Moving Beyond One-Size-Fits-All Medicine to Data-Driven Insights with Similarity Analytics
Updated: Feb 19
Traditionally, Doctors have been oriented toward diagnosing and treating individual organ systems. Clinical trials and medical research have typically focused on one disease at a time. And today’s treatment guidelines are geared toward treating a “standard” patient with a single illness.
One size does not fit all.
That’s nice… But the real world doesn’t work that way. Most patients do not fit these narrow profiles … especially as we grow older and things get complicated. Patients might display symptoms common to a variety of illnesses, or might already be suffering from multiple diseases. Almost 25% of Medicaid patients have at least five comorbidities.
This might explain why it’s estimated that physicians deviate from the recommended guidelines 40% of the time. It might also explain why there is a real thirst in healthcare for evidence-based insights derived from patient population data.
In other industries, data-driven insights are often the only way organizations work with their customers. Think of retailing and Amazon.com. Amazon analyzes your past purchases, your past clicks and other data to anticipate what you might need and present you with a variety of options all based on data-driven insights. You might think that by now, every industry would analyze data from the past to predict the future.
That’s not true in healthcare where treating complex patients can be challenging and technology to handle this level of complexity really hasn’t existed. Treatment guidelines are sometimes vague and may not exist at all when a patient has multiple diseases or is at risk for developing them. In other words, one-size-fits-all approaches tend to be self-limiting.
Treating patients with multiple conditions is also costly. In fact, 76% of all Medicare expenditures apply to patients with five or more chronic conditions. To reduce costs, doctors need ways to identify early intervention opportunities that address not only the primary disease but also any additional conditions that a patient might develop.
Consequently, Doctors are forced to adopt ad hoc strategies that include relying on their own personal experiences (and knowledge) among other approaches. Straying from those guidelines (where available) might not deliver the best outcomes but it’s been the only option they have … until now.
Similarity analytics offers a way to augment traditional treatment guidelines, enabling healthcare providers to use individual patient data (including both structured and unstructured data) as well as insights from a similar patient population to enhance clinical decision-making. With similarity analytics, healthcare providers and payers can move beyond a one-size-fits-all approach to deliver data-driven, personalized care that helps improve outcomes, increase the quality of care and reduce costs.
IBM similarity analytics capabilities, developed by IBM Research, play an essential role in IBM Patient Care and Insights … a comprehensive healthcare solution that provides a range of advanced analytics capabilities to support patient-centered care processes. Here is a link to a video (with yours truly) from the recent launch in Las Vegas (my part starts at 8:45 mins).
How do similarity analytics capabilities work?
Let’s take an elderly patient with diabetes (a chronic disease) who presents with ankle swelling, dyspnea (difficulty breathing) and rales (a rattling sound heard during examination with a stethoscope). Diabetes by itself is bad enough … but the care process gets more complicated (and more costly) when other comorbid conditions are present.
With these reported symptoms and observed signs, the patient might be at risk for other chronic diseases such as congestive heart failure. But exactly how much at risk and when?
In the past, Doctors have had no way of knowing this. There are tens of thousands of possible dimensions that need to be understood, analyzed and compared to get an answer to this question. Think of a spreadsheet where the patient is a single row … and in that spreadsheet, there are 30,000 columns of data that need to be analyzed in an instant … and someone’s life could be at stake based on the outcome of the analysis. In other words, Doctors have been handicapped in their ability to deliver quality care because of the absence of this type of analysis.
IBM Patient Care and Insights
With IBM Patient Care and Insights (IPCI), a healthcare organization can collect and integrate a broad range of patient data from electronic medical records systems and other data sources (such as claims, socioeconomic and operational) … from past test results to clinical notes … into a single, longitudinal record. Similarity analytics then enables the provider to draw on this comprehensive collection of data to compare the patient with other patients in a larger population. With IBM Similarity Analytics (part of IPCI), the provider can analyze tens of thousands of possible comparison points to find similar patients … those patients with the most similar clinical traits at the same point in their disease progression as the patient in question.
Why is finding similar patients helpful?
First, providers can see what primary diagnoses and treatments have been applied to similar patients … some diagnoses and treatments might have otherwise eluded Doctors. Second, providers (and payers) can identify hidden intervention opportunities … such as an illness that the patient is at risk of developing or the risk of the patient’s current condition deteriorating. Surfacing hidden intervention opportunities is critical in addressing the costs and complexity of healthcare … especially when treating patients with multiple diseases.
Importantly, providers can also predict potential outcomes for an individual patient based on the outcomes of similar patients. Knowing what has happened to a patient’s peer group given certain treatments can help doctors hone in on the right intervention for this particular patient … before things take a turn for the worse.
There are many areas where similarity analytics are helpful. Disease onset prediction, readmissions prevention, physician matching, resource utilization and management and drug treatment efficacy are just a few of the use cases. My colleagues in IBM Research have been working on this technology for years.
By finding similar patients, pinpointing risks and helping to predict results, similarity analytics can ultimately help healthcare providers and payers improve the quality of care and deliver better outcomes, even for patients with multiple illnesses. By working with other analytics capabilities to enable providers to apply the right interventions earlier, similarity analytics can also help pinpoint the specific risk factors for a given patient. Those risk factors can become the basis for an individualized care plan.
In a future blog post, I’ll focus on the care management capabilities of IBM Patient Care and Insights so you can see how this solution helps put analytics insights into action.
As always … look forward to reading your comments and questions.
 Projection of Chronic Illness Prevalence and Cost Inflation from RAND Health, October 2000.
 KE Thorpe and DH Howard, “The rise in spending among Medicare beneficiaries: the role of chronic disease prevalence and changes in treatment intensity,” <link: http://content.healthaffairs.org/content/25/5/w378.full> Health Affairs 25:5 (2006): 378–388.