With the development of artificial intelligence and the capture of well-curated digital information, analytics has the potential to improve clinical decision-making. This tool could eventually embody a data-driven point of view, assisting with the overall treatment strategy for a patient.
Currently, healthcare analytics can help providers further explore the ways in which they care for their patients. Care management programs and integrated patient transitions represent budding methods to better coordinate services.
This program for Medicare beneficiaries helps patients collect their vitals for continuous observation.
Its influence has helped enable the cultural expansion of wearable health devices and general home health therapies. RPM has helped bring providers closer to their patients, in their daily lives, allowing for more seamless primary care.
Pairing RPM with an analytics platform can contribute to the long-term management of chronic conditions.
Here at ThoroughCare, we’ve worked with more than 600 clinics and physician practices on intuitive software solutions. We’ve helped many practices establish and scale RPM programs and improve data reporting.
In this article, we’ll cover how RPM and healthcare analytics can help recognize patterns in patient data.
Recognizing Patterns in Patient Data
The intended function of RPM devices, such as a weight scale or pulse meter, help determine the vitals a patient will track. Device usage is usually aligned to the underlying conditions that influence chronic diseases, especially when a patient is co-enrolled in Chronic Care Management (CCM).
For providers, patient data collected through RPM can help identify trends that can aid conversations and interventions. This information can then support more personalized, proactive care.
Below, we’ll cover two examples of health data that can be tracked and visualized with the help of a data analytics platform.
1. Changes in Blood Pressure
A blood pressure cuff can provide a valuable point of data.
This vital sign is “the strongest evidence for causation” of cardiovascular disease, according to the American Heart Association. Diet and exercise can play a pivotal role in preventing such an outcome, along with additional interventions, such as medication.
Data analytics can help identify patient blood pressure trends, as well as correlate this information to other indicators of cardiovascular health. It can do so by “visualizing” such information through intuitive graphics or charts, and developing these resources as more data becomes available.
According to a study published in BMC Medical Informatics and Decision Making, visualizing a patient’s blood pressure can help tie clinical workflows to actionable insights. The study claims that “data visualization helps both patients and physicians to have a fuller understanding of the blood pressure ‘story’ and” enables more active patient engagement.
Elevated engagement could be the key point of value for analyzing blood pressure data.
On the other hand, rapid weight loss can indicate other difficult complications, such as cancer or Crohn's disease.
Either way, weight trends can offer valuable context for care decisions, or they can represent an exact diagnosis, such as obesity.
Analytics can help efficiently collate and visualize this information on a patient-by-patient basis. This insight can then be used to align care goals and develop interventions or lifestyle changes. Maintaining consistent observation of this vital can be a useful indicator for medical decision-making over the course of a patient’s life, especially if a patient’s weight tends to fluctuate or could further complicate existing chronic conditions.
In the case of obesity, remote vital tracking could influence patient engagement, though evidence suggests that societal changes, as well as improvements to health systems, are required to make lasting impacts.
Still, healthcare analytics can support such changes by helping derive insights to inform intelligent decisions.
Healthcare Analytics for Clinical Efficiency
It’s been estimated that a staggering $4 trillion is spent per year on healthcare in the U.S. Roughly 25% of that is linked to administrative costs. Simplifying this aspect of care delivery could significantly reduce overall spending, according to McKinsey & Company. And healthcare data can provide clarity and help focus these efforts.
A healthcare analytics solution can help practices filter through massive amounts of information and gain insights into clinical processes. With this data, it’s possible to pinpoint inefficiencies and bottlenecks and develop solutions that address them.