In the media

How to optimize data-driven technologies to improve care delivery

Jenna Phillips

By Jenna Phillips

Health Data Management

15 February 2023

Health systems and provider networks are looking for ways to intelligently deploy a variety of smart technologies to reduce costs and support care.

This article was first published in Health Data Management.

Over the last 40 years, healthcare spending per capita quadrupled – it’s the result of an aging population, increasing rates of chronic disease diagnoses and medical innovations that are driving up the cost of care and altering how it is delivered.

These rising costs are putting health systems and provider networks under pressure to find smarter, faster and clinically validated methods to deliver cost-effective, quality care.

Digital tools and technologies are proving to be a viable option for health systems and providers to augment their existing capabilities and manage costs, and data is being used to unearth insights that advance clinical practices and to develop predictive analytic tools that enhance care delivery.

Tools in the market today, such as smart risk simulators and indices, run patient data through an algorithm to yield a quantitative value related to the patient’s risk or the likelihood of a potential outcome. They can be developed using a system’s own clinical and operational data to identify efficiencies across several domains and use cases, or by participating in and accessing a clinical data registry that can provide an even larger sample of data from similar health systems to:

Here are a few examples of how these data-driven tools are used today in efforts to raise efficiency and conserve resources, as well as how healthcare organizations can act to leverage existing data assets to support the delivery of excellent care.

Managing capacity challenges

Variable and unpredictable capacity challenges in emergency departments and acute care settings can be addressed by taking a data-driven approach. In these settings, sustaining a consistent patient flow and throughput rate is imperative to operational efficiency and patient satisfaction.

Smart risk simulators can support these objectives by functioning as a digital triaging tool to move patients through the appropriate clinical pathways faster. These technologies consume a patient’s data to use fewer human and clinical resources to determine the patient’s appropriate clinical needs and risks, even providing screenings or diagnoses or pointing toward other recommended assessments that result in more informed clinical decisions.

For instance, algorithms can predict the likelihood that a patient will test positive for influenza without the need for administering a PCR test.

In most cases, simulators and algorithms can be developed with an organization’s own data or from a relevant clinical data registry, supporting accuracy in screening or diagnostic result. Moving patients through clinical pathways more efficiently and with minimized resource consumption can help drive down the cost of care, reduce the risk of medical error and optimize efficiencies in care delivery across clinical settings.

Indexing and risk monitoring

Smart indices are becoming increasingly common in acute care settings to predict adverse events or high-risk scenarios during critical periods, such as postoperative and critical care, in which they can be used to notify clinicians of the risk of adverse events to give more time to deliver a required intervention.

Risk assessment calculators and tools also can be used in high-volume settings to rule out the need for costly diagnostics, providing clinically validated, quantitative endpoints to inform decision making.

Leveraging data-driven tools and technologies

Here are important elements to taking advantage of these emerging capabilities to better support care delivery.

Identify the clinical and operational needs of the system or network. Health systems and provider networks are under pressure to significantly enhance clinical workflows to ensure timely care decisions, judicious resource allocation and improved outcomes. Systems and networks need to identify waste in clinical and operational processes, and that process can be supplemented by a risk simulator or similar technology.

Pairing a clinical or operational need, such as expeditious COVID-19 screening in emergency department settings or pre-arrival triaging in the patient portal, with the appropriate technology can help organizations realize the cost and efficiency benefits of leveraging these technologies.

The technology must be fit-for-purpose to maximize the return. To expand, a smart simulator that rules out the need for an influenza PCR test could see greater use and a higher return on investment at a high-volume acute care facility than at a rural local-access hospital, at which the number of annual screenings is significant lower.

Similarly, a rural local-access hospital could benefit from using smart indices or risk assessment calculators to provide real-time monitoring and alerts in acute-care settings, where there could be a deficit or shortage of specialized and qualified clinicians.

Engage the right staff and experts. The human insight component of any data-driven initiative can prove to be a significant differentiator when using these tools.

Many intelligent simulators and algorithms leverage artificial intelligence or machine learning applications that require clinical, operational and technical expertise. Drawing on strengths from each domain, these interdisciplinary teams are poised to successfully design, develop, implement and operationalize these digital tools.

Health system and provider network leaders should identify experts to join their teams or provide consultancy-based support to help them identify the most crucial use cases and develop visualizations and analytical approaches that can immediately add value by making clinical pathways more efficient.

For example, sourcing the right talent to democratize an organization’s data (for example, enabling access to approved clinical and operational data to all employees and providing education on how to interrogate it) can lead to front-line clinicians developing personalized dashboards or unique reports that optimize care-delivery procedures or processes in their unique domains and populations.

Similarly, enhancing an organizations digital and data literacy through talent augmentation can propagate the democratization of data to accelerate adoption of digital health tools and technologies among front line clinicians.

Clinical data registry participation. Healthcare associations and clinical data registries are positioned to spearhead the development and deployment of these technologies. Registries bring together interdisciplinary experts from medicine, healthcare quality, data science and technology, as well as representing the medical specialty’s interests overall.

Often, healthcare associations also establish, validate and set clinical practice guidelines for their specialties, augmenting credibility, leveraging their relationships to scale up and deploy novel technologies to health systems and healthcare stakeholders.

Registries also can support health systems and provider networks to facilitate healthcare quality reporting, benchmarking and other data-driven activities that give visibility on clinical guidelines in real time and reduce administrative and operational burdens on clinicians.

Advancing proactive care

Health system and provider network leaders need to shift care delivery standards from reactive to proactive. Smart technologies can support systems and provider networks transitioning to a proactive approach by providing timely, predictive, and actional insight based on a patient’s own data.

Enhancements in capacity and clinical pathways can control costs by conserving resources, expediting clinical decision making, and preventing adverse events linked to higher costs of care. The long-term capacity and capability augmentation will lower costs and ensure healthcare organizations can consistently meet quality benchmarks, leading to improved patient outcomes and increased patient and clinician satisfaction.

Instead of using data as a diagnostic resource, health systems and provider networks should view data as a proactive and prescriptive tool that minimizes costly care and conserves valuable resources.

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