Improving Provider Engagement with Innovative Data Analytics

Background

Data Overload & Interoperability Issues

Provider engagement creates strong relationships between hospitals and healthcare professionals that result in better health outcomes for patients. However, interoperability doesn’t stop at transferring and sharing data between hospitals and facilities. Healthcare professionals need the data to be translated into meaningful information. Over half of surveyed healthcare executives believed that data integration and interoperability were lacking in regard to data analytics (1). This is because it is often difficult to share, clean, and interpret data between different systems. This also makes it difficult for machine learning to create automated alerts that could increase provider engagement. The added burden on healthcare professionals for having to integrate and compile large amounts of data before using it can lead to clinician burnout. Therefore, the current system of data transfer and sharing can actually decrease provider engagement rather than increase it.

Solution

Translating Data Into Meaningful Information

Effectively increasing interoperability can provide physicians with multiple pieces of the patient’s medical health history and can lead to a better diagnosis (2). Creating an integrated and central system that contains all of a patient’s health records in the same format can lead to more effective data exchange and translation. Furthermore, combining EHR with data search functionality highlights important information and sends alerts to clinicians as needed (3). This allows clinicians to receive personalized information based on the patient and allows them to be more engaged in making holistic diagnoses.

This solution will also prevent clinicians from being overburdened with too much data by using natural language processing to organize it into meaningful connections (3). This allows information like a patient’s medications, lab visits, and test results to be synched together like different pieces of the bigger picture, rather than their own separate entities. This will prevent physicians from prescribing any medication that could lead to more severe outcomes for patients, as well as other misdiagnoses. Furthermore, clinicians can respond to alerts in the order of their priority. Additionally, the system should be able to notify the clinician if certain important information is missing (3). This allows doctors to obtain the necessary information for accurate diagnoses and provide preventive care (3). 

Conclusion

Increasing provider engagement is important to provide more holistic diagnosis and preventive care for patients. Currently, large amounts of data overwhelm clinicians and lack the proper translation into meaningful connections and information. Having an EHR system and a machine learning program that connects all of a patient’s medical history together is crucial. This allows clinicians to have the whole picture from different departments and hospitals when making diagnoses and treatment plans. Furthermore, increased provider engagement can create better communication and relationships with healthcare professionals and prevent medical errors. Therefore, creating innovative solutions to translate data into prioritized alerts and notifications allows clinicians to be more engaged in the system.

References

  1. https://www.fiercehealthcare.com/tech/majority-healthcare-executives-don-t-trust-their-organization-s-data-survey-finds
  2. https://www.fiercehealthcare.com/health-tech/himss22-boost-interoperability-survive-value-based-care-healthcare-execs-say
  3. https://www.fiercehealthcare.com/health-tech/google-health-announces-meditech-first-ehr-vendor-integrate-care-studio