Assessing Association Between Team Structure and Health Outcome and Cost by Social Network Analysis

Organization and quality of care in patient-sharing networks

Healthcare systems seek to provide continuous and coordinated care of high quality. However, patient pathways in the ambulatory sector may differ and result in various provider units. Our aim was to analyze whether health outcomes and the quality of care differ between different types of patient-sharing physician networks. We analyzed administrative data on patients with diagnosed heart failure in Germany. We investigated distinct networks of ambulatory physicians by using a modular-based optimization algorithm and characterized each network as having either a key physician at its center or some other kind of configuration. We subsequently conducted multilevel regression analyses to estimate the impact a network's configuration has on hospitalization rates and guideline-based process indicators. We identified 1,847 networks, of which 27% had a key physician at their center. Compared to physician networks with other configurations, networks that had a key physician at their center were associated in our regression analysis with (a) somewhat lower hospitalization rates, and (b) heart failure treatment that was more frequently in concordance with the German national treatment guideline. Organizing healthcare for people with chronic disease into units that have a key physician at their center and include the relevant specialists may foster treatment that is effective and of higher quality.

Provision of evaluation and management visits by nurse practitioners and physician assistants in the USA from 2013 to 2019: cross-sectional time series study

OBJECTIVE

To examine the proportion of healthcare visits are delivered by nurse practitioners and physician assistants versus physicians and how this has changed over time and by clinical setting, diagnosis, and patient demographics.

DESIGN

Cross-sectional time series study.

SETTING

National data from the traditional Medicare insurance program in the USA.

PARTICIPANTS

Of people using Medicare (ie, those older than 65 years, permanently disabled, and people with end stage renal disease), a 20% random sample was taken.

MAIN OUTCOME MEASURES

The proportion of physician, nurse practitioner, and physician assistant visits in the outpatient and skilled nursing facility settings delivered by physicians, nurse practitioners, and physician assistants, and how this proportion varies by type of visit and diagnosis.

RESULTS

From 1 January 2013 to 31 December 2019, 276 million visits were included in the sample. The proportion of all visits delivered by nurse practitioners and physician assistants in a year increased from 14.0% (95% confidence interval 14.0% to 14.0%) to 25.6% (25.6% to 25.6%). In 2019, the proportion of visits delivered by a nurse practitioner or physician assistant varied across conditions, ranging from 13.2% for eye disorders and 20.4% for hypertension to 36.7% for anxiety disorders and 41.5% for respiratory infections. Among all patients with at least one visit in 2019, 41.9% had one or more nurse practitioner or physician assistant visits. Compared with patients who had no visits from a nurse practitioner or physician assistant, the likelihood of receiving any care was greatest among patients who were lower income (2.9% greater), rural residents (19.7%), and disabled (5.6%).

CONCLUSION

The proportion of visits delivered by nurse practitioners and physician assistants in the USA is increasing rapidly and now accounts for a quarter of all healthcare visits.

Telehealth Use, Care Continuity, and Quality: Diabetes and Hypertension Care in Community Health Centers Before and During the COVID-19 Pandemic

BACKGROUND

Community health centers (CHCs) pivoted to using telehealth to deliver chronic care during the coronavirus COVID-19 pandemic. While care continuity can improve care quality and patients' experiences, it is unclear whether telehealth supported this relationship.

OBJECTIVE

We examine the association of care continuity with diabetes and hypertension care quality in CHCs before and during COVID-19 and the mediating effect of telehealth.

RESEARCH DESIGN

This was a cohort study.

PARTICIPANTS

Electronic health record data from 166 CHCs with n=20,792 patients with diabetes and/or hypertension with ≥2 encounters/year during 2019 and 2020.

METHODS

Multivariable logistic regression models estimated the association of care continuity (Modified Modified Continuity Index; MMCI) with telehealth use and care processes. Generalized linear regression models estimated the association of MMCI and intermediate outcomes. Formal mediation analyses assessed whether telehealth mediated the association of MMCI with A1c testing during 2020.

RESULTS

MMCI [2019: odds ratio (OR)=1.98, marginal effect=0.69, z=165.50, P<0.001; 2020: OR=1.50, marginal effect=0.63, z=147.73, P<0.001] and telehealth use (2019: OR=1.50, marginal effect=0.85, z=122.87, P<0.001; 2020: OR=10.00, marginal effect=0.90, z=155.57, P<0.001) were associated with higher odds of A1c testing. MMCI was associated with lower systolic (β=-2.90, P<0.001) and diastolic blood pressure (β=-1.44, P<0.001) in 2020, and lower A1c values (2019: β=-0.57, P=0.007; 2020: β=-0.45, P=0.008) in both years. In 2020, telehealth use mediated 38.7% of the relationship between MMCI and A1c testing.

CONCLUSIONS

Higher care continuity is associated with telehealth use and A1c testing, and lower A1c and blood pressure. Telehealth use mediates the association of care continuity and A1c testing. Care continuity may facilitate telehealth use and resilient performance on process measures.

Information exchange networks for chronic diseases in primary care practices in Germany: a cross-sectional study

Background

Coordination of care requires information exchange between health workers. The structure of their information exchange networks may influence the quality and efficiency of healthcare delivery. The aim of this study was to explore and classify information exchange networks in primary care for patients with chronic diseases in Germany.

Methods

A cross-sectional study was carried out between 2019 and 2021. As part of a larger project on coordination of care, this study focused on information exchange in practice teams regarding patients with type 2 diabetes (DM), coronary heart disease (CHD) and chronic heart failure (CHF). Social network analysis was applied to determine the number of connections, density and centralization for each of the health conditions for each of the practices. On the basis of the descriptive findings, we developed typologies of information exchange networks in primary care practices.

Results

We included 153 health workers from 40 practices, of which 25 practices were included in the social network analysis. Four types of information exchange structures were identified for the three chronic diseases: highly connected networks with low hierarchy, medium connected networks with medium hierarchy, medium connected networks with low hierarchy and lowly connected networks. Highly connected networks with low hierarchy were identified most frequently (18 networks for DM, 17 for CHD and 14 for CHF). Of the three chronic conditions, information sharing about patients with DM involved the most team members. Information exchange outside the family practice took place mainly with nurses and pharmacists.

Conclusions

This study identified four types of information exchange structures, which provides a practical tool for management and improvement in primary care. Some practices had few information transfer connections and could hardly be considered a network.

Trial registration

We registered the study prospectively on 7 November 2019 at the German Clinical Trials Register (DRKS, www.drks.de) under ID no. DRKS00019219.

Regional medical inter-institutional cooperation in medical provider network constructed using patient claims data from Japan

The aging world population requires a sustainable and high-quality healthcare system. To examine the efficiency of medical cooperation, medical provider and physician networks were constructed using patient claims data. Previous studies have shown that these networks contain information on medical cooperation. However, the usage patterns of multiple medical providers in a series of medical services have not been considered. In addition, these studies used only general network features to represent medical cooperation, but their expressive ability was low. To overcome these limitations, we analyzed the medical provider network to examine its overall contribution to the quality of healthcare provided by cooperation between medical providers in a series of medical services. This study focused on: i) the method of feature extraction from the network, ii) incorporation of the usage pattern of medical providers, and iii) expressive ability of the statistical model. Femoral neck fractures were selected as the target disease. To build the medical provider networks, we analyzed the patient claims data from a single prefecture in Japan between January 1, 2014 and December 31, 2019. We considered four types of models. Models 1 and 2 use node strength and linear regression, with Model 2 also incorporating patient age as an input. Models 3 and 4 use feature representation by node2vec with linear regression and regression tree ensemble, a machine learning method. The results showed that medical providers with higher levels of cooperation reduce the duration of hospital stay. The overall contribution of the medical cooperation to the duration of hospital stay extracted from the medical provider network using node2vec is approximately 20%, which is approximately 20 times higher than the model using strength.

Using social network analysis methods to identify networks of physicians responsible for the care of specific patient populations

BACKGROUND

Coordinating health care within and among sectors is crucial to improving quality of care and avoiding undesirable negative health outcomes, such as avoidable hospitalizations. Quality circles are one approach to strengthening collaboration among health care providers and improving the continuity of care. However, identifying and including the right health professionals in such meetings is challenging, especially in settings with no predefined patient pathways. Based on the Accountable Care in Germany (ACD) project, our study presents a framework for and investigates the feasibility of applying social network analysis (SNA) to routine data in order to identify networks of ambulatory physicians who can be considered responsible for the care of specific patients.

METHODS

The ACD study objectives predefined the characteristics of the networks. SNA provides a methodology to identify physicians who have patients in common and ensure that they are involved in health care provision. An expert panel consisting of physicians, health services researchers, and data specialists examined the concept of network construction through informed decisions. The procedure was structured by five steps and was applied to routine data from three German states.

RESULTS

In total, 510 networks of ambulatory physicians met our predefined inclusion criteria. The networks had between 20 and 120 physicians, and 72% included at least ten different medical specialties. Overall, general practitioners accounted for the largest proportion of physicians in the networks (45%), followed by gynecologists (10%), orthopedists, and ophthalmologists (5%). The specialties were distributed similarly across the majority of networks. The number of patients this study allocated to the networks varied between 95 and 45,268 depending on the number and specialization of physicians per network.

CONCLUSIONS

The networks were constructed according to the predefined characteristics following the ACD study objectives, e.g., size of and specialization composition in the networks. This study shows that it is feasible to apply SNA to routine data in order to identify groups of ambulatory physicians who are involved in the treatment of a specific patient population. Whether these doctors are also mainly responsible for care and if their active collaboration can improve the quality of care still needs to be examined.

Impact of State Nurse Practitioner Regulations on Potentially Inappropriate Medication Prescribing Between Physicians and Nurse Practitioners: A National Study in the United States

BACKGROUND

The American Geriatrics Society regularly updates the Beers Criteria for Potentially Inappropriate Medication (PIM) to improve prescribing safety.

PURPOSE

This study assessed the impact of nurse practitioner (NP) practices on PIM prescribing across states in the United States and compared the change in PIM prescribing rates between 2016 and 2018.

METHODS

We used data from a random selection of 20% of Medicare beneficiaries (66 years or older) from 2015 to 2018 to perform multilevel logistic regression. A PIM prescription was classified as initial or refill on the basis of medication history 1 year before a visit. PIM use after an outpatient visit was the primary study outcome.

RESULTS

We included 9 000 224 visits in 2016 and 9 310 261 in 2018. The PIM prescription rate was lower in states with full NP practice and lower among NPs than among physicians; these rates for both physicians and NPs decreased from 2016 to 2018.

CONCLUSIONS

Changes could be due to individual state practices.

Making sense of the French public hospital system: a network-based approach to hospital clustering using unsupervised learning methods

BACKGROUND

Hospitals in the public and private sectors tend to join larger organizations to form hospital groups. This increasingly frequent mode of functioning raises the question of how countries should organize their health system, according to the interactions already present between their hospitals. The objective of this study was to identify distinctive profiles of French hospitals according to their characteristics and their role in the French hospital network.

METHODS

Data were extracted from the national hospital database for year 2016. The database was restricted to public hospitals that practiced medicine, surgery or obstetrics. Hospitals profiles were determined using the k-means method. The variables entered in the clustering algorithm were: the number of stays, the effective diversity of hospital activity, and a network-based mobility indicator (proportion of stays followed by another stay in a different hospital of the same Regional Hospital Group within 90 days).

RESULTS

Three hospital groups were identified by the clustering algorithm. The first group was constituted of 34 large hospitals (median 82,100 annual stays, interquartile range 69,004 - 117,774) with a very diverse activity. The second group contained medium-sized hospitals (with a median of 258 beds, interquartile range 164 - 377). The third group featured less diversity regarding the type of stay (with a mean of 8 effective activity domains, standard deviation 2.73), a smaller size and a higher proportion of patients that subsequently visited other hospitals (11%). The most frequent type of patient mobility occurred from the hospitals in group 2 to the hospitals in group 1 (29%). The reverse direction was less frequent (19%).

CONCLUSIONS

The French hospital network is organized around three categories of public hospitals, with an unbalanced and disassortative patient flow. This type of organization has implications for hospital planning and infectious diseases control.