More and more research is being dedicated to predictive factors. I can appreciate the need to be able to predict outcomes. The one thing with FOTO that I truly love is the predictive ability based on the risk adjustment process. At the same time, my mind wonders.

As we know, having the ability to be able to predict an outcome AND the best path of care would have a high amount of value. In the near future, we will begin to see more money and time spent in designing systems to include machine learning. I wanted to take time to share machine learning and how it could be helpful in the medical world.

After listening to Suchi Saria speak, it really seemed to me that systematic reviews may not be the best option when it comes to physical prognostic factors. What would it look like if we began to implement machine learning? We’d have a ton of data and information points within the electronic medical record. We’d capture the real world, all encompassing, messy information versus the few data points and factors we seem to believe have the most value. I feel excited about what could be learned and how quickly we could learn based on real-life data. 

You’ll find the abstract to the recent study below.

Physical prognostic factors predicting outcome following lumbar discectomy surgery: systematic review and narrative synthesis.

The post Physical Prognostic Factors Predicting Outcome Following Lumbar Discectomy Surgery: Systematic Review and Narrative Synthesis appeared first on FOTO Patient Outcomes.

How many of you wonder if your patient will improve? You meet your patient and you wonder… will this patient improve? A friend of mine believed that one question alone would help answer this question. Very simply asking, “how confident are you that you will improve?” Okay, it may be helpful, I’m not sure it has a solid bulk of research supporting that a single question can give an answer.

Right now, all of you using FOTO have an opportunity to help Dave Walton. Let me jog your memory… Dave was a recent recipient of the $10,000 D. L. Hart Memorial Research Grant. Dave and his team are on an adventure. They are on a mission focused on predicting individuals who will experience continued disability and persistent pain. This research is definitely relevant for clinicians in the trenches. To know the risk will inform your clinical decisions – including the words you use, the therapeutic alliance you build, the interventions you choose and the referrals you recommend.

The way Dave and his team planned on learning and predicting hinged on a patient self-report measure, the Traumatic Injuries Distress Scale.  The study is: “Translation and Further Validation of a Risk-Based Prognostic Screening Questionnaire.” You have a very, very easy way to collaborate and assist Dave and his team’s endeavor. Within the administrative portal of FOTO, you can go to the various optional surveys. Set the Traumatic Injuries Distress Scale (TIDS) so that it is required. If you are familiar with the STaRT back screening tool, it seems to me that the TIDS is somewhat similar. The TIDS’s scores range between 0-24 points. Initial score thresholds for clinical interpretation of the TIDS are as follows: Rapid recovery scores: <= 3 points; Slow or partial recovery scores: = 4 – 9 points;Non-recovery scores: >= 10 points. If you could capture complete episodes of care for at least 10 patients who are being treated for a musculoskeletal problem, you’d really be helping this project. Dave’s team is depending on clinicians like you for successful completion of the research study. Please take this opportunity to clinically support this research project.

Until next time,

~Selena

The post Collaborative Opportunity for Clinicians and Academia appeared first on FOTO Patient Outcomes.