JOIN US: This is a chat-based conversation about advancements in next-generation health-care IT, including new collaboration tools, AI and telemedicine.
A2: There is a lot of data in EHRs that isn't being used to filter and cluster patients into the right cohorts. For example as a South Asian male my risk for cardiac disease can be assessed a lot better by looking at a like cohort and using historical data to assess specific risk
A2: I find that my clients have the best success in undergoing compliance audits when they have systems that interoperate, providing more control over their environments.
A2: For example, having Crowdstrike next-gen anti-malware feed into Splunk for easy reporting allows for better control. This can provide easy reporting and actionable items.
A2: Great question! In most modern systems, such as Splunk, report generation is extremely customizable. You can pick what fields you want to include on reports. Most systems allow for alert notifications on your risk tolerance levels.
Sidebar comment: AI and ML functionality is pervading all types of applications, and it seems that health-care is a hugely important sector for it. We at eWEEK expect to see more and more use cases involving AI and ML in this sector.
I've seem AI and ML used in my clients to get better control of data. Such as, helping with a patient's next best steps, or discovering additional money from Medicare by finding a patient treated for an illness at another hospital group.
Hi Chris! Thanks for having me on today! My name is Mike Seegel, Manager at Schellman and Co. I mostly focus on HITRUST assessments for our clients, but I'm also involved in many other healthcare and compliance audits, such as HIPAA assessments.
.@editingwhiz A1: I'm mostly excited for ML to really help us understand patient's intent in healthcare -- what do they want to achieve out of their HC experience. I think one of the powers of ML is to understand what a patient means not just says to provide equitable care
1. Increasing Access: Providing greater access to high quality care to more patients through resource optimization (similar to load factors in airlines). Simultaneously deliver high utilization of assets, low wait times for patients, and a large number of available slots for pati
2. Lowering costs e.g. claims adjudication, staffing, other back office functions. AI can minimize time spent on routine, administrative tasks, which can take up to 70 percent of a healthcare practitioner’s time.
3. Clinical applications of AI : Diagnostic applications, such as reading imaging scans or pathology reports, are probably one of the most intuitively obvious places where we would expect to see AI getting deployed at scale in the near to medium term.
4. Targeted Precision medicine: for higher efficacy drugs that act on my specific symptoms. There is so much patient specific data that can be used to identify “who am I like” from the thousands who may resemble me
