ANALYTICS IN HEALTHCARE INDUSTRY

Healthcare organizations around the world are challenged by pressures to reduce costs, improve coordination and outcomes, provide more with less and be more patient centric. Yet, at the same time, evidence is mounting that the industry is increasingly challenged by ingrained inefficiencies and suboptimal clinical outcomes. We help building analytics competency for your organizations to harness “big data” to create actionable insights, set your future vision, improve outcomes and reduce time to value.

What values does Analytics bring to Healthcare?

Predictive analytics uses technology and statistical methods to search through massive amounts of information, analyzing it to predict outcomes for individual patients. That information can include data from past treatment outcomes.

  • Potential benefits include detecting diseases at earlier stages when they can be treated more easily and effectively
  • Managing specific individual and population health and detecting health care fraud more quickly and efficiently
  • Certain developments or outcomes may be predicted and/or estimated based on vast amounts of historical data, such as length of stay (LOS)
  • Patients who will choose elective surgery
  • Patients who likely will not benefit from surgery
  • Patients at risk for medical complications

Areas of Analytics Implementation in Healthcare

 

Clinical operations

Comparative effectiveness research to determine more clinically relevant and cost-effective ways to diagnose and treat patients.

Public health

Analysing disease patterns and tracking disease outbreaks and transmission to improve public health surveillance and speed response. Faster development of more accurately targeted vaccines, e.g., choosing the annual influenza strains. Turning large amounts of data into actionable information that can be used to identify needs, provide services, and predict and prevent crises, especially for the benefit of populations.

Evidence-based medicine

Combine and analyze a variety of structured and unstructured data-EMRs, financial and operational data, clinical data, and genomic data to match treatments with outcomes, predict patients at risk for disease or readmission and provide more efficient care.

Genomic analytics

Execute gene sequencing more efficiently and cost effectively and make genomic analysis a part of the regular medical care decision process and the growing patient medical record. Pre-adjudication fraud analysis: Rapidly analyze large numbers of claim requests to reduce fraud, waste and abuse.

Device/remote monitoring

Capture and analyze in real-time large volumes of fast-moving data from in-hospital and in-home devices, for safety monitoring and adverse event prediction.

Patient profile analytics

Apply advanced analytics to patient profiles (e.g., segmentation and predictive modeling) to identify individuals who would benefit from proactive care or lifestyle changes, for example, those patients at risk of developing a specific disease (e.g., diabetes) who would benefit from preventive care.

Predictive analytics will help preventive medicine and public health

With early intervention, many diseases can be prevented or ameliorated. Predictive analytics, particularly within the realm of genomics, will allow primary care physicians to identify at-risk patients within their practice. With that knowledge, patients can make lifestyle changes to avoid risks. As lifestyles change, population disease patterns may dramatically change with resulting savings in medical costs.

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