Hashtags are a grassroots form of semantic data. It is about the community who uses the information labeling and taking control of how it is disseminated. In some ways it can be considered an “inside language” or code by which participation (in sow c…

How about this?

Healthcare Social Media can be defined as the interactive engagement through use of electronic platform(s) for the multi-directional exchange of user-generated information, knowledge, data and wisdom including anecdotal experiences amongst patients, their families, healthcare professionals, health researchers and healthcare administrators.

Schein, R., Wilson, K., Keelen, J. Literature review on the Effectiveness of the Use of Social Media, A Report for Peel Public Health. Brampton, ON: Peel Public Health; 2010.

Do you think we need to include the word “technology” or is this unnecessary because it is either obvious or redundant?

I agree that from a inferential statistics viewpoint, statistical significance cannot be determined until we compare probability (p-value) to our level of significance (Alpha) in order to reject or accept the null hypothesis. While we’ve been monitoring day-to-day operations and business processes for a while using well-established measures of performance, the same does not apply to analyzing and monitoring Social Media yet.

Given that trends keep changing much faster online than they do offline, what is statistically significant today, might not be tomorrow. For example, if we are monitoring the correlation between which platform patients use and a specific topic (e.g. Peptic ulcer), we might one month see a spike in use of one social media platform over the others, and the next month, the percentage of posts are close to equal. This spike might be an indication of a marketing campaign or other factors, and it can be a one time thing.

As for how the numbers will help organizations make decisions, I attempted to do this when I started working on the first draft of the matrix, as it is the primary objective. If you click on the image of the matrix in my blog post (it will open an HTML page) and hover the mouse over the intersections (e.g. Platform/Timestamp), you will read the following in the alt-text:

“Comparing count of users & posts per platform, per time can help us drill down to identify if there are any patterns between the time “hour/day/week/month/quarter/year” of the post and the platform used. If any automated announcements or even manual are planned, they can be better targeted to the community when posted at the right time”

I have attempted to add such information for each correlation of two or more variables in order to clarify how the collected data is to be used to make decisions. What I provided are examples, and I will add more later.

I read your post on Web 3.0 and while I agree that folksonomy should be a consideration, I personally find myself leaning more towards a formal method of standardizing ontologies when it comes to healthcare. Since the information we are gathering here is of great value and effect on people’s health, we need to ensure that the data is collected and classified as accurately as possible, in order to 1) minimize the data collection and analysis time, 2) minimize the margin of error in the gathered data, and 3) justify ROI for our initiatives.

Once again, thank you for your much valued and appreciated comments. I will continue working on the model during my spare time and post updates on my blog whenever I add/modify anything in the matrix.

In the opening paragraph in your comment above you have identified that the results are intended to be used by organizations and identify patients’ tweets as the data source. You will need to think through how analyzing a series of tweets, presumably by persons with an illness, perhaps caregivers and even health care professionals will aid in organizational decision making. Why types of decisions? Policy? Programs and Services?

Tweets by laypersons are more likely to contain organic hashtags in the form of a folksonomy. Having a list populate while writing a tweet could conceivably “force” a patient to use another system (e.g., SNOWMED) that has been defined by others, most likely not patients. You may want to take a look at my blog post, “Why Web 3.0 may be a step backwards from Web 2.0″ that might help explain why I do not think this is a good approach.

I think if you want to be sure that you collect all the tweets by a certain population (including those that use hashtags to identify terms and those that do not) you should query the Twitter firehose using their streaming API directly. Anything else would mean that your sample could be inadvertently tainted.

I hope these comments and suggestions are of value. Your ideas are interesting and this work could be very useful in many ways.

1- I guess the primary objective of this initiative is to figure out a comprehensive “conceptual” statistical model, which health care organizations can use to evaluate their social media engagement and make decisions accordingly, through relying on indicators with true statistical significance. Again, this is intended to be for ongoing (monthly, quarterly, yearly) evaluation. So far I haven’t done any real research, but rather been brainstorming, and relying on my past work experience. The results were the two blog posts you mentioned and one before them titled: “Business Process Mapping, A Detailed Model” which focused more on the organization’s internal workflow and quality.

2- The users of the system are the trickiest part here. I used sharepoint as an example if a health care organization choses to build its custom SM tool. But we all know that patients use their preferred SM portals (Facebook, Twitter, Imedix, etc). In order to truly capture and analyze all the information provided by, say for example, diabetic patients, we need to figure out a way of collecting all the posts about diabetes from all these different platforms, and either convert them into a homogenous format that we can then analyze, or analyze the data in each platform individually, then aggregating it to get the bigger picture. Either way, the model I’m working on is intended to be high level, with the assumption that data can be collected in the way we want it. Automating hashtags was a part of this, focusing on how to help patients and the community use the proper hashtags to help us in properly categorizing and analyzing all the health data on twitter.

3- Again, SharePoint was used as an example; we have to consider a unique and applicable way for each major platform on how to filter and collect the data we want. We always run the risk of users using non-standard abbreviations or misspelling, and that’s a part of the margin of error that we have to accept.

The very first post in my blog is a research paper I’ve done a few months back on the potential for Semantic Web in Health Care. I referred to SNOMED in step 3, but given that we are collecting data from patients as well as healthcare practitioners, the terminology used might differ. It even differs between physicians and ICD. One example of physicians refusing to use ICD (task assigned to the coders to do) was for the diagnosis “Retinoblastoma”. In ICD 10-AM (the standard we use in the middle east), it is Malignant Neoplasm of the Retina.
I believe ICD is useful as the end result, as in, once we identify the data gathered, converting to ICD will add value for processing, but it is not reliable for collection.

I’ve lead an HL7 integration project in the past, and it is useful only when we fully have control over the databases. But for my purpose here, which is, gathering data from Social Media, something like Message A01 or A03 are irrelevant. I have consulted with a friend of mine who works in AI and she recommended reading about NLP, specifically semantic clustering and semantic distances. The challenge here is that I’m not a programmer, so I’m trying to focus on the overall Matrix, and benefits of correlating different indicators to influence decision making.

This undertaking is neither a thesis nor an official project. It’s just something I’m doing during my spare time for fun, or at least that’s what it is at the moment.

I quickly read through the “Social Semantic Web” when I was doing my research a few months back, but never got the chance to read the whole thing in detail. Looking forward to it, and looking forward to your feedback.