Healthcare analytics has evolved during Covid19, enabling us to better understand what patients express on telehealth consultations, live feeds, and social media channels. The sudden onset of the pandemic has resulted in a paradigm shift in the way healthcare has been traditionally managed and delivered. Governments and healthcare organizations have realized that AI-driven technologies can be optimized for patient care and patient voice data in newer and better ways. This has also lead to a better quality of healthcare data that is being used to improve care through sentiment analysis of the gathered information..
AI-driven Healthcare Analytics
Covid19 has forced healthcare organizations to lean momentously on artificial intelligence (AI) technologies in their race against time to meet the escalating demands on critical care. Healthcare analytics, by leveraging data collected from patient voice and healthcare organizations, has resulted in the growing acceptance that basic care does not need to be dependent on sophisticated infrastructure. There are two major strengths that have emanated from this scenario:
- Leveled regional shortages: AI-powered virtual care has ensured better time management and efficiencies by leveling the playing field in healthcare distribution. A very important development has been in increased access to quality care because regional shortages and uneven distribution of specialists have been supervened through virtual healthcare delivery models. Thus virtual care powered by healthcare data analytics has lead to people living in small towns having the same access to healthcare as those in major cities.
- Increased patient engagement: Another facet has been the increase in better chronic disease management brought upon by increased patient engagement in their treatment plans. Patients no longer have to visit a physical location unless there is an actual need for a physical consult. All of these efficiencies have helped augment healthcare analytics tremendously as the data gathered is more varied and diverse, and has an increased level of input from the Voice of the Patient.
The diagram below represents how AI-driven healthcare analytics has helped virtual care in bringing crucial advantages to existing medical delivery models.
What are the new sources of healthcare data?
New sources for healthcare data analytics include electronic health records, voice notes, patient forums, video consultations, social media, surveys, and customer care data.
- Electronic health records (EHR) : EHRs are perhaps the most important sources for healthcare analytics as they contain clinical, medical, and treatment histories of patients. This can include patient names, addresses, gender, age, diagnosis, treatment plans, vaccination records, allergies and medications, surgical procedure histories, lab results, and even scans and radiology images.
- Voice notes: Voice note recordings made during consultations hold vital information that can be used for the historical analysis of medical data. Studies have proven that they play a significant role in better patient satisfaction, recall, fulfillment of information needs, and decision-making, especially in patients over the age of 50. Audio content analysis can process all this data for valuable insights for current and future use.
- Video consult records: Video consultation and telemedicine have been around for long but haven’t really been that popular until the pandemic made people realize how important these services can be. Data-wise, records from telehealth are very useful in healthcare data analytics because they contain not only medical information but also patient experience data. These sources can be leveraged through video content analysis for sentiment mining as well as for comparative analysis of records for clinical research.
- Medical surveys: Medical surveys by clinicians are a key source of data for healthcare analytics. They are crucial to clinical trials, vaccine research, epidemiological studies, as well as to understand the concerns of medical staff like equipment, workload, remuneration, etc. This survey data gathered by the Candian Medical Association is a great example. Similarly, patient voice surveys beget important information on care improvements such as to discover which locations of a hospital are in need of additional resources, or which pharmacy locations have higher footfalls.
- In-care questionnaires: In-care questionnaires are vital to monitor the quality of care and psychometric performance in critical areas like oncology, cardiovascular, burn units, and intensive care medicine. They are equally important in geriatric care, where plans of treatment and healthcare data analytics are enriched through the patients’ perspective of their experience.
- Feedback forms and emails: Healthcare analytics benefit greatly from feedback web forms and emails as they are useful in cases where people may not prefer an impromptu customer care telephone call for feedback. This could be because of patient limitations like age-related disparity or because of personal choice. This form of feedback can be used for all age-groups thanks to user-friendly smartphones and designs that are developed also keeping the elderly in mind.
Use Cases – How Healthcare Analytics is Improving Patient Care
Healthcare analytics in improving patient care through four major areas: Hospital management; doctor and clinician offices; critical staff and healthcare employee satisfaction; and an enhanced patient voice. We detail each of these areas with live examples below:
- Hospital healthcare aspects: Text analytics and sentiment analysis allow us to extract and classify vital insights from aspects like patient admissions, hospital administration and hygiene, medical staff, the ER, and more. Healthcare analytics from surveys and internal management systems can tell how a hospital’s location features on the satisfaction scale, and even how facilities like the cafeteria, washrooms, parking issues, are being managed.
- Doctors office healthcare aspects: Repustate’s text analytics solution examined and processed doctor notes recorded in the EHRs. This helped the doctors understand compare and analyse aspects like the effectiveness of medications and dosage combinations, the effectiveness of primary physicians, lab results, medical imaging, and more.
- Healthcare worker satisfaction: Patient care is dependent as much on a conducive work environment for the medical staff as it is on medicine. Important elements scrutinized for sentiment through healthcare data analytics include remunerations, working hours, benefits, the quality of medical equipment, effective leadership, and staff morale.
- Patient voice for the public sector: Government bodies and public healthcare organizations depend heavily on healthcare analytics to understand the progress or the lacuna of their policies. Studies from millions of annual surveys and feedback forums help government agencies equate the distribution of medical resources and specialty care to ensure that all members of the society receive equal quality care.
Also Read: Topic-Based Sentiment Analysis
