The steady growth of medical data is outpacing many health providers&#8217; ability to make use of it. Data mining and analytics tools previously used for commercial data are being applied to medical data in various forms. In 2020, for instance, experts from Sisense joined forces with the G-Med online community to create the <a href="https://aws.amazon.com/blogs/apn/using-amazon-comprehend-medical-sisense-and-g-med-to-create-an-ai-enhanced-knowledge-base-to-combat-covid-19/" target="_blank" rel="noreferrer noopener nofollow">Medin’Sight app</a> to help fight COVID-19, and Sisense client GeriMedica began infusing analytics insights into practitioner workflows to improve elder care.  


Embedded patient analytics have huge potential in medical data applications, because actionable intelligence from analyzed data is surfaced to users more or less automatically, without interrupting medical professionals’ workflows. The right insight, presented to the right professional at the right time, can be vital in helping achieve better patient outcomes. 


Companies building software for medical professionals can greatly enhance their offerings by buying a robust analytics platform and embedding it in their core product. Providing increased access to more specific data on patients, combined with data from a variety of other sources, can help predict potential problems, recommend novel treatments, and streamline treatment. 


&gt;&gt;&gt; Infusing analytics into apps and workflows seems daunting. Simplify it.


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<h2 class="wp-block-heading">Embedded patient analytics analyze medical data from many sources in one place</h2>


One of the strengths of modern medical analytics implementations is healthcare data integration — the ability to connect and draw conclusions from data derived from many streams. Healthcare practitioners need all the context they can get to provide the best care, so creating insights based on data from a wide array of sources is key. Software builders wishing to connect to multiple datasets would do well to investigate third-party analytics platforms instead of building from scratch, as analytics projects can quickly spiral out of control when multiple data sources come into play.


The data these healthcare analytics systems handle comes from an ever-growing range of devices. Current systems already link electronic medical record and electronic health record platforms, wearable devices, and countless other sources. As a result of this healthcare data integration, the sheer volume of medical data available for analytics processing can be too much for in-house coders to handle when attempting to build analytics from scratch. 


Embedding a third-party analytics platform into user-facing systems puts actionable intelligence gleaned from all these systems in one place, at the right moment to help practitioners make better decisions. If different patient care analytics systems served up insights from each of these standalone data sources in their own programs, the time required to switch back and forth from the primary program to different dashboards would slow care workflows and discourage teams from actually using them. <a href="https://www.sisense.com/blog/ai-for-all-augmented-analytics-infused-everywhere/" target="_blank" rel="noreferrer noopener">Actionable intelligence</a> needs to be present where, when, and how users can make use of it — and that’s a job for embedded healthcare analytics.


<h3 class="wp-block-heading">Choose a scalable analytics platform to tackle data growth</h3>


The volume and the complexity of social research data, biomedical data, and simple healthcare readouts doubles every 12 to 14 months. This data is also coming from more sources every year: A recent global study shows that some 18,000 of 20,000 surveyed mobile medical apps harvested user data. In 2015, there were about 24,000 mobile health apps on the Google Play store. By the first quarter of 2021, that number had swelled to more than 50,000. 


<figure class="wp-block-image size-full fancybox"><img decoding="async" class="wp-image-130587" src="https://cdn.sisense.com/wp-content/uploads/embedded-analytics-patient-care-august-2021-inline-770x000-1.png" alt="The Case for More Embedded Analytics in Patient Care" /></figure>


Medical data amounts and sources are only going to increase, as will practitioner reliance on insights from analyzed data. Medical software providers attempting to build analytics from scratch may want to instead embed analytics from a third party to handle the increasing data volumes and number of sources. Practitioners need access to actionable intelligence from these growing datasets and sources to deliver better care, and many inexperienced analytics-builders are not prepared for the challenges that scaling to handle these immense amounts of data present.


Embedding third-party analytics into user-facing systems puts the task of connecting to these sources on the back of the analytics provider, which is uniquely well-suited to handle it, then surfaces those insights in the format that users are most familiar with.


<h2 class="wp-block-heading">Better access to analyzed data improves patient outcomes </h2>


But is more data actually better data? The promise of modern healthcare data integration is that as more data is captured, combined, and analyzed, clinicians will be served actionable intelligence that fits their exact needs, empowering them to do their jobs better and more easily. Users working with aging populations can see analytics drawn from a user’s individual information combined with demographic data to spot ailments of aging they may be susceptible to. Practitioners dealing with heart health may want to see what other medications a patient is on, mashed up with percentages of harmful interactions drawn from demographic data and their own in-house records.


Sisense client <a href="https://www.sisense.com/case-studies/glytec/" target="_blank" rel="noreferrer noopener">Glytec</a> analyzed vast amounts of hospital data around glucose monitoring for diabetes patients and was able to prove to practitioners that personalized care programs could be developed to reduce diabetes-related complications and reduce overall patient contact during the COVID-19 pandemic. <a href="https://www.sisense.com/case-studies/luma-health/" target="_blank" rel="noreferrer noopener">Luma Health’s</a> deep dive into patient-provider text data was able to help streamline communications and automate some aspects of patient scheduling, freeing up healthcare professionals to focus on more challenging aspects of their roles.


These are just a few ways patient analytics are already improving healthcare practitioner workflows and patient experiences and outcomes. Actionable intelligence presented directly in user workflows can do much more, showing clinicians the patient’s risks for specific health conditions without them having to ask, suggesting treatments, and revealing the relative risks and potential complications of different options. As more data and more types of data are analyzed, healthcare professionals will become better equipped to help patients get ahead of possible ailments. 


<h2 class="wp-block-heading">Unified analytics interfaces streamline clinician workflows</h2>


One of the greatest strengths of embedding analytics directly in user workflows is that all the data that practitioners need will be present in a unified format from a single source. Clinicians will not have to seek out specific permissions for each data source, nor will they have to comply with several virtually identical but parallel data protection compliance requirements. Record-keeping and quality control audits will be both more straightforward and more reliable. 


Additionally, because much of the data being collected from new sources (medical wearables, health apps, etc.) is specifically being gathered for healthcare purposes with the patient’s buy-in, many ethical barriers to gathering and using the data can be circumvented. Regardless of where the data comes from, regulatory considerations present another strong reason for buying rather than building analytics from scratch: Industry-leading platforms are designed to handle sensitive and secure data and have access and governance protocols designed to show only the right information to the right user at the right time and place.


<h2 class="wp-block-heading">Actionable intelligence boosts decision-making</h2>


Embedding patient analytics into healthcare software helps generate actionable intelligence to augment human decision-making. Modern, analytics-enhanced medical care can help improve risk analysis, add new dimensions to a clinician’s traditional expertise, and surface insights from a patient’s case history. 


Being assisted with obvious tasks that require no judgment (handled by machine learning algorithms) means clinicians also experience less “<a href="https://www.sisense.com/blog/cmos-its-time-to-embrace-infusing-analytics-into-workflows/" target="_blank" rel="noreferrer noopener">decision fatigue</a>.” This means when they are called upon to make medical judgments, they’re at their best, not overtaxed from swatting away an array of other issues. Quality embedded medical analytics deliver up better care, better workflows for healthcare professionals, and better outcomes for everyone. 


&gt;&gt;&gt; Infusing analytics into apps and workflows seems daunting. Simplify it.


<a class="action-btn " href="https://www.sisense.com/whitepapers/three-steps-to-infusing-analytics/" target="_blank" rel="noopener noreferrer">Learn the steps</a>


Matt Madden, Sisense’s Senior Director, Go to Market, has over 20 years of experience in the data and analytics market. He’s held roles in sales and marketing, all with the goal of helping organizations make better decisions with their data.

The Case for More Embedded Analytics in Patient Care

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