SaaS cuts down on sick visits

onically, seeking out healthcare can actually spread germs contained in a room packed with patients. If you've recently been in such a situation, and been forced to wait an hour or even longer to be seen, you might have thought, as I did, "there has to be a better way." There is, thanks to SaaS.

SaaS makes it possible for patients to use any web-enabled device to access convenient and affordable healthcare. It does not just give information like you'd find on WebMD, but a  personalized diagnosis and even a prescription--if warranted--at a cost that is just slightly higher than standard insurance copayments

Minneapolis-based Zipnosis is the company that developed the SaaS platform. It promises "online diagnosis and treatment in minutes" for $25. 

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SaaS Replaces Sick Visits

Data for doctors: should there be limits on it?

This summer, Carolinas HealthCare System made the news rounds as a warning of the new levels of data mining available to healthcare companies. In Hospitals Are Mining Patients' Credit Card Data to Predict Who Will Get Sick, we get a very Big Brother type of picture of the invasiveness of such data mining with an illustrative picture showing a doctor saying, “Don’t lie to me, Susan, I know about the 2 a.m. Papa John’s deliveries.”

 It makes for dramatic copy, but it’s still in the realm of fiction rather than fact, as I found our when  I contacted Carolinas HealthCare and got a response from Jason Schneider, Director, Clinical PR. He explained that the article “focused on how providers could use data for in the future and didn't include details what data we are currently using and how we are using it.”

The data they are currently using does not follow an individual’s consumer trail but looks at things like socio-economic circles, neighborhood limitations, and cultural affiliation that could shape one’s access to healthcare. One example of that was identifying why patients in one particular area were not coming in for regular doctor’s visits. It turned out that it didn’t have reliable public transportation to a doctor's office. After identifying the geographic problem, Carolinas HealthCare set up a doctor in the neighborhood itself.

As the person quoted in each of the articles on Carolinas use of data is Dr.  Michael Dulin, chief clinical officer for analytics and outcomes research at Carolinas, I contacted him and spoke with him on the phone. He explained that Carolinas has a decade of experience in using data to improve healthcare by identifying individuals within contexts that could pose obstacles to care.

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More Info in the Name of Better Healthcare

RX for Prescription Errors: Big Data Analytics

We’re all familiar with the stereotype of the doctor with bad handwriting whose prescriptions are all but illegible. While the use of electronic medical records (EMR) eliminates that chicken-scratch problem, it unfortunately doesn't fix other, often fatal, errors.

(Source: Carbon Arc)

In fact, EMR's introduction has made “new and alarming types of errors that didn't exist in manual records” creep up, says Dr. Gidi Stein, co-founder and CEO of MedAware, a startup using big-data analytics to combat the problem of prescription errors. Automatic selections are faster and neater than individual writing, but they also make it possible for doctors to put in errors they wouldn't have made on their own.

As reported in "Medical Errors: a Report by the Staff of U.S. Senator Barbara Boxer" (available in PDF format), prescription errors account for many of the 210,000 to 440,000 deaths in America each year that result from "medical errors and other preventable harm at hospitals.” 

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Analytics Startup Prescribes Fix for RX Errors

Big data, analytics, and insight into health

Mobile phones coupled with apps make it possible for people to hold the key to their own health information in their hands at all times. There are major ramifications for this possibility.

  Healthcare Analysis: Doctor vs. Device explores the position that doctors are becoming obsolete, replaced by more timely feedback from monitoring devices.

IFighting Heart Disease With Big Data  looks at the Health eHeart plan to collect health data of a million people over ten years in an effort to learn more about heart disease, the leading cause of death in America.  

Big Data Health Hazards

If anything can go wrong, it will." Murphy's Law (or Sod's Law, as it is known in the UK) applies to big data projects, as well. When those projects concern someone's health, something going wrong in the data can lead to something going very wrong with the patient.

The more one relies on the accuracy of the system, the higher the potential for error. Electronic health records (EHRs) are considered a boon to data aggregation, but they hold a potential downside.  Read more here