How can Big Data & AI Improve an Orthopedic Clinic's Revenue Model?
Think about potential value-adding applications big data could support in health care. We referenced the following questions to guide our thinking:
- What sources of data would you be accessing?
- Would the data already be structured, or will you need AI to convert it from an unstructured state?
- How would access to big data in your particular use case improve a medical clinic?
As a healthcare marketing specialist focusing on new patient acquisition, it is essential to correlate marketing funds to an ROI. This is very difficult to measure as a majority of healthcare clinics have a difficult time evaluating patient ROI, average revenue generated per patient, per case, and per clinic visits based on specific diagnosis and treatment plans when compared to their marketing plan.
With AI, NLP and machine leaning I am hopeful to evaluate practice reimbursement based on the patient's unique treatment plan.
To evaluate a revenue model per patient visit/treatment ACL surgery cases for example, the practice would need access to common treatment plans, unique treatment procedures that may expedite healing, industry proven data sources, and past patient experiences/medical records. this data would be unstructured, and the use of NLP would benefit a human's time efficiency and lower the error rate to better structure the data to be clearly evaluated and calculated by a machine learning algorithm.
By accumulating structured data on surgical techniques, patient demographics, and post surgical rehab, a physician can evaluate with more accuracy at much faster rates on how to calculate a clinic ROI per ACL patient and compare it to their medical marketing expenses. Unstructured data will allow the physician to evaluate and determine the best case surgical method and treatment plan, which will then forecast the necessary medical care treatment plan and number of clinic visits needed for the patient to expedite healing and precisely calculate a patient revenue value, which would put the power back in the hands of the clinic manager when forecasting revenue and cost streams.
With this now structured data utilized in a machine learning model to forecast new patient revenue streams, a practice can grow at their preferred rate, with the patients they prefer, while minimizing physician burnout, at a pace that is sustainable.
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