Venous Access: National Guideline and Registry Development (VANGUARD): Advancing Patient-Centered Venous Access Care Through the Development of a National Coordinated Registry Network

There are over 8 million central venous access devices inserted each year, many in patients with chronic conditions who rely on central access for life-preserving therapies. Central venous access device-related complications can be life-threatening and add tens of billions of dollars to health care costs, while their incidence is most likely grossly mis- or underreported by medical institutions. In this communication, we review the challenges that impair retention, exchange, and analysis of data necessary for a meaningful understanding of critical events and outcomes in this clinical domain. The difficulty is not only with data extraction and harmonization from electronic health records, national surveillance systems, or other health information repositories where data might be stored. The problem is that reliable and appropriate data are not recorded, or falsely recorded, at least in part because policy, payment, penalties, proprietary concerns, and workflow burdens discourage completeness and accuracy. We provide a roadmap for the development of health care information systems and infrastructure that address these challenges, framed within the context of research studies that build a framework of standardized terminology, decision support, data capture, and information exchange necessary for the task. This roadmap is embedded in a broader Coordinated Registry Network Learning Community, and facilitated by the Medical Device Epidemiology Network, a Public-Private Partnership sponsored by the US Food and Drug Administration, with the scope of advancing methods, national and international infrastructure, and partnerships needed for the evaluation of medical devices throughout their total life cycle.

Adapted Kaizen: Multi-Organizational Complex Process Redesign for Adapting Clinical Guidelines for the Digital Age

The need for a method to examine complex, multidisciplinary processes involving many diverse organizations initially led multiple US federal agencies to adopt the traditional Kaizen, a Lean process improvement method typically used within a single organization, to encompass multiple organizations each with its own leadership and priorities. First, the Centers for Medicare and Medicaid Services and the Office of the National Coordinator for Health Information Technology adapted Kaizen to federal agency processes for the development of electronic clinical quality measures. Later, the Centers for Disease Control and Prevention (CDC) further modified this adapted Kaizen during its Adapting Clinical Guidelines for the Digital Age (ACG) initiative, which aimed to improve the broader scope of guideline development and implementation. This is a methods article to document the adapted Kaizen method for future use in similar complex processes, illustrating how to apply the adapted Kaizen through CDC's ACG initiative and showing the reach achieved by using the adapted Kaizen method. The adapted Kaizen includes pre-Kaizen planning, a Kaizen event, and post-Kaizen implementation that accommodate multidisciplinary and multi-organizational participation. ACG included 5 workgroups that each developed products to support their respective scope: Guideline Creation, Informatics Framework, Translation and Implementation, Communication and Dissemination, and Evaluation. Despite challenges gathering diverse perspectives and balancing the competing priorities of multiple organizations, the ACG participants produced interrelated standards, processes, and tools-further described in separate publications-that programs and partners have leveraged. Use of a siloed approach may not have supported the development and dissemination of these products.

Building capacity of community health centers to overcome data challenges with the development of an agile COVID-19 public health registry: a multistate quality improvement effort

Objective

During the coronavirus disease 2019 (COVID-19) pandemic, federally qualified health centers rapidly mobilized to provide SARS-CoV-2 testing, COVID-19 care, and vaccination to populations at increased risk for COVID-19 morbidity and mortality. We describe the development of a reusable public health data analytics system for reuse of clinical data to evaluate the health burden, disparities, and impact of COVID-19 on populations served by health centers.

Materials and Methods

The Multistate Data Strategy engaged project partners to assess public health readiness and COVID-19 data challenges. An infrastructure for data capture and sharing procedures between health centers and public health agencies was developed to support existing capabilities and data capacities to respond to the pandemic.

Results

Between August 2020 and March 2021, project partners evaluated their data capture and sharing capabilities and reported challenges and preliminary data. Major interoperability challenges included poorly aligned federal, state, and local reporting requirements, lack of unique patient identifiers, lack of access to pharmacy, claims and laboratory data, missing data, and proprietary data standards and extraction methods.

Discussion

Efforts to access and align project partners’ existing health systems data infrastructure in the context of the pandemic highlighted complex interoperability challenges. These challenges remain significant barriers to real-time data analytics and efforts to improve health outcomes and mitigate inequities through data-driven responses.

Conclusion

The reusable public health data analytics system created in the Multistate Data Strategy can be adapted and scaled for other health center networks to facilitate data aggregation and dashboards for public health, organizational planning, and quality improvement and can inform local, state, and national COVID-19 response efforts.

The role of technology and engineering models in transforming healthcare

The healthcare system is in crisis due to challenges including escalating costs, the inconsistent provision of care, an aging population, and high burden of chronic disease related to health behaviors. Mitigating this crisis will require a major transformation of healthcare to be proactive, preventive, patient-centered, and evidence-based with a focus on improving quality-of-life. Information technology, networking, and biomedical engineering are likely to be essential in making this transformation possible with the help of advances, such as sensor technology, mobile computing, machine learning, etc. This paper has three themes: 1) motivation for a transformation of healthcare; 2) description of how information technology and engineering can support this transformation with the help of computational models; and 3) a technical overview of several research areas that illustrate the need for mathematical modeling approaches, ranging from sparse sampling to behavioral phenotyping and early detection. A key tenet of this paper concerns complementing prior work on patient-specific modeling and simulation by modeling neuropsychological, behavioral, and social phenomena. The resulting models, in combination with frequent or continuous measurements, are likely to be key components of health interventions to enhance health and wellbeing and the provision of healthcare.