together. However, at an organizational and communicative level there were many barriers such as interference in each other's work and a lack of clear policies. Conclusion The driving force behind all changes in integration of acute care organizations in an urban context during the COVID-19 crisis seemed to be a great sense of urgency to cooperate in the shared interest of providing the best patient care. We recommend shifting the postcrisis focus from overcoming the crisis to overcoming cooperative challenges. Show less
Girwar, S.A.M.; Fiocco, M.; Sutch, S.P.; Numans, M.E.; Bruijnzeels, M.A. 2021
BackgroundWithin the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered... Show moreBackgroundWithin the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered with one general practice, this offers a unique possibility to perform Population Health Management analyses based on general practitioners' (GP) registries. The Johns Hopkins Adjusted Clinical Groups (ACG) System is an internationally used method for predictive population analyses. The model categorizes individuals based on their complete health profile, taking into account age, gender, diagnoses and medication. However, the ACG system was developed with non-Dutch data. Consequently, for wider implementation in Dutch general practice, the system needs to be validated in the Dutch healthcare setting. In this paper we show the results of the first use of the ACG system on Dutch GP data. The aim of this study is to explore how well the ACG system can distinguish between different levels of GP healthcare utilization.MethodsTo reach our aim, two variables of the ACG System, the Aggregated Diagnosis Groups (ADG) and the mutually exclusive ACG categories were explored. The population for this pilot analysis consisted of 23,618 persons listed with five participating general practices within one region in the Netherlands. ACG analyses were performed based on historical Electronic Health Records data from 2014 consisting of primary care diagnoses and pharmaceutical data. Logistic regression models were estimated and AUC's were calculated to explore the diagnostic value of the models including ACGs and ADGs separately with GP healthcare utilization as the dependent variable. The dependent variable was categorized using four different cut-off points: zero, one, two and three visits per year.ResultsThe ACG and ADG models performed as well as models using International Classification of Primary Care chapters, regarding the association with GP utilization. AUC values were between 0.79 and 0.85. These models performed better than the base model (age and gender only) which showed AUC values between 0.64 and 0.71.ConclusionThe results of this study show that the ACG system is a useful tool to stratify Dutch primary care populations with GP healthcare utilization as the outcome variable. Show less
OBJECTIVE:To provide insight into the motives for hospital self-referral during office hours and the barriers deterring general practitioner (GP) consultation with a primary care request.SETTING... Show moreOBJECTIVE:To provide insight into the motives for hospital self-referral during office hours and the barriers deterring general practitioner (GP) consultation with a primary care request.SETTING:People who self-referred at a Daytime General Practice Cooperative (GPC) in two hospitals in The Hague, The Netherlands.PARTICIPANTS:A total of 44 people who self-referred were interviewed in two hospitals. The average age of interviewees was 35 years (range 19 months to 83 years), a parent of a young patient was interviewed, but the age of patients is shown here. There were more male patients (66%) than female patients (34%). Patients were recruited using a sampling method after triage. Triage was the responsibility of an emergency department (ED) nurse in one hospital and of a GP in the other. Those excluded from participation included (a) children under the age of 18 years and not accompanied by a parent or legal guardian, (b) foreign patients not resident in the Netherlands, (c) patients unable to communicate in Dutch or English and (d) patients directly referred to the ED after triage by the GP (in one hospital).RESULTS:People who self-referred generally reported several motives for going to the hospital directly. Information and awareness factors played an important role, often related to a lack of information regarding where to go with a medical complaint. Furthermore, many people who self-referred mentioned hospital facilities, convenience and perceived medical necessity as motivational factors. Barriers deterring a visit to the own GP were mainly logistical, including not being registered with a GP, the GP was too far away, poor GP telephone accessibility or a waiting list for an appointment.CONCLUSION:Information and awareness factors contribute to misperceptions among people who self-referred concerning the complaint, the GP and the hospital. As a range of motivational factors are involved, there is no straightforward solution. However, better dissemination of information might alleviate misconceptions and contribute to providing the right care to the right patient in the right setting. Show less
