- 05/07/2024
In the post-pandemic era, most healthcare organizations renewed focus on being data-driven. The healthcare sector generates a lot of data in the form of electronic medical records, medical images, clinical data, and wearables; and it is growing exponentially. The digitization of healthcare records over the last few years, coupled with the proliferation of tech-driven medical devices as well as structured and unstructured patient-centric data, has led data volumes to skyrocket.
Unlocking the potential of this data paves a new path for improved healthcare outcomes, reduced healthcare costs and better community health management as the healthcare industry is undergoing a rapid transformation to be patient-centric. But how do we derive these needed insights from this data?
This is where data analytics comes to the fore.
Data analytics can help uncover patterns with different techniques to fuel the decision-making for healthcare organizations. Health informatics, the confluence of data, IT and business insights to manage patient care and healthcare operations have been witnessing great value from data analytics. But there is a catch.
Healthcare data is multi-faceted, a lot of it is unstructured, which often limits the extent to which this data can be analyzed. As per research from HIMSS and Arcadia, less than 60% of data in healthcare organizations is leveraged for intelligent insights. However, most healthcare leaders agree that quality data and insights from across healthcare systems are critical for organizations’ performance.
Harnessing the power of Data Analytics in Health Informatics
Well-informed decisions powered by data analytics can drive patient outcomes and empower change in new-age healthcare services. There is now more data than ever from electronic health records, medical images, clinical results, and patient logs to analyze and fuel decision-making. Here are a few data analytics use cases in healthcare:
- Forecasting operations – To improve the scheduling of hospital equipment and clinical procedures, providers need to understand patterns from their past data. This can help them to plan their resources more effectively. Distinctive operation capabilities can be a true differentiator in the new care model.
- Readmission risk – Readmissions increase costs significantly to healthcare providers. Hence, analyzing the data for a readmission score can help in enhancing post-operative care and communication with the patients.
- Managing medical errors – Errors in diagnosis, surgeries, and medical imaging affect around 400,000 patients annually. Data analytics can help identify these outliers to some extent and reduce the costs as well as improve the quality of care.
- Population health management – Understanding the healthcare needs within a community can help mitigate severe outbreaks and plan for the resources accordingly.
These are just a few to showcase the value of data analytics. A lot more can be achieved; depending on the payer and provider needs to reduce costs and optimize operations.
Benefits
Focusing on a data-led patient-centric care approach can help resolve a lot of existing challenges in healthcare. Here are a few benefits of leveraging data analytics in health informatics.
- Preventative care and adherence to treatment plans
- Personalizing treatment plans and care options
- Lower costs and better operational efficiencies
- Healthier population and communities
Conclusion
Patient-centric care is a transformative approach to new-age healthcare needs. Predicting health risks, and improving operational performance of healthcare facilities while understanding the patient behavior plays a vital role in patient-centric care. However, challenges with the security and privacy of patient data need to be mitigated while leveraging data analytics in health informatics. The path to value creation is bound to adopting data analytics, enhancing operational workflows and standardized practices. Want to kickstart your healthcare data analytics journey? Reach out to us at reach@altysys.com
