Parkinson’s disease, a progressive nervous system disorder affecting movement, is a complex disease that makes diagnosis difficult. Up to 25% of patients with PD are misdiagnosed; of these patients, approximately 50% are mistreated, resulting in the presentation of periods with uncontrolled symptoms and lower quality of life. Misdiagnosis and treatment for a non-PD disease can lead to medical malpractice. However, utilizing more advanced statistical analysis and machine learning, researchers are beginning to develop methods for better predicting PD.
By utilizing advanced statistical methods, researchers may be able to more accurately predict PD based on motor and non-motor predictors of PD, resulting in lower misdiagnosis and therefore less medical malpractice. The statistical model developed by one research group included predictors based on gender and the four parts of the unified PD rating scale. Based on a long-term study for PD, these findings can potentially take the subjectivity out of a diagnosis and, through a rating system for patient history and past treatment, be able to better diagnose PD.
Utilizing machine learning renders the analysis of a lot of multidimensional data attainable and therefore makes connections that cannot be typically made from the rote analysis of data. Using this analysis, researchers were able to positively predict PD with over 83% accuracy, resulting in a significantly higher rate of accurate diagnosis. With methods of predictability easily accessible, not utilizing these resources to better predict PD within patients may more easily fall within the realm of medical malpractice.
In a situation where an individual or loved one has been misdiagnosed and treated for something other than their disease, resulting in a lower quality of life, more aggressive symptoms or injury due to mistreated symptoms, the patient may be entitled to damages. A qualified professional attorney may be able to help obtain these damages and retribution for medical malpractice when there are tools available to more accurately predict certain diseases.