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How can HIT data analysis improve chronic condition outcomes?

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Chronic conditions, such as diabetes, heart disease, and arthritis, affect millions of people and pose significant challenges for health care systems. Managing these conditions requires ongoing monitoring, treatment, and education to prevent complications and improve quality of life. How can health information technology (HIT) data analysis help in this process? In this article, we will explore some of the ways that HIT data analysis can improve chronic condition outcomes.

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Andy Dé

Transformational Chief Marketing Officer (CMO) I Healthcare & Life Sciences AI+Analytics Innovation Visionary…

1 Data collection and integration

One of the first steps in using HIT data analysis for chronic condition management is to collect and integrate data from various sources, such as electronic health records (EHRs), patient-generated health data (PGHD), remote monitoring devices, and social determinants of health (SDOH). These data can provide a comprehensive and holistic view of the patient's condition, needs, preferences, and goals. By using standardized and interoperable data formats, such as FHIR, HIT data analysis can facilitate data sharing and collaboration among different health care providers, settings, and systems.

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Andy Dé

Transformational Chief Marketing Officer (CMO) I Healthcare & Life Sciences AI+Analytics Innovation Visionary, Evangelist, Thought+Change Leader & Board Advisor, Impacting $ Bn in Enterprise SaaS Revenue I Forbes Council
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Current advances for data collection for chronic diseases like Diabetes include Continuous Glucose Monitoring (CGM) which are now being integrated with Artificial Intelligence (AI) especially Machine Learning, Natural Language Generation (NLG) and Natural Language Processing (NLP), which not only monitor glucose levels in the blood on a 24x7 basis, but apply predictive analytics to trigger alerts via text messages letting the patient know his/her glucose levels are approaching high or low thresholds, enabling them to proactively take their doctor prescribed medication or insulin as needed, thereby minimizing the occurrence of unforeseen or adverse events with debilitating impact on the health of the patient.

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Ankur Bhargava, MD, MPH, MScBMI

Addiction Medicine/Preventive Medicine/Clinical Informatics
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Social determinants of health data cannot be relegated to second string in these processes. Arguably it is more important than physiologic data because it sets up the environment that makes conditions chronic. Using diabetes as our running example, all the physiological data and blood sugar patterns and A1c information in the world is, in my opinion, not useful if someone doesn’t have the right tools to make wisdom from this information. If my glucose monitor tells me that I’m spiking a high after every meal - that’s one thing. But if you pair that information with the fact that the nearest grocery store is a 4 hour round trip on public transportation, well, there’s a better way to solve the problem other than throwing drugs at it!

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2 Data visualization and reporting

Another way that HIT data analysis can improve chronic condition outcomes is by using data visualization and reporting tools, such as dashboards, charts, graphs, and maps, to present data in a clear and meaningful way. Data visualization and reporting can help health care professionals and patients to understand the current status, trends, patterns, and gaps in chronic condition management. For example, data visualization and reporting can show how well a patient is adhering to their medication regimen, how their blood glucose levels are changing over time, or how their social and environmental factors are affecting their health outcomes.

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Andy Dé

Transformational Chief Marketing Officer (CMO) I Healthcare & Life Sciences AI+Analytics Innovation Visionary, Evangelist, Thought+Change Leader & Board Advisor, Impacting $ Bn in Enterprise SaaS Revenue I Forbes Council
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Data visualization and reporting is key to monitoring, measuring, analyzing and managing/ improving outcomes for chronic disease patients. For instance, most continuous glucose monitoring (CGM) systems provide free apps that work on your smartphone of choice and capture glucose levels, plot these to reveal patterns and then provide alerts to take appropriate action such as taking your medication, insulin or food to ensure that you are within the stipulated range, which ultimately helps lower your A1C levels. I am currently using the FreeStyle Libre CGM system from Abbott Labs which comes with a 14 day sensor I wear on my arm, that instantly tells me my glucose level and also charts it on a daily, weekly, and monthly basis.

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3 Data analytics and decision support

A third way that HIT data analysis can improve chronic condition outcomes is by using data analytics and decision support techniques, such as artificial intelligence, machine learning, natural language processing, and clinical decision support systems, to generate insights and recommendations from data. Data analytics and decision support can help health care professionals and patients to identify risks, opportunities, and best practices for chronic condition management. For example, data analytics and decision support can predict the likelihood of a patient developing a complication, suggest the optimal treatment plan based on evidence and guidelines, or provide personalized and timely feedback and education to the patient.

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Andy Dé

Transformational Chief Marketing Officer (CMO) I Healthcare & Life Sciences AI+Analytics Innovation Visionary, Evangelist, Thought+Change Leader & Board Advisor, Impacting $ Bn in Enterprise SaaS Revenue I Forbes Council
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Data analytics and patient decision support (PDS) is key to altering behavior. Here is a compelling example in a Diabetes management context: Medtronic has also partnered with IBM Watson Health to leverage Watson’s AI capabilities and integrate this with their Guardian CGM system, thru an AI enabled app called Sugar.IQ. Sugar.IQ, the “personal diabetes assistant” continually analyzes how an individual’s glucose level responds to food intake, insulin dosages, daily routines, and other information provided by the app user. With the Sugar.IQ? app, people with diabetes can uncover patterns and insights that affect their glucose levels, which can give them information throughout the day to help stay on track, and maintain healthy glucose levels.

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4 Data quality and security

A fourth way that HIT data analysis can improve chronic condition outcomes is by ensuring data quality and security throughout the data lifecycle. Data quality and security are essential for ensuring the accuracy, reliability, validity, and confidentiality of data. By using data quality and security measures, such as data validation, cleansing, encryption, authentication, and authorization, HIT data analysis can prevent data errors, inconsistencies, breaches, and misuse that could compromise the quality and safety of chronic condition management.

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5 Data governance and ethics

A fifth way that HIT data analysis can improve chronic condition outcomes is by following data governance and ethics principles and practices. Data governance and ethics are the policies, standards, and processes that guide the collection, integration, visualization, reporting, analytics, and decision support of data. By adhering to data governance and ethics, HIT data analysis can ensure the respect, trust, consent, and participation of patients and stakeholders in chronic condition management. For example, data governance and ethics can ensure that data are used for the intended purpose, that data are shared with the appropriate parties, that data are protected from bias and discrimination, and that data are evaluated for their impact and value.

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6 Data-driven culture and innovation

A final way that HIT data analysis can improve chronic condition outcomes is by fostering a data-driven culture and innovation in health care. A data-driven culture and innovation is one that embraces and leverages data as a strategic asset and a source of learning and improvement for chronic condition management. By creating a data-driven culture and innovation, HIT data analysis can encourage health care professionals and patients to adopt a continuous improvement mindset, to experiment with new ideas and solutions, to collaborate and share best practices, and to measure and celebrate their progress and achievements.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can HIT data analysis improve chronic condition outcomes?

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3

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1 Data collection and integration

2 Data visualization and reporting

3 Data analytics and decision support

4 Data quality and security

5 Data governance and ethics

6 Data-driven culture and innovation

7 Here’s what else to consider

Healthcare

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How can HIT data analysis improve chronic condition outcomes?

1

2

3

4

5

6

7

1 Data collection and integration

2 Data visualization and reporting

3 Data analytics and decision support

4 Data quality and security

5 Data governance and ethics

6 Data-driven culture and innovation

7 Here’s what else to consider

Healthcare

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