The healthcare industry is currently experiencing a significant big-data revolution that holds the potential to transform the way healthcare is delivered. This transformation is driven by a substantial increase in the availability of data. Over the past decade, pharmaceutical companies have amassed extensive research and development data into medical databases. Concurrently, payors and healthcare providers have digitized patient records. Public stakeholders, including the US federal government, have also contributed to this wealth of healthcare knowledge by sharing data from clinical trials and information about patients covered under public insurance programs.
These developments have been complemented by recent technological advancements, making it easier to collect and analyze data from various sources. This capability is especially valuable in healthcare, where patient data can originate from multiple payors, hospitals, laboratories, and physician offices.
The primary driver behind the demand for big-data applications in healthcare is the pressing need to address fiscal concerns. Healthcare expenses in the United States have steadily increased over the past two decades, now representing a substantial 17.6 percent of the GDP, exceeding the expected benchmark for a nation of similar size and wealth by nearly $600 billion. In response, many payors have transitioned from fee-for-service compensation, which incentivizes treatment volume, to risk-sharing arrangements that prioritize outcomes. In this evolving landscape, healthcare stakeholders have a greater motivation to compile and exchange information.
While cost containment is a critical driver behind the adoption of big data in healthcare, clinical trends have also played a pivotal role. Traditionally, physicians relied on their clinical judgment when making treatment decisions. However, recent years have seen a shift towards evidence-based medicine, which involves systematically reviewing clinical data to make treatment decisions based on the best available information. Aggregating individual data sets into big-data algorithms often provides more robust evidence, particularly for rare nuances in subpopulations.
Despite the significant potential of big data in healthcare, the industry has been slower to adopt these technologies compared to sectors like retail and banking. Concerns about patient confidentiality, among other issues, have been significant barriers. However, it appears that the healthcare sector is now poised to catch up. Early adopters in healthcare data analytics are already experiencing favorable outcomes, motivating other stakeholders to take action.
Nonetheless, this progress raises an important question: Is the healthcare industry fully prepared to harness the complete potential of big data, or are there impediments that might hinder its use?
Big data undeniably holds immense value in healthcare. However, traditional tools and methodologies may not fully exploit its potential. Conventional healthcare value levers, such as unit-price discounts, are primarily based on contracting and negotiation. Most established value levers focus on cost reduction rather than improving patient outcomes. While these tools remain essential, stakeholders will only realize the full potential of big data by adopting a more holistic, patient-centered approach to value, emphasizing both healthcare spending and treatment outcomes.
To help healthcare stakeholders redefine value and identify appropriate tools for this new era, we have developed five key pathways. These pathways focus on essential concepts:
- Right Living: Encouraging patients to play an active role in their health by making informed choices about diet, exercise, preventive care, and other lifestyle factors.
- Right Care: Ensuring patients receive timely and appropriate treatment, emphasizing a coordinated approach where all caregivers have access to the same information and work towards common goals to avoid duplication and suboptimal treatment strategies.
- Right Provider: Ensuring that all healthcare professionals treating patients have strong performance records and can achieve the best outcomes, selecting them based on skill sets and abilities rather than job titles alone.
- Right Value: Continuously seeking ways to enhance value while preserving or improving healthcare quality. This could involve developing a system in which provider reimbursement is tied to patient outcomes or implementing programs to eliminate wasteful spending.
- Right Innovation: Focusing on identifying new therapies and healthcare delivery approaches while simultaneously enhancing the innovation process itself, such as advancing medical research and boosting research and development productivity.
These value pathways remain adaptable, evolving as new data becomes available. Consequently, they foster a feedback loop within the healthcare system. For example, the concept of “right care” might change if new data suggests that the standard treatment protocol for a particular disease does not yield optimal results. Changes in one pathway can also influence others since they are interconnected.
The interest in big data extends beyond traditional healthcare players. Since 2010, over 200 new businesses have emerged, specializing in innovative healthcare applications. A substantial portion of these applications focuses on direct health interventions or predictive capabilities, marking a new frontier for health data applications. These innovations move beyond retroactive reporting to interventions and predictive analytics.
Several innovative devices take patient monitoring to new heights. For instance, one company has developed a GPS-enabled tracker for asthmatics to record inhaler usage. The collected data is analyzed to identify trends at the individual, group, and population levels. This data is then combined with information about asthma catalysts, aiding physicians in developing personalized treatment plans.
Another company offers a mobile application that allows patients with specific conditions to be tracked through their smartphones. The application records data about calls, texts, geographic location, and physical movements, supplementing it with survey responses. These insights, combined with public research on behavioral health, enable personalized behavioral health therapies.
The potential impact of these new value pathways in healthcare is substantial. By evaluating various healthcare initiatives, we assessed their potential annual cost savings, assuming that outcomes remain constant, based on a 2011 baseline. Scaling up these early successes to achieve systemwide impact could result in annual cost reductions ranging from $300 billion to $450 billion. This represents 12 to 17 percent of the $2.6 trillion baseline of US healthcare costs.
Even seemingly simple interventions can have a significant impact when expanded to a broader scale. For instance, promoting aspirin use, early cholesterol screening, and smoking cessation for individuals at risk of coronary heart disease could collectively reduce total care costs by over $30 billion. Big data enables the rapid identification of high-risk patients, more effective interventions, and improved monitoring, enhancing the effectiveness of these measures.
It’s important to emphasize that our estimate of $300 billion to $450 billion in reduced healthcare spending may be conservative. The potential for insights and innovations in healthcare remains vast, with many opportunities on the horizon. There is still much to learn about the efficacy of cancer therapies in specific subpopulations and predictive indicators of relapse, among other areas. The ongoing big-data revolution is expected to unveil numerous learning opportunities in these domains.
However, realizing the full potential of big data in healthcare is not without challenges and caveats. Several structural issues may present obstacles. The shift away from fee-for-service care, already underway, must continue. Traditional medical management practices must evolve, as they often create misaligned incentives among payors and providers.
Additionally, stakeholders must acknowledge the value of big data and be prepared to act on its insights. This requires a fundamental mindset shift, which may be challenging for many. Patients will not benefit from research on exercise if they continue with sedentary lifestyles, and physicians may not improve patient outcomes if they resist treatment protocols based on big data in favor of their clinical judgment.
Privacy remains a paramount concern. While modern computer programs can effectively de-identify records before transferring them into large databases, vigilance is required as more information becomes publicly accessible.
Lastly, the healthcare sector should learn from previous data-driven revolutions. Some players have exploited data transparency for self-serving objectives, and similar behavior may manifest in the healthcare sector. For example, providers of medical imaging services could use big data solely to identify underserved patients and disease areas, leading to unnecessary procedures and increased costs without corresponding benefits.
In conclusion, the potential for big data to transform healthcare is undeniable. Stakeholders who are committed to innovation, willing to build their capabilities, and open to new value paradigms are likely to be the first to reap the rewards of big data. This transformation promises not only to reduce costs but also to improve patient outcomes, marking a significant step toward a more efficient and effective healthcare system.