Understanding and Engaging Your Healthcare Target Audience through Data Analytics

Today, data analytics is used in virtually every industry but is no more prominent than in healthcare where it has emerged as a valuable tool for improving patient care and establishing healthcare target audiences for marketing. Data analytics is essentially the use of vast amounts of data to extract meaningful insights regarding patient needs as well as optimising treatments and achieving better targeted markets. Currently we are seeing an increased emphasis towards data-driven decision making which navigates complicated challenges, identifies patterns and trends, allocates resources and contributes to strategic planning for goals such as cost management and reduction.

Discovering Patient Behaviours and Preferences with Data Analytics

Data analytics can play a pivotal role in the personalisation of medicine by permitting healthcare providers to use patient-specific data in order to tailor treatments to individuals. Understanding patient behaviours is absolutely vital in the personalisation of medicine and stems from a detailed insight into datasets involving genetic, environmental and lifestyle information. Such information enables researchers to accurately inform treatment decisions as well as likelihood of response to these treatments. One example involves David Howell from Surrey Heartlands Health and Care Partnership in which he used his data analysis skills to create a machine learning model that accurately predicts 70-80% of hospital readmissions.

In addition to readmissions, the analysis of patient data enables healthcare providers to identify trends in medication errors, problems with post-discharge follow-ups as well as inadequate patient education. Another example can be found with Paul Knox from the Mid & South Essex NHS Foundation who used to work on two weekly reports taking a total of 3.5 hours to produce. Using newly acquired computer skills, the reports now take less than 30 seconds. One of these reports is used to identify priority patients for operating theatres, and now takes less than a minute to produce, which when combined with the second report saves in total 182 hours a year.

Using Healthcare Target Audience Data for Personalisation and Targeted Outreach

Marketing personalisation uses data to target audiences and individuals with a brand message that speak specifically to a customer’s interests, needs and buying behaviours. It aims its products directly at consumers most likely to require or desire them. This form of marketing also improves connections with customers, can boost revenue, increase brand reputation, increase customer loyalty and improving overall customer experience. NHS and private healthcare providers currently utilise health tips, check-up reminders, product advertisements, and targeted health awareness campaigns which in turn will have a net result of greater compliance and improved health for individual consumers.

Role of Feedback

Investigation into patient preferences can also permit better staff management through the use of patient satisfaction scores. This works in conjunction with surveys, questionnaires, polls, social media and online reviews which further contribute not just to improved service level but also improvements and greater success in marketing strategies. Further to this the continuous use of feedback from patients incurs a cyclical process whereby a practice is continually receiving updated information on how to improve its services.

Challenges and Considerations

The level of detail and quality of data is extremely important when it comes to procuring valuable insights, and healthcare data is often incomplete, inconsistent or stored in a variety of systems and differing departments or data silos. This is likely the biggest threat to the furthering of adoption of data analytics as low quality and fragmented data can lead to false conclusions and generally poor decision making.

Currently speaking overcoming data silos requires greater cooperation and data-sharing amongst practices as well as ensuring all data is clean. This means that specific errors in the data are corrected, duplicates are removed, and incomplete entries are filled to ensure reliability.

It is also essential for any healthcare provider to ensure that data is protected against unauthorised access, breaches or theft. Excellent starting points are ensuring companies adhere to privacy regulations like the General Data Protection Regulation (GDPR). They should also train staff regarding security protocols and conduct regular risk assessments to identify potential vulnerabilities.

Companies would also do well to further educate employees as healthcare data analytics requires a specific level of expertise. The shortage of data analysts continues to present a major challenge and in recent years there has been a noticeable upturn in government-funded data analysis apprenticeships.

Conclusion

The use of data analytics is set to continue in growth as more and more data becomes available and new technologies emerge. A vast number of technologies have already been adopted and demonstrated significant success. One example involves Imperial College NHS Trust using a digital alert system that analyses biometric data and alerts doctors if a patient is exhibiting signs of sepsis. The overall end result is an additional 24% reduction in the risk of death.

What we expect to see now is an increased adoption of artificial intelligence which has so far driven a vast amount of data analysis and whose services will prove to be invaluable as the amount of healthcare data available grows. This is not simply for the goal of improved patient outcomes but also to make sure that the company at hand remains competitive amongst different practices with different strategies for continued growth.

For more information on how you can engage your healthcare target audience through data analytics click here.