Data-Driven Decisions: Leveraging Healthcare Software Analytics for Improved Clinical Care

Tradition has always been the point of reference for humanity to move forward. The same principles have been inherited in the healthcare industry as well. From solely relying on intuition to hand out prescriptions to making complex clinical decisions, the healthcare industry has inherited a great deal of progress in providing care.

However, as the healthcare industry is moving towards a digital transition, the significance of custom healthcare software has become even more evident than ever before, and for good reasons. Healthcare software analytics has enabled healthcare providers to streamline their clinical workflow and processes. Taking it forward from here, the integration of EHR systems and medical data insights empowered practices to make data-driven decisions to improve patient outcomes.

Healthcare analytics tools in custom healthcare software have enhanced healthcare in various ways. However, how to use healthcare software analytics for better patient care is what many healthcare professionals struggle with.

So, to make the use of healthcare software analytics easier and how it can be leveraged to improve patient outcomes, we’ve curated this blog for you. Having said that, let’s dive deep into how healthcare software analytics can empower data-driven decisions in healthcare.

The Power of Big Data in Healthcare

Ever since healthcare practices started adopting healthcare software systems in their processes to deliver care, there has been an explosion of healthcare data. With data collected from healthcare systems like electronic health records (EHRs), clinical trials and wearable devices, a vast healthcare data is generated which can be used to provide better care.

While this data stands as evidence to provide better care, custom healthcare software can transform this raw data into actionable insights. This would not only enhance the care delivery process but also help providers make data-driven decisions that can significantly improve patient outcomes.

Healthcare analytics tools bring data to the epicenter of healthcare practice. This way, the diagnosis process is improved with accuracy, personalized treatment plans are enabled, and the administrative side of the healthcare practice is also optimized. Big data combined with healthcare analytics tools can help healthcare providers improve the accuracy of their diagnosis, which enables providers to curate personalized treatment plans that directly or indirectly lead to improved patient outcomes.

Furthermore, utilizing this data to optimize resource allocation also helps the healthcare practices to streamline and manage their practice accordingly.

Applications of Healthcare Software Analytics in Clinical Care

One of the most used applications of healthcare software analytics in providing clinical care is for predictive analytics for risk assessment of patients. Let’s try to understand this with an example.

For instance, a patient comes to consult for issues they might be facing in breathing. Here, data analytics tools can analyze the patient’s data with historical data on other patients suffering from other respiratory diseases. They can then show if the patient is on the verge of developing a chronic disease like COPD, asthma, etc. Here, predictive analytics can kick in to help healthcare providers make better clinical decisions and improve patient outcomes.

Now, let’s try to understand another application of healthcare software analytics. For instance, clinical decision support systems can be integrated with healthcare systems, which can assess patient’s data in real time collected from EHRs and wearable devices. With clinical decision support systems integration for clinical data analysis, it can help healthcare providers in recommending treatments that can complement the patient’s ongoing treatment to improve patient outcomes.

Another application of healthcare software analytics is medication management. Since the analytics can help identify potential drug interactions with the diseases and the patient outcomes it provides. It can be analyzed with historical data, and the system can suggest optimized medication regimens that can help healthcare providers in improving patient outcomes significantly.

Last but not least, healthcare software analytics can help streamline hospital operations as data becomes the epicenter of the healthcare processes; it can improve resource allocations like beds, and private wards. Apart from that, it can also help management make better staffing decisions with respect to patient flow.

Ensuring Data Quality and Security

Improving clinical care with healthcare software analytics has been one of the main reasons why it has been integrated into the healthcare systems. However, to ensure accurate clinical data analysis and medical data insights it is important to ensure the quality of data that too in a standardized format.

Suppose the healthcare software analytics system is trained on unstructured and inaccurate data, then it cannot generate reliable analytics results, leading to misleading care planning and deteriorating patient outcomes. That is why it is important to feed the healthcare software analytics clean, accurate and standardized data for reliable results.

Along with that, the healthcare industry being highly prone to cyber attacks and threats, the need to implement robust security measures that aims to protect sensitive patient information is crucially important. Abiding by the rules of regulatory bodies such as HIPAA, HITECH Act, GDPR, etc. is one of the best practices to protect sensitive patient information in healthcare systems.

Furthermore, along with securing patient data, it is equally important to ingrain transparency in healthcare systems. This helps healthcare providers to build trust with patients and in telling them how the data is collected is used.

Challenges and Considerations for Implementing Analytics in Healthcare

There are many benefits of data-driven decision-making in clinical settings, however, implementing healthcare software analytics systems comes bearing many challenges. To simplify this for you, let’s deep dive into the challenges of implementing analytics in healthcare and what are the considerations you must keep in mind.

• Integration with Existing IT Infrastructure: EHR systems have somewhere become the backbone of modern-day healthcare systems. These systems collect all types of data about the patient and act as a data bank for the systems; integration with EHR systems is extremely important. To integrate seamlessly, it is important to achieve interoperability as it enables systems to communicate seamlessly with other systems.
• Interdisciplinary Collaboration: For healthcare analytics tools to be successfully implemented into healthcare systems, it is important for clinicians, data analysts, and IT professionals to collaborate with each other. This is to ensure that the data is being used effectively and in the right way to deliver optimum care.
• The Human Factor: Even though the healthcare industry is moving towards automation with custom healthcare software systems, the human factor is essential for the delivery of care. That is why data insights must be analyzed and finalized by healthcare professionals for optimal care.

The Future of Healthcare Analytics

Since the healthcare industry is transitioning to a completely digital ecosystem, its combination with advanced technologies can enhance the future of healthcare analytics. Given the current state of the healthcare industry and technologies, this is how the future of healthcare analytics might look in the near future:

• Artificial Intelligence (AI) and Machine Learning (ML): AI and ML algorithms can enhance accuracy in diagnosis as these systems can analyze complex data in seconds and with accuracy. This will enhance the diagnostic process with accuracy and enable healthcare providers to take a holistic approach to provide better-personalized care.
• Precision Medicine and Personalized Health: Back in 2003, inaccurate mediation was one of the reasons for deaths. Data analytics can pave the way for driving precision in medication with targeted therapies based on individual patients’ genetic profiles. This way, clinical data analysis will become an integral part of care delivery, and providers will be able to provide personalized care with accuracy and data-backed decision-making.
• Big Data for Population Health Management: It is natural that healthcare analytics tools will use different types of healthcare data to generate results. In population health management, it can be used to track disease outbreaks in different patient population groups and predict the healthcare needs they might need beforehand. Such practices can not only improve public health but also change the nature of care from reactive to proactive.

Conclusion

Healthcare software analytics have the potential to change the complete nature of healthcare delivery from enabling data-driven decisions to clinical care improvement, it can allow healthcare providers to provide proactive care with better personalized care and preventive care measures.

Furthermore, with data as the epicenter of care delivery it enables healthcare providers to make better decisions on the basis of data and cyclic predictions. These medical data insights are crucial in improving patient outcomes and enhance the functioning of the entire healthcare industry.

So, make sure your healthcare providers make data-driven decisions to improve patient outcomes. Let’s start the transformation today. Click here to get started.