Imagine a world where 100,000 lives are saved annually by simply adhering to clinical preventive care guidelines. This isn't a futuristic ideal; it's a possibility articulated by the Centers for Disease Control (CDC). Yet, our healthcare reality is riddled with gaps: patients without timely treatment, missed diagnoses during critical early stages, and clinical guidelines that often remain unheeded. When cancer treatment is delayed even by a month, the risk of mortality can rise between 6 to 13%, a peril that escalates with each ticking second, as per research published by the British Medical Journal.
In this blog, we will discuss why early diagnosis and guideline adherence are critical for improving overall care and how the combination of longitudinal patient records and advanced analytics can help in making that happen. You will get insights into why the AI wave is crucial for pharma companies and what role they can play in closing care gaps.
Why adhering to guidelines is challenging for providers
practice guidelines are a starting point for personalized healthcare, often leading to favorable outcomes. However, adherence to clinical guidelines can be challenging due to real-world complexities in routine clinical practice.
We are living in an age in which new drug launches and rapid innovation in the treatment landscape are outpacing updates to treatment guidelines. However, the launch of new drugs every year, based on clinical research, makes it difficult to update the guidelines at the same pace and difficult for clinicians to keep up with the pace of new evidence generation and new treatment launches. For example, 237 novel oncology therapies have been launched in the last 20 years with nearly half in just the last five years.
Frequent changes and the intricacies of these guidelines can make applying them challenging in routine practice. Guidelines from the National Comprehensive Cancer Network (NCCN) for cancer cover recommended management for 97% of patients with cancer and are used by as many as 95% of oncologists in the US. Since the establishment of the NCCN Guidelines, cancer management options have continued to increase in quantity and complexity because of advances in personalized medicine, novel therapeutics, and technology. Based on a JAMA study, there has been an exponential growth in the page volume and references cited over the last two decades, making it difficult for clinicians to deliver optimal care.
Managing the healthcare of patients with multimorbidities, multiple specialists, and medications prescribed by different providers can be quite overwhelming. Physician burnout has reached an all-time high in 2021 at 63%, up from 38% in 2020 as providers may not have access to the resources they need to follow the guidelines, such as electronic health records (EHRs) or clinical decision support (CDS) tools to automate redundant tasks.
The human cost and financial cost of misdiagnosis
Most Americans will experience a diagnostic error at least once in their lifetime, according to PinnacleCare. Patient deaths due to these errors are estimated at 40,000 to 80,000 per year. Diagnostic errors and other inefficiencies cost the U.S. economy $750 billion each year. One of the most critical elements in achieving better patient outcomes is early diagnosis. Missing an early diagnosis can lead to:
The marked emergence of clinical AI algorithms backed by partnerships between digital health, pharma, and health systems is transforming disease detection in key therapeutic areas such as Alzheimer’s, cancer, and heart diseases.
Take for instance a recent study that revealed an AI algorithm's prowess in detecting breast cancer at an impressive 94.5% accuracy, surpassing even seasoned human radiologists. Similarly, in Alzheimer's disease, AI's ability to sift through brain scans has opened doors for predicting disease progression with pinpoint accuracy. This, in turn, empowers healthcare professionals to embark on early interventions and devise personalized treatment plans. In the realm of heart diseases, AI plays a pivotal role in pinpointing patients susceptible to heart failure, leading to improved treatment outcomes, reduced hospital readmissions, and overall healthcare expenses.
Central to this transformation is the necessity for having a unified, longitudinal patient record. This is where the platform steps in. By weaving together data from diverse sources—EHRs, Claims, Lab results, Social Determinants of Health (SDoH), and patient-generated insights—the platform can craft a comprehensive portrait of a patient's health journey over time. With this holistic view of a patient, clinicians can unlock the full potential of such clinical decision tools, ensuring not only accurate diagnosis and guideline adherence but also facilitating a shift from episodic to continuous patient care.
But, longitudinal patient data is not enough to improve care. That’s because while it is possible to analyze data for one patient, it becomes difficult when the number of patients increases to 100 or 1,000. This is where AI can help simplify post-longitudinal patient data adoption processes, such as identifying trends, predicting the next-best action plan, big data analysis, workflow automation, and more.
Yet, for these insights to influence real-time decision-making, integration into clinical workflows is imperative. This is because only when tools and solutions are naturally embedded within a clinician's daily routine can we ensure they are utilized to their fullest potential, ultimately enhancing patient outcomes.
Innovaccer enables first-mile connectivity (data Ingestion, integration & activation) to the last-mile (engagement applications) in solving most complex problems in value-based care. Innovaccer offers a unified data fabric and “intelligence layer” using an Open API framework with third-party integration for 360-degree system administration — all of this through the Best-in-KLAS data platform.
The data platform is interoperable which allows it to input healthcare data from disparate sources such as EHR, claims, SDOH, labs, etc., and create a comprehensive history of the patient. Once unified, the patient data is run through a proprietary suite of value-based care solutions to create actionable insights at the point of care for providers, care managers, and patients.
Innovaccer’s EHR-agnostic solutions have been deployed across more than 1,600 hospitals and clinics in the US, enabling care delivery transformation for more than 96,000 clinicians, and helping providers work collaboratively with payers and life sciences companies. Innovaccer has helped its customers unify health records for more than 54 million people and generate over $1.5 billion in cumulative cost savings. The Innovaccer platform is the #1 rated Best-in-KLAS data and analytics platform by KLAS, and the #1 rated population health technology platform by Black Book. For more information, please visit innovaccer.com.
Schedule a demo to talk to our experts and understand how we are helping pharma companies to identify and seize sustainable revenue opportunities with our healthcare-contextualized technology.