Around the world, every health care system is struggling with rising costs and uneven quality despite the hard work of well-intentioned, well-trained clinicians. Health care leaders and policy makers have tried countless incremental fixes—attacking fraud, reducing errors, enforcing practice guidelines, making patients better “consumers,” implementing electronic medical records—but none have had much impact.
It is time for a fundamentally new strategy. At its core is maximizing value for patients: that is, achieving the best outcomes at the lowest cost. We must move away from a supply-driven health care system organized around what physicians do and toward a patient-centered system organized around what patients need. We must shift the focus from the volume and profitability of services provided—physician visits, hospitalizations, procedures, and tests—to the patient outcomes achieved. And we must replace today’s fragmented system, in which every local provider offers a full range of services, with a system in which services for particular medical conditions are concentrated in health-delivery organizations and in the right locations to deliver high-value care. Making this transformation is not a single step but an overarching strategy. It will require restructuring how health care delivery is organized, measured, and executed.
Healthcare Data Analytics is one such strategy that offers ways to change our collective mindset about healthcare systems, enabling us to improve performance that is otherwise stagnant, argues Ankesh Arora, CEO of A-Square Group.
Data analytics is the process of examining raw datasets to find trends, draw conclusions and identify the potential for improvement. Health care analytics uses current and historical data to gain insights, macro and micro, and support decision-making at both the patient and business level. The use of health data analytics allows for improvements to patient care, faster and more accurate diagnoses, preventive measures, more personalized treatment and more informed decision-making. At the business level, it can lower costs, simplify internal operations and more.
The collection of data in health care settings has become more streamlined in recent years. Not only does the data help improve day-to-day operations and better patient care, it can now be better used in predictive modeling. Instead of just looking at historical information or current information, we can use both datasets to track trends and make predictions. We are now able to take preventive measures and track the outcomes.
There is a growing demand for patient-centric, or value-based, medical care which has led to a considerable shift towards predictive and preventive measures in regard to public health in recent years. Data makes this possible. Instead of simply treating the symptoms as they present, practitioners can identify patients at high risk of developing chronic illnesses and help treat an issue before it surfaces. This helps to lower costs for the practitioner, insurance company and patient as the preventive treatment may help to stave off long-term issues and expensive hospitalizations. If hospitalization is necessary, data analytics can help practitioners predict risks of infection, deterioration and readmission. This too can help lower costs and improve patient care outcomes.
COVID-19 has further accelerated the rate of healthcare transformation. Overnight our children, ages 5-24, from kindergarten to graduate school have become the Zoom-Generation. This is also becoming the “Zoom-Generation” of healthcare (albeit not exclusively through the Zoom platform). As such, advancing technology will be the vigorous driver behind a much needed refocusing of healthcare delivery to put the patient experience and navigation of health services back where it belongs: front and center.
The outbreak has also deeply tested the adaptation capacity and the resilience of our health care system. COVID-19 caused stress on bed capacity, equipment, and health care personnel in public hospitals in ways not previously experienced. How can health systems prepare to care for a large influx of patients with future outbreaks? A-Square Group focuses on data analytics and artificial intelligence in: (i) developing a strategy for patient volume and complexity; (ii) protecting and supporting heath care workers on the front lines; (iii) defining a strategy to allocate health care resources; and (iv) developing a robust, transparent, and open communication policy.
All in all, the transformation to a value-based data-centric health care is well under way. Some organizations are still at the stage of pilots and initiatives in individual practice areas. Other organizations, such as A-Square Group, have undertaken large-scale changes involving multiple components of data management, lean six-sigma and healthcare process improvement.
Rapid improvement in any field requires measuring results—the guiding principle applied by A-Square Group across numerous hospitals in managing healthcare processes. Teams improve and excel by tracking progress over time and comparing their performance to that of peers inside and outside their organization. Indeed, rigorous measurement of value (outcomes and costs) is perhaps the single most important step in improving health care. Wherever we see systematic measurement of results in health care—no matter what the country—we see those results improve.
There is no longer any doubt about how to increase the value of care. The question is, which organizations will lead the way and how quickly can others follow? The challenge of becoming a value-based data-centric organization should not be underestimated, given the entrenched interests and practices of many decades.
What we can expect—at least in the near-term future—is more digital transformation, more cloud, more integration, more automation, and overall a more coherent, consistent, and comprehensive delivery of healthcare.