How to use Healthcare Analytics to Improve Patient Self-Management Care

16 Mar 2020 by Emma Foster 5 minute read

There has been growing momentum in self-management care and organisations supporting patients with long-term conditions to manage their own health. Patients can take an active role in maintaining their wellbeing and with wearable technology advancing data is becoming available to patients and healthcare service providers. 

Healthcare Analytics for Patient Self-Management Care

The effective, secure and informative use of this data can help patients understand what they can do to stay healthy and help healthcare providers use data analysis to predict and make informed decisions around next steps for individual patients. 

Patients with long-term conditions are continually working with care providers to build management plans to keep on top of their conditions, here are a couple of examples and how analytics can boost patient engagement and collate data for healthcare providers. 

Data Analytics for Medication Management 

Data analytics for medication management is leading to reduced costs and more patient engagement.  

Health monitoring technology is making its way into patients' lives, there are several innovations out there to track and improve patient medication adherence including digital pill boxes and bottles, text message alerts and numerous apps to assist with patient medication reminders. Barriers can be removed with this technology by sending feedback to healthcare providers directly from patients but also providing real-time analytical insights and expert advice to patients reiterating the importance of following medication plans as prescribed. 

Healthcare providers are looking towards analytics and predictive analytics to discover patients who don’t adhere to their medication plans. Currently medication planning and interventions have been a “one-size fits all” strategy whereas predictive analytics will allow providers to see specific patients that need help and different types of interventions for each case.  

“Population-level studies have shown that medication adherence is only about 50 to 60 percent.” (Mike Miliard, Healthcare IT News) 

Big data analytics can help predict who is likely to become non-adherent, identify timings of when to intervene to increase success and how well patients respond to different types of interventions. 

Data Analytics for COPD Management 

There are many long-term conditions were self-management could see better patient outcomes but one that is currently under research and patients find difficult to manage is COPD (chronic obstructive pulmonary disease). Research has been undertaken to see if wearables and self-management apps could help patients with COPD, from ERJ Open Research 

“Technology could improve patients' ability to manage their condition both in daily life and during exacerbations by connecting how they feel and by knowing their oxygen saturation, heart rate and activity. The technology may help them address feelings of fear and panic associated with exacerbations and may provide reassurance and connectedness.” 

Real-time data analytics fed into mobile apps could give patients with COPD instant access to resources, expert advice and external data insights that could affect their condition. “In patients with COPD, self-management has been shown to increase quality of life and reduce respiratory-related hospital admissions” (ERJ Open Research) 


Data analytics does not only have to come from the patient by tracking their health through wearable technology but accessible easy to use dashboards can be provided to patients giving advice and connecting to the information they need. One example is air quality and how patients need a view of it to manage their respiratory problems.

Air Quality Data Analysis and Health Management

55% of the population live in urban environments and these environments play an essential role in human health and wellbeing. Air pollution contains air particulate matter, PM2.5, that can stay in the air for long periods of time and be inhaled deep into the lungs. Pollution within urban areas can have an affect on asthma sufferers and "Around two thirds of people with asthma tell us poor air quality makes their asthma worse, putting them at risk of an asthma attack." (

Asthma is a long term condition that needs careful management and monitoring to keep it under control reducing risks of attacks. People are feeling the effects of air pollution on their asthma so how can they manage attacks when they have less control over air pollution? 

Clean Air for Healthcare is a health data research platform and suite of applications for those living with respiratory conditions and those who care for them. By giving a unique, personalised perspective on health and air quality it allows users to see localised air quality data and empower their decisions on when to take part in certain activities or areas to avoid.

It will help to enable personal action plans and with historical data and trend analysis help predict air quality changes giving the patient real-time and predictive information. 


By leveraging data analytics and predictive analytics patients can participate in their own care plans that could lead to improved patient health outcomes, reduced healthcare costs and improved care delivery.  

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Topics: Data Analytics, Predictive analytics, Big Data, Analytics as a Service, Data Optimisation, HIMSS20

Emma Foster
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Emma Foster


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