Why Everyone Can Benefit from Predictive Analytics in Healthcare

     Predictive analytics is simply the way we achieve personalized medicine; we are predicting how a patient will respond to a medicine or treatment, given that person’s #genotype and #phenotype. The problem of misdiagnosis is an example of a great opportunity for predictive analysis to learn tools such as searching for root cause analysis (#RCA) of diagnostic errors. Root cause analysis of medical mishaps are one of the main requirements of accreditation/lean standard.  #Six #Sigma basically used four ideas concerned with #systematic, #statistically driven #improvements for #products and is often used by manufacturing. The process includes measuring baseline, establishing goals and moving toward those goals. Six Sigma Oriented companies aimed for #perfection of #production such that six standard deviations would fit on each side of mean between upper and lower tolerances.Whatwill

Biomedical informatics branch of six sigma having goals as

  • Increasing Effectiveness
  • Developing more efficient process.
  • Increasing patient-Centeredness.

Everyone likes concept of gazing into a crystal ball to learn #what will #happen in the #feature.  Many people are interested to #change what will #happen in the #future. We #can’t #change the #future, but can we #predict with #reasonable #accuracy what it is that might happen? Using this in #healthcare can follow a more #proactive approach to the #diagnosis and #treatment of #disease.  Moving from #reactive to #proactive response in healthcare. #Predictive #analysis aim is to guide in selecting and tailoring treatments for individuals by predicting the course of events that is likely to occur with every treatment option that is available. Of course, these concepts not only apply to #individuals but also to #populations and by using  the predictive analysis we can foresee #public #health #threats and take the  necessary steps to #lessen their #burden or #prevent them from happening at all.

#Medicine, #Healthcare data moved towards #Big #Data as vast amounts of biomedical data accumulated in many form such as #notes, free #text, #radiographs, #photos, #gene #sequences, #microarrays, #vital signs, and #lab values, predictive analytics tools, and #learning to recognize #patterns in this #data. The #more #historical #data (experience) that are processed in the #building (or training) of the model via #Machine #Learning Tools, the better it will provide #Predictive #Analytics.

As #time #passes and new data become available they can be added to the #training #data #sets and #model can be trained. So, each year passes, your #model can become #increasingly #accurate. Predictive analysis will play a #key #role in meeting the #goals associated with the #meaningful #use. Predictive analysis is well established in area of #business like #CRM, #fraud #detection, sales #forecasting, online #retails, airline uses in #scheduling the flights. In #healthcare, we are catering #treatments based on how a patient will optimally respond, #scheduling the #nurse and #doctors and #staff based on #predicted #patient #volume. #Efficient #purchase and #storage of #medical supplies according to #predicted #demand.

A common approach to incorporate text into predictive modelling projects is called #Statistical #Natural #Language #Processing (SNLP) methods. This approach starts by counting words and phrases across #documents and calculates relatives or otherwise transformed word frequencies as a #predictor in a model. #Notes #information can be used to increase the #accuracy of subsequent #heath #risk #predictions. #Text #mining and #clustering can be used in #predictive #modelling to #uncover #unexpected information.  #Primary #Care Physician and #Speciality #Physicians in Physician #Episode vs #Normative Episode cost ratio is calculated and when the ratio is greater than 1.0 the physician cost is higher than expected. Patient Mix helps in how to identify high readmission rate rules using association rules data mining on the initial admission diagnosis.  #Predict the possibilities of #Patient #Readmission. Those possibilities can be presented to the doctors who are working with these patients. #Reason #Scores are a way to explain the prediction of analysis model and to identify the #root #cause #driving specific prediction

predictive-analytics-modelling-processWeight of Evidence(#WoE) provided by #patient #engagement.  #Patient #Portals potentially produce more accuracy in #diagnosis because #physicians often make “#Contextual Errors” during routine office exams.  Controlled studies defined such errors as #physician #missing #cues to #correct the #diagnosis and #treatments. In Artificial Intelligence, actors were trained to present either “#Contextual” or “#Biomedical” #cues that a physician might or might not #pick up in diagnosis. If a physician probed further, a more accurate diagnosis and/or treatment resulted. Some #patients are unable to take medicine due to cost factor.  Patients potentially serve as a good source of data which physicians can use in personalising #treatment developed in a virtual reality program.  There is unlimited #Potential for #Predictive analysis in #Connected #Digital #Health.

Sachin Nimbalkar works as Technical Leader with Compumatrice.com and PatientConnect360.com Services and Products, He known for his excellent leadership skills, customer centric approach, enthusiasm, passion and candour as well as turning “Technical Strategy Into Action” to generate results. He is an effective leader with proven capabilities in hiring and retaining talent, mentoring teams, and enabling knowledge sharing amongst the team. He works with customers, delivery managers and technical teams for securing and executing synchronized projects.

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sachin@compumatrice.com

Sachin Nimbalkar works as Technical Leader with Compumatrice.com and PatientConnect360.com Services and Products, He known for his excellent leadership skills, customer centric approach, enthusiasm, passion and candour as well as turning “Technical strategy into action” to generate results. He is an effective leader with proven capabilities in hiring and retaining talent, mentoring teams, and enabling knowledge sharing amongst the team. He works with customers, delivery managers and technical teams for securing and executing synchronized projects.

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