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Quantification of the resilience of primary care networks by stress testing the health care system.

ABSTRACT: There are practically no quantitative tools for understanding how much stress a health care system can absorb before it loses its ability to provide care. We propose to measure the resilience of health care systems with respect to changes in the density of primary care providers. We develop a computational model on a 1-to-1 scale for a countrywide primary care sector based on patient-sharing networks. Nodes represent all primary care providers in a country; links indicate patient flows between them. The removal of providers could cause a cascade of patient displacements, as patients have to find alternative providers. The model is calibrated with nationwide data from Austria that includes almost all primary care contacts over 2 y. We assign 2 properties to every provider: the "CareRank" measures the average number of displacements caused by a provider's removal (systemic risk) as well as the fraction of patients a provider can absorb when others default (systemic benefit). Below a critical number of providers, large-scale cascades of patient displacements occur, and no more providers can be found in a given region. We quantify regional resilience as the maximum fraction of providers that can be removed before cascading events prevent coverage for all patients within a district. We find considerable regional heterogeneity in the critical transition point from resilient to nonresilient behavior. We demonstrate that health care resilience cannot be quantified by physician density alone but must take into account how networked systems respond and restructure in response to shocks. The approach can identify systemically relevant providers.

SUBMITTER: Lo Sardo DR

PROVIDER: S-EPMC6883827 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Quantification of the resilience of primary care networks by stress testing the health care system.

Lo Sardo Donald Ruggiero DR Thurner Stefan S Sorger Johannes J Duftschmid Georg G Endel Gottfried G Klimek Peter P

Proceedings of the National Academy of Sciences of the United States of America 20191111 48

There are practically no quantitative tools for understanding how much stress a health care system can absorb before it loses its ability to provide care. We propose to measure the resilience of health care systems with respect to changes in the density of primary care providers. We develop a computational model on a 1-to-1 scale for a countrywide primary care sector based on patient-sharing networks. Nodes represent all primary care providers in a country; links indicate patient flows between t ...[more]

PMID: 31712415

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Project description:BackgroundPrimary care is a relevant pillar in managing not only individual, but also societal medical crises. The COVID-19 pandemic has demanded a rapid response from primary care with interventions in the health care system. The aim of this paper was to explore the responses of primary care practitioners (PCP) during the early COVID-19 pandemic and to analyze these with a view on the resilience of the primary health care system from the PCPs perspective.MethodsShortly after the first COVID-19 wave (July-October 2020) n = 39, semi-structured telephone interviews were conducted with PCP in practices and at Corona contact points (CCP) in Baden-Wuerttemberg (Germany). Qualitative content analysis was applied, and the evolved categories were related to in a framework for resilience.ResultsPrimary care had an overall strong ability to adapt and show resilience, albeit with wide variance in speed and scope of the responses. When coping with uncertainty, the reasons given by PCPs in favor of opening a CCP mainly involved intrinsic motivation and self-initiative; the reasons against doing so were i.e. the lack of personal protective equipment, problems with space, and worries about organizational burden. A strong association existed between the establishment of a CCP and the use of resources (i.e. existing networks, personal protective equipment, exercising an office of professional political function). Our study predominantly found adaptive aspects for measures taken at medical practices and transformative aspects for setting up outpatient infection centers. PCPs played an important role in the coordination process (i.e. actively transferring knowledge, integration in crisis management teams, inclusion in regional strategic efforts) reaching a high level in the dimensions knowledge and legitimacy. The dimension interdependence repeatedly came into focus (i.e. working with stakeholders to open CCP, interacting among different types of primary care facilities, intersectoral interfaces). A need for regional capacity planning was visible at the time of the interviews.ConclusionsThe results can be used for practical and research-based institutional and capacity planning, for developing resilience in primary care and for augmentation by perspectives from other stakeholders in the primary health care system.

Project description:BACKGROUND:Health system performance is one of the important components of the health care delivery; its achievement depends on the quality of services rendered and the health system responsiveness of its beneficiaries. Health system responsiveness is a multi-dimensional concept and is usually measured through several domains. Health system responsiveness (HSR) remains to be a key indicator for evaluation of health system performance in any settings. This study aimed at assessing the situation of health system responsiveness in primary health facilities in Tanzania prior to introduction of the Direct Health Facility Financing (DHFF) program. METHODS:This was a cross sectional study conducted between January and February in 2018. We collected data from 42 primary health facilities (14 health centers and 28 dispensaries) where a questionnaire was administered to a total of 422 participants. The questionnaire collected information on attention, respect to dignity, clear communication, autonomy, access to care, respect to confidentiality and basic amenities. Descriptive analysis was done to determine the distribution of the variables whereas ANOVA and linear regression analysis was employed to discern the association between variables. RESULTS:More than 67% of participants had visited the same health facility more than 5 times. Sixty seven percent of the patients were residing within 5kms from the public primary health care facilities. The geographical access to health care scored the lowest (43.5% for Dispensaries and 36% for Health center) mean as compared to other domains of health system responsiveness. The highest score was in respect to confidentiality (86.7%) followed by respect to dignity (81.4%). Linear regression analysis revealed no statistical association between any of the social demographic features with the overall HSR performances. However, in post hoc analysis, Pwani and Shinyanga regions didn't differ significantly in terms of their performances whereas those two regions differ from all other regions. CONCLUSION:Based on the study findings health system responsiveness domains has performed relatively poor in many regions except for respect of dignity and confidentiality scored high of all the domains. Shinyanga and Pwani regions scored relatively well in all domains this could have been due to the effect of Results Based financing (RBF) in the respective regions. All in all the Government and other stakeholders in the health sector they should deliberately invest on the access to care domain as seem to be a challenge as compared to others.

Project description:BackgroundThere are several definitions of resilience in health systems, many of which share some characteristics, but no agreed-upon framework is universally accepted. Here, we review the concept of resilience, identifying its definitions, attributes, antecedents and consequences, and present the findings of a concept analysis of health system resilience.MethodsWe follow Schwarz-Barcott and Kim's hybrid model, which consists of three phases: theoretical, fieldwork and final analysis. We identified the concept definitions, attributes, antecedents and consequences of health system resilience and constructed an evidence-informed framework on the basis of the findings of this review. We searched PubMed, PsycINFO, CINAHL Complete, EBSCOhost-Academic Search and Premier databases and downloaded identified titles and abstracts on Covidence. We screened 3357 titles and removed duplicate and ineligible records; two reviewers then screened each title, and disagreements were resolved by discussion with the third reviewer. From the 130 eligible manuscripts, we identified the definitions, attributes, antecedents and consequences using a pre-defined data extraction form.ResultsResilience antecedents are decentralization, available funds, investments and resources, staff environment and motivation, integration and networking and finally, diversification of staff. The attributes are the availability of resources and funds, adaptive capacity, transformative capacity, learning and advocacy and progressive leadership. The consequences of health system resilience are improved health system performance, a balanced governance structure, improved expenditure and financial management of health and maintenance of health services that support universal health coverage (UHC) throughout crises.ConclusionA resilient health system maintains quality healthcare through times of crisis. During the coronavirus disease 2019 (COVID-19) epidemic, several seemingly robust health systems were strained under the increased demand, and services were disrupted. As such, elements of resilience should be integrated into the functions of a health system to ensure standardized and consistent service quality and delivery. We offer a systematic, evidence-informed method for identifying the attributes of health system resilience, intending to eventually be used to develop a measuring tool to evaluate a country's health system resilience performance.

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Quantification of the resilience of primary care networks by stress testing the health care system.

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Quantification of the resilience of primary care networks by stress testing the health care system.

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