Social Value of Online Communities

Social media might be taking precedence in our lives these days, but there is another form of online communication that we used to (and for some of us still) rely on for exchanging knowledge or receiving emotional support from strangers – the online forums or bulletin boards. For example, Stack Overflow features a Q&A platform for software developers to exchange coding knowledge. Slickdeals, a deal and promotion-sharing forum, on the other hand, has a typical forum structure that has threads, original posters, and responses to those original posters.

Various streams of research have been conducted on online communities, from motivations behind participants in contributing time and knowledge in helping strangers, to the economic value of such contribution. What is lacking is the social value of online communities. Scholars Goh, Gordon and Agarwal (2016) aim to bridge this gap by looking at how an online community addresses the health disparity of rural and urban populations. They are also the first to quantify the social value of online communities.

Their assumptions are as follows: there is limited access to resources such as specialized care, information, healthcare programs, and social support groups in rural areas, which creates significant disadvantages for rural patients. Therefore, rural patients tend to have decreased health status and health functioning, possess less health knowledge, and have lower health-seeking skills, beliefs and self-efficacy. Together with other health capability gaps, rural patients are more likely to have poorer health statuses and higher mortality rates than urban patients. Given rural patients’ disadvantage relative to the urban patients, these researchers suggest that online communities can reduce the health capabilities gap experienced by rural patients by enabling the exchange of social support, in the form of both health information exchange and emotional support. Moreover, to the degree that community interaction has a relatively more positive effect for rural patients, they hypothesize that online communities generate social value by reducing rural–urban health disparities.

To prove their hypothesis, they collected message data on a rare disease online forum posted by 111 rural patients and 527 urban patients from October 2005 through June 2009. They adopted a network methodology in studying the knowledge and emotional exchange among original posters and responses. To illustrate, each node in the network represents a patient who participated in the forum. There is directionality associated with support provisions such that a supportive tie between a patient who posts a thread and a response from another patient is represented by a directed dyadic tie, where the arrow points toward the originating poster and the arrow head terminating at the recipient (e.g., a patient whose initial post generates a reply in the thread would have a tie that is directed toward her).  If their hypothesis is correct, it should show that the rural nodes are more likely to be recipients and urban nodes are more likely to be providers of social support.

Their findings suggest the following: the probability of a node with an incoming tie is 7 percent higher for a rural node as compared to an urban node. In other words, all else equal, a rural patient is more likely to receive support compared to their urban counterparts. They also find that rural patients are less likely than their urban counterparts to provide support.

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The research suggests that support online flows in one direction

Taken together, these results show that the likelihood of an urban patient responding to a rural patient is higher than the likelihood of responding to another urban patient, all else equal, and therefore providing support for the claim that there is a net surplus of social support flowing from urban to rural users.

Their results yield implications for policy makers and practitioners concerned with meeting patient needs and overcoming disparities in medical access. Entities responsible for resource allocation decisions, such as governments, community agencies, and public health facilities should leverage the powerful role that online collectives can play. Online communities can serve as a low cost alternative to or as a complement to existing health programs. For instance, healthcare entities can have professional nurses or doctors participate in these communities by providing information in addition to regular patients. Such information shouldn’t replace necessary office visits. Rather, it can guide the patients in the right direction and serve as a conduit towards further examination.

 

Reference:

Goh, Jie Mein; Gao, Guodong (Gordon); and Agarwal, Ritu. 2016. “The Creation of Social Value: Can an Online Health Community Reduce Rural-Urban Health Disparities?” MIS Quarterly, (40: 1) pp.247-263.

Disclaimer: This Blog is for educational purposes only as well as to provide general information and a general understanding of the topics discussed.  The Blog should not be used as a substitute for legal advice and you are advised to seek additional information from your insurance carriers, Medicare and/or Medicaid agencies for additional criteria and regulations regarding these services.

Bananas and Peanut Butter

Apparently, there was an overwhelming response to my first blog so I get to write another one. Thanks again to both of you who read it.

Given all the doom and gloom about AI in the media recently, in this episode I’d like to talk (write) about some of the positive things going on with a branch of AI known as “deep learning”. But first, a little history. Way back in ancient times when computers were just getting started in the ‘60s, while some scientists worked to perfect the lava lamp, others invented something called a neural network. Neural networks were loosely modeled after how they thought the brain worked back then, with an input and output and in between hidden layers of artificial neurons.

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Like a brain without the squishy parts.

Neural networks were really good at solving a lot of problems, but unfortunately were also similar to grilled cheese sandwiches (stay with me here). As problems became more complex, the neural networks required more hidden layers to find a solution. However, after more than about two layers, they wouldn’t solve the problem and generally led to a mess that someone had to clean up instead of the cheesy goodness they were hoping for.

This was pretty much the situation until 2009 when Geoffrey Hinton and his team at the University of Toronto figured out that by tweaking the layers ahead of time, they could overcome this problem and voila!, peanut butter and banana tall stack.

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Hungry yet?

These systems with many layers were called deep neural networks and could be used to solve much more complex problems and without any cheese being scraped off the ceiling. Fast forward to today and deep learning is being implemented everywhere, including the voice and image recognition done by Apple, Google, and Amazon. It’s also the foundation of self-driving cars.

 

Now rewind back to last year.

I met Andrew Beck at a conference in DC. Andrew has an MD from Brown and a PhD from Stanford. He has started three successful companies and is also an associate professor at Harvard (you know, the kind of person you secretly hope has some awful dark secret like a third foot or something). Dr. Beck also appeared to be a genuinely nice guy (unfortunately) as he presented some research he had done on pathology. The first set of research presented was done to determine which factors were most important in getting accurate pathology results.

Care to take a guess? Anyone? Type of equipment? Experience of the pathologist? Day of the week?

Who said “day of the week”? Ding! Ding! Ding! You win! You win, that is, unless your sample hits the pathologist’s desk first thing Monday morning, then not so much. It turns out, pathology labs are really busy on Mondays and Tuesdays where a pathologist may have upwards of a thousand slides to review in one day. By Wednesday, however, things have slowed down to the point that you can get a more reliable analysis.

For those not sure how important this is, it’s really, really important. The pathology results can determine the next steps in care ranging from no treatment at all to aggressive cancer treatment. I’m not authorized to dispense medical advice, but as your friend, if you need any tests done, I suggest they occur after taco Tuesday.

So back to Andrew Beck and team (oh, and before I forget: he only had the two feet, as far as I could tell anyway). They went to work on the problem with a deep learning AI. After fine-tuning the system, they compared it against an actual pathologist using samples with known results. The AI had an error rate of 7.5%. The pathologist, taking his time, achieved an error rate of 3.5%.

So good news: people are still better at some stuff, right? Sort of, but the better news was that working together, the AI and pathologist reduced the error rate to 0.5%. It became apparent that in pathology, people and computers make different types of mistakes, and by combining them, the accuracy of both can be improved. The whole process could also be sped up with the AI identifying features on the slide that the pathologist should focus on versus taking the time to review the entire sample.

Now fast forward to a few weeks ago.

I can’t be more specific because I don’t know when you’re reading this. If it’s 2275, then it’s obviously been more than a few weeks, and I’m more concerned that my words are the ones that posterity has passed on to a future society and suspect that your utopia is consequently in serious danger of collapse. Anyway, I had never heard of Taryn Southern until a few weeks ago (in 2017). According to my daughter, who is an expert in such things, Taryn is a YouTube personality who was on American Idol and is now also a pop singer. Taryn’s new album (or whatever you call them now) was just released and the single “Break Free” is doing well.

I’m sure you’re beginning to wonder how this is relevant. Trust me, it is. Taryn’s new album was the first one where the entire album was written and produced by an AI. Taryn found working with the AI in most cases to be preferable to working with human collaborators. She also said it sped up the creative process 20-fold.

So, yes, finally, we have the technology to streamline the creation of pop songs.

I’m running out of time, so, to sum up: from pathology to top 40, humans and AI working together are discovering new and better ways of doing things, helping us find new approaches to old problems.

So remember: the next time you hear that AI is going to destroy mankind, it might someday. But more optimistically, AI may also be responsible for getting you a more accurate diagnosis or producing that new hit song you can’t get out of your head.

If you have any questions about AI or machine learning, or need a good grilled cheese recipe, drop me a line.

Thanks!

If You Bundle… They Will Save

Bundled payments aren’t just a way to save on auto and home insurance but are potentially an effective method to save on healthcare costs… unless they aren’t.

Under the MACRA legislation, CMS’s Center for Medicare and Medicaid Innovation (CMMI) created a number of rules in an effort to move Medicare providers into a more quality-centered payment model. One part of this endeavor was the creation of Episode Payment Models (EPM) and the Cardiac Rehabilitation (CR) Incentive Payment Model. These episodic models were intended to reduce the overall cost of some procedures that required significant time in a hospital to complete treatment. For example, surgery to treat a hip fracture and all of the care following that surgery would be paid by Medicare at the same rate regardless of the volume or types of procedures necessary to complete treatment. The CR incentive program was designed to provide incentive payments based on the Medicare beneficiary use of cardiac services in the first few months following a heart attack or coronary bypass surgery.

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Photo credit: imperix.ch

The idea is to fix the cost of all procedures and tests together in one convenient bundle in order standardize the cost and encourage hospitals to increase coordination of care. The result would be an increase in quality of care and a reduction in overall cost.

The final rules for Medicare bundled payment programs were published in the Federal Register on December 20, 2016 and mandatory participation in the programs was slated to begin on July 1, 2017.  The models were held from starting on July 1st and placed on hold while the Department of Health and Human Services could reevaluate the program. Fast forward to 3 weeks ago on August 15, 2017 and CMS published a proposed rule that cancels the EPM and CR programs completely. Citing a hope to increase future voluntary participation, CMS was stopping the bundled payment programs as they were required to be mandatory.

I wanted to bring this up not only because it is relevant to VBH but more so because this is a change occurring right now. The world surrounding VBH is fluid and dynamic. There are ebbs and flows in policy however, VBH has made steady progress toward full realization over the past couple decades. While it doesn’t matter what stance you take with regard to policy changes of this kind it is important to remember that it will take significant investment from providers, hospitals and payers for VBH to take hold as the predominant model for healthcare payment. Resistance to change is usually a given and the more stakeholders that are satisfied and open to a change now, the less likely there will be resistance in the future.

This turn of events might be considered a step backward but, bundled payments are only a small part of the larger whole. In fact not all of CMS’s bundled models were cancelled. The Comprehensive Care for Joint Replacement (CCJR) model was left mostly intact. The geographic regions required to participate in the program were decreased and only about half of the total overall hospitals intended to participate remain in the program.  I also don’t believe that bundled payments will go away anytime soon. They are a relatively straightforward way to drive change when it comes to major hospital-centric procedures

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