Abhishek: So Jim what's been the common thread from cyber security to healthcare in terms of analytics and data science? What are the technical and other hurdles that are common between them that allowed you to make that leap?
Jim: So the ability to detect various kinds of events in your data streams, and I'm going to use that term somewhat loosely, detecting events and data streams and then combining them together into patterns is something that happens in cyber security as well as fraud, waste and abuse. So whether you do that by looking at a series of bad claims or a series of services that shouldn't have occurred in sequence is a simple idea.
We can't pull a tooth from a baby. That's a great example of a very simple idea, that sequence of events shouldn't be happening in healthcare, and you can see in a series of network activity the same kinds of events in principle would occur as a series of patterns and as you get good at detecting those kinds of patterns you can apply the same skill and learning about how to deal with the 3Vs of big data, the variety and the velocity and whatnot to really harness this in very high speed fashion.
Abhishek: Right, now not that the cyber security issues aren't important, but are the stakes higher for healthcare, or is the market larger? What's at stake when you point your technology and expertise at the healthcare sector?
Jim: Well, so when you look at the spend that, healthcare is three trillion dollars and climbing rapidly approaching on four, and that's a really big number. It's just hard to imagine how much money there is, but when you look at a lot of experts who say that the amount of abuse and waste and fraud that occurs inside of the healthcare industry, many universities and some, very big think tanks are saying that this number of fraud, waste and abuses and idea to be as high as 750 billion dollars.
So just at the macro level that's a very big number to deal with in the healthcare systems and it's a very, very big challenge because healthcare is so complicated and it's been built out over such a long period of time. It is very difficult to go in and without help of technology to find ways to combat that very large number of fraud, waste and abuse.
Abhishek: And of course there's a great deal of interest in trying to reduce the growth in overall healthcare cost, and this is certainly one way to do it, and then that money ends up coming out of just about everyone's pocket. We all have to pay for insurance in one fashion or another. So that's money that's going to be something we would all benefit from if we take that off the table.
Jim: Right, absolutely and looking at this strictly from the cost perspective is certainly one thing that we should all be worried about, but at the same time I think it's important that we realize that as this initiative unfolds over time, it's not just enough to save money. The quality of care that people are getting in the advocacy of that care needs to go up along with it.
So it's a double challenge that we need to improve the quality of care that everybody's getting and make it more meaningful and individualized but at the same time we have to figure out how to do that as a country we have to figure out how to do that more cheaply, and bringing technology to bear on it is certainly going to be part of the equation going forward. By leveraging advanced analytics techniques such as machine learning, predictive modeling, and data mining, healthcare providers can glean valuable insights from vast amounts of patient data. These insights enable clinicians to make more informed decisions tailored to the unique needs of each individual, ultimately leading to improved outcomes and patient satisfaction.
For example, predictive analytics can help identify patients at high risk of developing certain medical conditions or experiencing adverse events, allowing proactive interventions to prevent or mitigate these risks. Similarly, analytics-driven decision support systems can assist clinicians in selecting the most effective treatments based on a patient's medical history, genetic profile, and other relevant factors, thereby optimizing clinical outcomes while minimizing adverse effects. Moreover, analytics holds the potential to make healthcare delivery more meaningful by promoting a patient-centered approach that prioritizes individual preferences, values, and goals. Through the analysis of patient-generated data, including health records, wearables, and patient-reported outcomes, healthcare providers can gain a deeper understanding of each patient's unique circumstances and preferences. This holistic view enables the customization of care plans and interventions to align with the individual's lifestyle, cultural background, and treatment preferences.
Additionally, analytics can facilitate more effective communication and shared decision-making between patients and providers, empowering patients to actively participate in their own care and promoting a collaborative care model that fosters trust and engagement. Furthermore, analytics has the potential to drive cost efficiencies throughout the healthcare system by optimizing resource allocation, reducing waste, and preventing unnecessary interventions. By analyzing clinical pathways, resource utilization patterns, and operational data, healthcare organizations can identify opportunities for streamlining processes, reducing wait times, and eliminating inefficiencies.
Additionally, predictive analytics can help identify patients who are likely to require costly interventions or readmissions, enabling proactive interventions to prevent complications and reduce healthcare costs in the long term. Furthermore, by leveraging population health analytics, healthcare providers can identify high-risk populations and implement targeted interventions aimed at preventing chronic conditions, reducing hospitalizations, and improving overall health outcomes. In this way, analytics not only improves the quality and individualization of care but also helps make healthcare more affordable and accessible for patients and providers alike.