Ambulatory & Post-Acute Care

Rising acuity, swelling healthcare demands, and increasing focus on social drivers, understanding how to serve the growing demand of outpatient management with existing resources is a growing challenge. Leverage data analytics to address ambulatory practice challenges and augment the clinical and operational decisions that impact the well-being of patients and success and satisfaction of providers.

Sample Solutions

Patient Population Management

Identifying High-Risk Patients: Identify patients at higher risk for chronic conditions or specific health issues to proactively manage care, preventing complications and reducing healthcare costs.

Tailored Care Plans: Leverage data to create personalized care plans based on individual patient needs. This patient-centered approach can lead to improved outcomes and patient satisfaction.

Operational Efficiency

Improving Appointment Access: Optimize appointment scheduling by analyzing historical data to identify peak appointment times, reduce wait times, and improve patient access to care and better utilize resources.

Streamline Workflows: Identify inefficiencies in practice workflows to optimize staff schedules, support equitable workload distribution, reduce wait times, and improve the provider and patient experience.

Clinical Decision Support

Augment Clinical Decision Making: Provide clinicians with valuable insights, including evidence-based guidelines and best practice and patient physiologic trajectory, at the point of care to support informed treatment decisions and high-quality care.

Quality Improvement

Monitor Key Performance Indicators: Track performance on key measures related to clinical quality, patient safety, and overall practice performance.

Compare Performance: Benchmark performance against peer practices to provide insights into areas for improvement and opportunities to adopt best practices.

Revenue Cycle Management

Identify Billing and Coding Opportunities: Assist revenue cycle management by identifying coding errors, improving claims submission processes, and optimizing reimbursement strategies.

Supply Chain Management

Optimizing Inventory: Leverage historical data trends and predictive analytics to manage inventory efficiently, ensuring that medical supplies and medications are adequately stocked. This reduces waste, prevents shortages, and contributes to cost savings.

Delivering top-tier analytical and research solutions that truly make a difference in the healthcare landscape

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Joseph Beals, PhD, MBA, FAMIA

Joe is an accomplished healthcare executive with a track-record of bringing innovative technologies to market and helping client organizations develop and execute digital health strategies. A common thread throughout his career is a passion for translational data science – the “bench to bedside” operationalization of evidence-based insights and data driven technologies.

Joe has extensive experience in health informatics research, including predictive model development, clinical trial design, and process and outcomes improvement. He has published widely and received research awards from the American Medical Informatics Association, the Society of Critical Care Medicine, and the Journal of Biomedical Informatics. He worked closely with the FDA on one of the first real-world evidence supported regulatory clearances for AI/ML software used in predicting patient deterioration. Joe has led US and European federal grant projects, most recently as co-principal investigator on a contract from the US Department of Health and Human Services Biomedical Advanced Research and Development Authority to develop and deploy an Emergency Department triage tool.

In a prior role as CEO of PeraHealth, Joe expanded the company’s focus to encompass health care delivery challenges across the care continuum, including in sub-acute and home health environments and he oversaw the transformation of the company’s analytics service offerings in these areas. Following the acquisition of PeraHealth by Spacelabs Healthcare, Joe led Spacelab’s data science and analytics strategy, spear-heading the company’s digital health and analytics roadmap. Joe is a long-time proponent of the concept of augmented intelligence in healthcare – the operationalization of innovative AI/ML technologies which can both enhance the effectiveness and improve the experience of clinical care providers.

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Kathy W. Belk

Kathy is a clinical informaticist, data scientist and healthcare technologist with over 25 years of experience conducting healthcare research and performance improvement initiatives.

She has a passion to transform healthcare through data-driven care redesign and a diverse background enabling her to balance innovative strategy with rigorous scientific methodologies to create fit-for-purpose solutions.
She has extensive experience leading data-driven projects for pharmaceutical, biotech, medical device companies, health service providers as well as specialty societies and government agencies. Her work includes both traditional and innovative study designs across a wide range of topics and clinical areas and has resulted in more than 100 peer-reviewed publications and/or presentations in scientific journals and conferences.

Kathy has extensive data collection and linkage experience across many types of healthcare datasets including two used by the U.S. Food and Drug Administration, for which she was a subject matter expert. In a prior role with Premier, Inc., she served as data expert and lead scientist for an innovative pay-for-performance demonstration project with the Centers for Medicare & Medicaid Services. More recently she has led a range of data science, analytics and clinical technology initiatives for effective development and deployment of clinical decision support technology.