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Life Science And Healthcare Analytics

What is Life Sciences and Health Care analytics and its Challenges?

Enormous Data Analytics in the Life Sciences and Health Care area will increment multi-crease to a few billion USD. Among the best on the planet, Life Sciences and Healthcare enormous information statical or information specialists to have Advanced Robust Analytics arrangements about numerous difficulties that are coming up because of the current economic situations and the unique changes in the LSHC area. We can have powerful usage of the Revenue Cycle Management framework, utilizing Big Data investigation.Normal Language Processing and enormous information examination assume a significant part in customized medication.

Case Study 1: How to increase the success rate of Clinical Trials
Business Problem Statement

On an average, it takes around 10 Years for Research and Development team to come up with a stable and successful drug. Pharmaceutical companies invest a lot of funds on clinical trials so that they can come up with a blockbuster drug on which they can survive. Failure of these clinical trials would cost them fortunes and thereby leads to the risk of its own existence.

Business Challenges

  • Increase in failure rate of clinical trials
  • Increase in the cost of clinical trials
  • Risk associated with human involvement in trials
  • Identifying the right sponsor for clinical trials
  • Increased volume and variety of unstructured data in terms of clinical trials is making it difficult to identify the reasons behind failures
  • Failures of clinical trials leading to pharma companies going bankrupt

Business Solutions

  • Clustering to identify the right segments based on demographic details of patients, drug, disease, etc.
  • Text Mining & Natural Language Processing on each cluster to identify the top reasons behind clinical trial failures
  • Statistical prediction model to predict the probability of successfully completing the clinical trial on time
  • Identifying the appropriate sponsor, under whose supervision a clinical trial can be successfully completed
  • Descriptive analytics in generating various trends associated with clinical trials in various countries, age groups, etc.

Business Impact

  • Reduction in number of clinical trial failures & thereby improved cost saving
  • Improved success rate of clinical trials & thereby increasing the opportunity of venturing into new clinical trials with less turn-around time
  • Improved funding due to better predictability
  • Reduction in compensation cost to patients

Case Study 2: How to avert the risk of patients getting readmitted
Business Problem Statement

Prognosis & diagnosis is an extremely challenging task for doctors. Bacteria & virus become immune to the medicines & evolve in a different form, making the task of diagnosis extremely difficult for doctors. Heavy antibiotics might temporarily suppress the virus, giving a fake sign of recovery, forcing hospitals to discharge the patients. After a few days’ patients might get re-admitted to hospital, which will have a penalty implication on hospitals for not helping patients recover fully. Sometimes the physicians also might by oversight perform wrong diagnosis leading to patients getting readmitted to hospitals. Patients lose confidence in those hospitals & doctors & might not come back for any other treatment in future. Also insurance firms are taking a huge brunt of claims, which could have been avoided with first time right diagnosis.

Business Challenges

  • High volume of hospital re-admission resulting in Hospitals being penalized
  • Decreased ratings
  • Huge loss for health care firms
  • Lack of knowledge in identifying the causes for the same
  • Lack of standard programs for addressing issues of re-admissions

Business Solutions

  • Clustering to identify the patients based on demographic details of treatment, diseases, hospitals, etc.
  • Identify the exact reasons for re-admission on different patient groups based on areas and the practices of hospitals
  • Pre-discharge and Post-discharge programs initiated for patients to educate them on medication, revisit appointments, regular home visits, physiotherapy etc.
  • Identify potential patients who may get readmitted and make follow-up checks on them to avoid re-admission

Business Impact

  • Reduction in number of Re-admissions
  • Reduction of losses due to penalties for hospitals caused by re-admissions
  • Increased overall health of community
  • Improved overall services provided by hospitals
  • Improved brand image of Hospital
  • Improved Hospital ratings

Case Study 3: Want to know how to reduce the number of insurance claims by predicting the diseases, which patients might encounter
Business Problem Statement

Insurance has become a necessity for an individual, be it personal health or commercial insurances. Most of the insurance companies have died down due to improper strategies and irrelevant premium plans. There is no mechanism to predict the diseases, which would result in sharp increase in the number of insurance claims. More the claims, less the profits for the insurance companies.

Business Challenges

  • Increase in complexity in predicting infectious disease outbreak
  • Increase in number of insurance claims
  • Decrease in profitability of Insurance companies
  • Reduction in efficiency of Insurance firm services
  • Complexity in determining the right insurance plan

Business Solutions

  • Clustering to identify the right segments based on demographic details of patients, drug, disease, etc.
  • Text Mining & Natural Language Processing on each cluster to predict disease from medical records of patient
  • Capturing health-related data from social media websites to identify anomalies by pattern recognition
  • Statistical prediction model to predict the probability of disease outbreak, with improved accuracy, sensitivity
  • Descriptive analytics in generating various trends associated with the probable disease outbreak

Business Impact

  • Reduction in number of Insurance claims
  • Improved cost saving
  • Improved efficiency of Services by Insurance vendors
  • Effective decision making due to better predictability
  • Reduction in compensation cost to patients
  • Preventive medical care for patients

Case Study 4: How to predict the triple-negative breast cancer, which is not possible using even advanced medical tests
Business Problem Statement

Although technology is growing at a rapid pace, there are certain gray areas where the tools pertaining to advanced technology needs to be addressed. Life Sciences and Health care sector has many areas where there is a huge potential of unearthing hidden problems. Identifying Triple-Negative breast cancer at any stage is posing a huge challenge in healthcare sector.

Business Challenges

  • Identification of receptors in TNBC is highly complex
  • Difficulty in treatment
  • Classification of Triple Negative Breast Cancer into basal-type or others
  • No standard set for care for patients

Business Solutions

  • Using Pearson Chi Squared Test, classified IDC, ILC and mixed cases
  • Determined AR positivity in Grade II and Grade III tumors
  • Clustering to identify the right segments based on demographic details of patients, drug, disease, etc.
  • Text Mining & Natural Language Processing on each cluster to predict disease from medical records of patient
  • Using statistical methods, the AR positivity for Locally Advanced Breast Cancer at different stages is determined for various cases

Business Impact

  • Identification of Triple Negative Breast cancer in a timely manner
  • Treatment at early stages results in high success rate
  • Effective decision making due to better predictability
  • Improved efficiency of Services by Hospitals
  • Preventive medical care for patients

Case Study 5:How to use image processing to predict cancer based on tumor images
Business Problem Statement

Developing & under developed countries are struggling to provide medical services at reasonable cost for the down trodden, thereby reducing the life expectancy. Lack of availability of advanced techniques has inflated the cost of medical service by taking it away from the reach of even the middle-class society. Old fashioned techniques for diagnosis has reduced the chances of correctly identifying the diseases, thereby making the medical procedure of treating the disease very costly.

Business Challenges

  • Advanced medical tests also are unable to find out the presence of cancer tumours at an early stage
  • Too many medical tests to diagnose cancer is causing pain to the already distressed patients
  • High cost of diagnosis is making the availability of services difficult to the poor & needy

Business Solutions

  • Performed clustering of the tumour cell images of various patients based on various parameters including demographic & previous medical history data
  • Performed deep learning techniques in analyzing the images of tumour cells & classify as benign or malignant
  • Image processing performed to match with the other tumour cell images in determining the stage in which cancer exists

Business Impact

  • Early identification of disease led to better chances of survival through early medication
  • Less cost of diagnosis for patients
  • Less turnaround time in diagnosis the disease
  • Increase in the success rate of effectively diagnosing

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