Transforming Neurology Care Delivery Through a Population Health Data Strategy

Abstract

Background

With more than 30% of global data originating from health care, deriving usable insights that improve health requires population health analytics. In neurology, data-driven approaches have grown in significance because of digital health records and advanced analytics. A vital aspect of this evolution is adopting a population health data strategy (PHDS).

Recent Findings

Crafting a tailored PHDS for neurology involves cataloging data points and measures spanning demographics, clinical history, genetics, and social determinants. Neurologic outcomes include mortality rates, functional and cognitive abilities, and imaging results. A robust strategy relies on interoperability, advanced analytics, and transparent AI algorithms.

Summary

Neurology is embracing data-driven health care. The PHDS synthesizes diverse patient data to provide personalized care. It includes a wide range of outcome measures to address neurologic complexities. Advanced analytics and collaboration among neurologists, data scientists, and business leaders uncover hidden patterns and promote outcome-driven medicine in the 21st century.

Background

The evolution of medicine has long been informed by data, but in the 21st century, the amount of data and its granularity has vastly expanded. More than 30% of the world's data volume is being generated by the health care industry, outpacing manufacturing, financial services, and media and entertainment.¹ Population health, a discipline that emphasizes health outcomes in a group of individuals, harnesses the power of data through analytics to provide valuable insights and actionable recommendations. The population health-based data model integrates multiple sources of information at the person level and enables longitudinal measurement.²

In this volume of papers dedicated to value in neurologic care delivery, challenges in data collection, access, integration, and modeling as well as appropriate measurement and methods were all identified as challenges to transforming to a value-based approach (A.M. Wilson, et al., unpublished data, 2024). In the evolving landscape of medical care, neurology, like many other specialties, is increasingly recognizing that data-driven methodologies are paramount to successful patient outcomes.^1,3,4 With the surge in digital health records and the capabilities of advanced analytics, a paradigm shift is evident. While the proliferation of the electronic medical record (EMR) has been crucial to managing quality and variation risk in primary care, neurology care has yet to capitalize on EMR data to drive systems-level care delivery.

Anchoring this transformation is the adoption of a population health data strategy. For neurologists aiming to harness the full potential of this approach, understanding foundational data, measures, and the roles and responsibilities of team members is paramount. In this study, we delve into these essential elements of a population health data strategy tailored for neurologists.²

Foundational Data and Measures of the Population Health Data Strategy

The first step in tailoring the population health data strategy is inventorying the data points and measures to include. Of interest for neurologic outcomes are demographic details, clinical history, genetics, environmental exposures, and social determinants of health. Patient outcomes data are critical indicators of the effectiveness and quality of medical care and interventions. For the field of neurology, outcomes data can encompass a wide range of measures, given the diversity of neurologic conditions. Table 1 presents important assessments, scales, and measures whose data points are needed to measure patient outcomes specific to neurology.

Table 1.

Assessments, Scales, and Measures for a Neurology-Focused Population Data Strategy

Scale or measure	Neurology-focused information
Mortality rate	The percentage of patients with a particular neurologic condition (e.g., stroke, brain tumors) who die within a certain timeframe after diagnosis or treatment5
Functional capabilities	Includes the degree of mobility in patients' poststroke or spinal cord injury, recovery of speech and language abilities after a stroke, and return to activities of daily living (ADLs) after traumatic brain injury6-9
Neurologic deficit scales	Includes the NIH Stroke Scale (NIHSS) scores for stroke patients and the Glasgow Coma Scale or the Comprehensive Executive Function Inventory (CEFI) for patients with traumatic brain injury10,11
Symptom reduction and management in neurologic conditions	For example, decreased frequency and severity of epileptic seizures after starting a new medication and improvement or resolution of symptoms after sport-related concussion12-14
Cognitive assessments	Captures memory, skills in language, math, and spatial abilities and other mental functions for patients with dementia or mild cognitive impairment15,16
Quality of life scale	Patient-reported outcomes using tools like the Quality of Life in Neurological Disorders (Neuro-QoL) measure or the Parkinson Disease Questionnaire17-19
Complication rates	Include incidence of complications after neurosurgical procedures, such as infection, hemorrhage, or neurologic deficits20
Recurrence rates	Measures the percentage of patients who avoid or experience a recurrence of conditions like stroke or seizures within a specified time after initial treatment21,22
Imaging outcomes	Includes resolution or reduction in tumor size on MRI after treatment for brain tumors, restoration of blood flow on imaging after intervention for ischemic stroke23,24
Medication adherence and efficacy	Rates of adherence to prescribed neurologic medications and assessment of medication effectiveness and the need for dose adjustments25,26
Patient satisfaction	Patient-reported satisfaction with the care received encompasses factors like the quality of doctor-patient communication, wait times, and overall confidence in the care team27
Rehabilitation progress or improvement	In physical, occupational, or speech therapy sessions for patients with various neurologic impairments28
Social determinants of health (SDoH)	Includes economic stability (employment status, income level, medical debt), education (literacy level, educational attainment), social and community context (social support, exposure to violence or trauma such as traumatic brain injury), health care access (coverage, transportation access), neighborhood and built environment (housing stability, access to healthy foods, environmental exposures), culture and language (primary language spoken, cultural beliefs29
Value-based care (VBC) contract outcomes	Emphasize quality, efficiency, and overall value of the care provided. In the context of neurology, data to collect or calculate for reporting include clinical outcomes (readmission rates, chronic disease management, preventive care), cost efficiency (cost savings, utilization management), patience experience (satisfaction and engagement), care coordination (referral efficiency, care continuity, care management), provider experience (engagement and satisfaction, retention rate, qualitative feedback on VBC model), financial outcomes (shared savings, performance against benchmark, risk management)
Population health management metrics	Include disease incidence and prevalence, community health initiatives and impact such as stroke prevention community programs, prevention programs, quality metrics, adherence to neurologic clinical guidelines and care pathways, clinical outcomes such as improvements in mobility poststroke or reduced seizure episodes in epilepsy patients because of interventions, effective use of EHRs to track outcomes and coordinate care, and telehealth and the impact of remote monitoring in managing neurologic conditions

Paired with tools and platforms that can deliver existing data for benchmarking, advanced analytics methods to analyze the vast amounts of data, and transparent machine learning and AI algorithms organizations can begin to identify patterns and correlations that might be missed in a model that treats one patient at a time. In addition, use of technology and its interoperability is an essential element to success. Systems, including EMRs, must be capable of sharing and receiving data across platforms. As health care continues to transition toward value-based models, outcome measures become pivotal in gauging success. The emphasis shifts from sheer volume to the outcomes-based value delivered to patients, making the health system more patient-centric, efficient, and outcome-driven.

Key Considerations for Partnering to Execute the Population Health Data Strategy

In today's era of digital medicine, harnessing the power of data is essential for optimizing patient care, especially in specialized fields like neurology. Challenges to implementing a population health data strategy in neurology such as ensuring interoperability across technologies, managing cybersecurity, EMR-related clinician fatigue, and the proliferation of technology can be overcome by choosing data and analytic partners that have existing data sets to benchmark performance, understand health care and specialty care workflows, and have built-in data governance processes. These partners must be able to integrate data sources, normalize data into practical data models, use robust methods of analysis, deliver reporting and visualization tools, and provide insights for action or intervention.

Roles and Responsibilities of Neurologists, Data Scientists/Analysts, and Health Equity/Business Leaders

Best practice to support performance toward comprehensive whole person care delivery in a value-based care model, especially in fields as specialized as neurology, requires each key stakeholder to perform a unique role in a team troika to improve patient care, care affordability, and quality.² Neurologists, data scientists/analysts, and health equity/business leaders all play a role in contributing specific expertise and knowledge to identifying insights from data and translating them to effective actions and interventions. Neurologists stand at the forefront, shouldering the pivotal role of patient care. Although their expertise in neurologic disorders is paramount, their role has evolved. They are now expected to immerse themselves in the latest data insights, ensure meticulous data entry into EHRs, and actively collaborate in research and interventions.

Population Health Data Models

The population health data model (PHDM) is an example of data infrastructure and modeling that can support value-based care analytics for neurology. In its basic form, the PHDM has person-level observations from multiple data sources at equally distant points in time that can be aggregated to an entire population and segmented by smaller subgroupings.² The Observations Medical Outcomes Partnership Common Data Model is an example of the PHDM used mainly in research.³⁰ In practice, health care delivery and payer organizations have built PHDMs in their EMR and enterprise data warehouses to support value-based care. The PHDM is not specific to neurology but can be tailored to support population assessment, stratification and segmentation, and outcomes measurement specific to neurology practice.

The PHDM enables accessibility of identifiable data at an individual level, facilitating the extraction of insights regarding factors influencing health outcomes, identification of the most appropriate health care interventions for overall health care requirements, anticipation of unfavorable outcomes, and informed choices regarding the distribution of health care resources. Ethical principles must be incorporated into PHDM development and application to mitigate bias and unintended consequences of its use.²

Conclusion

The adoption of a population health data strategy is not just an upgrade; it is a transformation. By capitalizing on the power of data, neurologists can refine their decision-making processes, leading to more personalized care, better outcomes, and a profound impact on community health. Embracing this model will position neurologists at the forefront of 21st century medicine, where data drive decisions and every patient benefits from the collective knowledge of the medical community.

TAKE-HOME POINTS

• → Data-driven health care evolution: The surge in health care data generation surpasses other industries, propelling the adoption of population health analytics. Neurology, like other specialties, increasingly relies on data-driven strategies for improved patient outcomes.

• → Population health data strategy: The adoption of a population health data strategy in neurology involves synthesizing diverse data points and measures. This strategy offers insights into patient demographics, clinical history, genetics, and social determinants of health, fostering personalized care.

• → Diverse outcome measures: Neurologic care demands a broad spectrum of outcome measures, including mortality rates, functional capabilities, cognitive assessments, and imaging outcomes. This comprehensive approach reflects the intricacies of neurologic conditions and patient needs.

• → Advanced analytics: Adopting a population health strategy in neurology entails leveraging advanced analytics, transparent AI algorithms, and interoperable technology systems. This data-driven and technology approach uncovers hidden patterns and correlations, enabling value-based care and outcome-driven medicine.

• → Collaborative teams for success: The successful execution of the population health data strategy relies on a synergistic partnership among neurologists, data scientists/analysts, and health equity/business leaders. By translating data insights into effective interventions, this collaboration empowers neurology to lead in 21st century medicine, fostering personalized care and community health.

Appendix. Authors

Name	Location	Contribution
Ines M. Vigil, MD, MPH, MBA	Clarify Health Solutions	Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data
Martha Sylvia, PhD, MBA, RN	Medical University of South Carolina College of Nursing	Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data

Study Funding

The authors report no targeted funding.

Disclosure

The authors report no relevant disclosures. Full disclosure form information provided by the authors is available with the full text of this article at Neurology.org/cp.