5.9 Conclusion
The availability of a large amount of data in the healthcare arena from various platforms and its ease of analysis thanks to modern software offer numerous benefits to patients in remedial and preventive medicine. In addition to managing patient outcomes in real time, data analytics offers predictive modelling to conserve resources and identify patients at risk of further harm, rather than just focusing on managing current illnesses. Policymakers must ensure adequate governance of data access to protect patient privacy and balance reasonable access to be able to yield meaningful correlations without the risk of incorrect or biased results.
REFLECTION
- From your professional experience, what are the main challenges you observe in using data analytics to enhance value-based healthcare?
- Can you identify data analytics gaps in your organisation that if addressed could improve value-based healthcare?
- What type of data would be useful in your practice to improve value-based healthcare?
- In your experience, what are the key factors that contribute to the success of using data analytics to improve value-based healthcare?
- What areas in your practice could benefit from the use of data analytics to improve value-based healthcare?
- What strategies do you recommend to improve the use of data analytics in your practice for value-based healthcare?
- Which category of data analytics would be more useful in your practice to provide effective value-based healthcare and why?
- In your practice, which patient populations would benefit the most from data analytics for value-based healthcare?
- In your practice, how does the use of data analytics improve your organisation’s financial incentives?
- How do you see healthcare delivery evolving with the widespread adoption of data analytics in your area of clinical practice, particularly in the context of value-based healthcare?
