The nature and scope of patient-sharing network research: a novel, important area for network science

Tracing the potential of networks to improve community cancer care: an in-depth single case study

Background

Despite overall declines in cancer mortality in the USA over the past three decades, many patients in community settings fail to receive evidence-based cancer care. Networks that link academic medical centers (AMCs) and community providers may reduce disparities by creating access to specialized expertise and care, but research on network effectiveness is mixed. The objective of this study was to identify factors related to whether and how an exemplar AMC network served to provide advice and referral access in community settings.

Methods

An embedded in–depth single case study design was employed to study a network in the Midwest USA that connects a leading cancer specialty AMC with community practices. The embedded case units were a subset of 20 patients with young-onset colorectal cancer or risk-related conditions and the providers involved in their care. The electronic health record (EHR) was reviewed from January 1, 1990, to February 28, 2018. Social network analysis identified care, advice, and referral relationships. Within-case process tracing provided detailed accounts of whether and how the network provided access to expert, evidence-based care or advice in order to identify factors related to network effectiveness.

Results

The network created access to evidence-based advice or care in some but not all case units, and there was variability in whether and how community providers engaged the network, including the path for referrals to the AMC and the way in which advice about an evidence-based approach to care was communicated from AMC specialists to community providers. Factors related to instances when the network functioned as intended included opportunities for both rich and lean communication between community providers and specialists, coordinated referrals, and efficient and adequately utilized documentation systems.

Conclusions

Network existence alone is insufficient to open up access to evidence-based expertise or care for patients in community settings. In-depth understanding of how this network operated provides insight into factors that support or inhibit the potential of networks to minimize disparities in access to evidence-based community cancer care, including both personal and organizational factors.

How Specialist Aftercare Impacts Long-Term Readmission Risks in Elderly Patients With Metabolic, Cardiac, and Chronic Obstructive Pulmonary Diseases: Cohort Study Using Administrative Data

BACKGROUND

The health state of elderly patients is typically characterized by multiple co-occurring diseases requiring the involvement of several types of health care providers.

OBJECTIVE

We aimed to quantify the benefit for multimorbid patients from seeking specialist care in terms of long-term readmission risks.

METHODS

From an administrative database, we identified 225,238 elderly patients with 97 different diagnosis (ICD-10 codes) from hospital stays and contact with 13 medical specialties. For each diagnosis associated with the first hospital stay, we used multiple logistic regression analysis to quantify the sex-specific and age-adjusted long-term all-cause readmission risk (hospitalizations occurring between 3 months and 3 years after the first admission) and how specialist contact impacts these risks.

RESULTS

Men have a higher readmission risk than women (mean difference over all first diagnoses 1.9%, P<.001), but similar reduction in readmission risk after receiving specialist care. Specialist care can reduce readmission risk by almost 50%. We found the greatest reductions in risk when the first hospital stay was associated with diagnoses corresponding to complex chronic diseases such as acute myocardial infarction (57.6% reduction in readmission risk, SE 7.6% for men [m]; 55.9% reduction, SE 9.8% for women [w]), diabetic and other retinopathies (m: 62.3%, SE 8.0; w: 60.1%, SE 8.4%), chronic obstructive pulmonary disease (m: 63.9%, SE 7.8%; w: 58.1%, SE 7.5%), disorders of lipoprotein metabolism (m: 64.7%, SE 3.7%; w: 63.8%, SE 4.0%), and chronic ischemic heart diseases (m: 63.6%, SE 3.1%; w: 65.4%, SE 3.0%).

CONCLUSIONS

Specialist care can greatly reduce long-term readmission risk for patients with chronic and multimorbid diseases. Further research is needed to identify the specific reasons for these findings and to understand the detected sex-specific differences.

A network-based method with privacy-preserving for identifying influential providers in large healthcare service systems

In data science, networks provide a useful abstraction of the structure of many complex systems, ranging from social systems and computer networks to biological networks and physical systems. Healthcare service systems are one of the main social systems that can also be understood using network-based approaches, for example, to identify and evaluate influential providers. In this paper, we propose a network-based method with privacy-preserving for identifying influential providers in large healthcare service systems. First, the provider-interacting network is constructed by employing publicly available information on locations and types of healthcare services of providers. Second, the ranking of nodes in the generated provider-interacting network is conducted in parallel on the basis of four nodal influence metrics. Third, the impact of the top-ranked influential nodes in the provider-interacting network is evaluated using three indicators. Compared with other research work based on patient-sharing networks, in this paper, the provider-interacting network of healthcare service providers can be roughly created according to the locations and the publicly available types of healthcare services, without the need for personally private electronic medical claims, thus protecting the privacy of patients. The proposed method is demonstrated by employing Physician and Other Supplier Data CY 2017, and can be applied to other similar datasets to help make decisions for the optimization of healthcare resources in the response to public health emergencies.