Life sciences companies seek and leverage many forms of intelligence: market intelligence, competitive intelligence, and now patient intelligence. Indeed, patient intelligence is applicable in a number of ways, including:
1. Developing and leveraging risk and impactability scores. A prospective risk score represents a quantified assessment of the future risk of patients based on a variety of factors, such as medication non-compliance, inpatient visits and ER visits. Impactability scores measure how likely patients are to be successful with specific care management interventions. Such knowledge can help life sciences companies understand risk and more successfully participate in value-based contracts with payers. Life sciences companies can also use this patient intelligence to work closely with care providers, counseling them on how to improve patient compliance with treatment regimens and to develop direct messaging that will resonate with patients.
2. Gaining insight into the treatment journey. An in-depth longitudinal analysis of patient-level claims data can uncover the therapies involved in the treatment of a specific medical condition. Such analysis is important to life sciences companies as they shed light on the many treatment methods and paths available. While the first line of therapy for many diseases is typically standardized, the second and third line of treatment can vary. For example, with Type 2 Diabetes, the guidelines typically state the use of biguanides, such as metformin, as the first medicines doctors should prescribe. The second and third lines of treatment, however, become much more complex, where many other treatments such as GLP-1s, DPP-4s, and SGLT2s could potentially be used in combination. Therefore, life sciences companies need to know where their product falls within the line of therapy. Gaining a better understanding of the journey provides valuable insights that could potentially enable life sciences companies to work with providers to have them embrace their products earlier.
3. Targeting undiagnosed patients. Care providers often fail to diagnose patients, even when following best practices. Consider the following: The 2017 National Diabetes Statistics Report by the Centers for Disease Control finds that as of 2015, 30.3 million Americans or 9.4% of the U.S. population had diabetes. Of the 30 million, 7.2 million people were undiagnosed. This represents about 24% of the total diabetes population. Life sciences companies can leverage in-depth patient information and patient personas to identify patients who have yet to be diagnosed with certain conditions1. And, they can then provide valuable treatments, which can improve the health care for many people.
4. Assessing outcomes. There's a number of ways in which outcomes can be measured and analyzed. Specific outcomes can include a patient’s prevalence for co-morbid conditions, including, but not limited to, hypertension, obesity, diabetes, coronary artery disease, depression, congestive heart failure, asthma, rheumatoid arthritis, Parkinson’s disease, and multiple sclerosis. Outcomes can also be measured by an aggregate scoring of per member per month (PMPM) utilization of healthcare service in areas such as utilization of inpatient acute, outpatient surgical, emergency room, outpatient medical, outpatient surgical, evaluation and medical, ancillary and pharmacy. This patient information can then be used to identify care gaps and drive toward desired outcomes. Life science companies can also use patient outcomes data to prove product value and optimally structure value-based contracts with payers and health systems.
These are just a few of the ways that life sciences companies can leverage patient data to move their organizations forward. For a more comprehensive discussion of the importance of patient intelligence, tune in to our webinar, entitled Patient Intelligence: Applying Advanced Analytics to Patient Personas.
1. Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2017. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf