Wednesday, January 22, 2014

Social Media & Clinical Workflows 2

I have previously written about the integration of public social media into clinical workflows in order to improve the engagement of patients in collaborating with their providers to make healthcare decisions. In that post (http://posttechnical.blogspot.com/2013/12/clinical-workflow-other-arcane-rituals.html), I said that the inclusion of information from public social media such as Facebook, YouTube, G+, Twitter etc. into clinical workflows might provide important information not otherwise available to the clinician & also might serve to more deeply engage the patient in clinical decision making as information that they independently provided would be part of the decision process. I wrote in general about how this might work – here’s a more detailed (& nuanced) view.

Providers are very protective of their workflows, especially workflows that involve patient interaction. In the past several years, these workflows have had to change substantially if a provider is going to qualify for meaningful use, that is specific use of electronic health records in order to qualify for higher reimbursements from CMS (the Centers for Medicare & Medicaid Services, HHS). Most providers I have talked to recently are not interested in more workflow changes, especially if they are not going to be paid for them. There is, however, one motivating factor that can change this - improving patient outcomes. If providers could be convinced that including information from external sources, such as public social media, in their patient interaction & diagnosis workflows, then it might be possible make this integration work productively, but several principles would have to be followed:
  •        Change the current EHR-based workflow as little as possible
  •      Maintain the information from the external source separately from the EHR so that the EHR workflow can still be executed as currently
  •      Provide information that is either unique & relevant to the diagnosis & treatment, or enhances information already available through the EHR.

I’ll describe a possible workflow scenario that follows these principles in a little, but first…

I also described the criteria for collaboration in that previous post. If the patient is going to be re-engaged in making medical decisions with their provider, there has to be a model for how to do this. As I stated previously, that model is not collaboration-based because the criteria for collaboration: shared goal structure, similar reward structure & symmetry in knowledge or resources, are not met by the patient-provider interaction. The model for this interaction is shared decision making, so an additional principle for this workflow change is to provide the context for shared decision making by the patient & provider.
The principles of shared decision making in the medical context are well documented[1] & include at least:    
  • relationship building between patient & provider,
  •        introducing choice with respect to treatment
    •     offering options
    •     deferring decision
  •      discussing & deciding on a treatment plan.
    •    Risk-reward trade-offs
    •      Use of decision aids (paper-based, electronic etc.) for education
    •       Elicit preferences
    •      Discuss preferences
    •      Reach consensus

Shared decision making is most appropriate in situations where there are several possible treatments with somewhat similar side effects, costs & outcomes (if there is one most effective treatment, the need for shared decision making, but not for transparency, is moot). This process only works if the decision-making effort is mutual, that is both the patient & provider contribute to the trade-off, preferences & decision discussions.

So where are we? I’m maintaining that the integration of information from public social media can be an important contribution to clinical workflows for two reasons: it may provide unique & important information to the diagnosis & treatment process that has not been previously available & it may serve to more deeply engage patients in their healthcare decisions because information provided by them is being used by their provider(s). I’ve also stated that shared decision making is the most effective model for structuring the provider-patient interaction. How might this actually work?

The most common provider workflow for patient interaction today is provider by the electronic health record system in use. There is no current provision for integrating external data into this workflow (other than lab, pharmacy & other provider related data), & there is little motivation by either providers or vendors to provide such integration. There is, however as previously noted, a lever to motivate providers – better outcomes. The provider (or one of their staff) is previewing the patient’s records prior to a scheduled encounter. The system informs the reviewer that additional information is available from an identified source. This source could be a PHR or a social media stream that the patient has posted personal health information to. The information may be a post from the previous week where the patient described feeling sick or even an image of a skin rash or other physical symptom. As already stated, this information is in a separate stream from the EHR data & is not moved from its source unless the reviewer requests it. The reviewer can look at the information, decide if it is relevant, & choose to include it in the overall data package to be available during the encounter. If the reviewer chooses not to include it, a notification is included that external information from specified sources was reviewed but not included & the provider has the option to look at it during the encounter. If the information is included, it is presented as a separate stream at the beginning of the workflow so as not to alter the rest of the encounter. It is also stored separately & tagged as part of a specific encounter, so that it can be recovered as part of the encounter, but is not managed as part of the EHR data. This could change over time as such information becomes more accepted & important to diagnosis & treatment.

There are many other ways that information from these external sources could be integrated into provider workflows. They could be part of the practice management workflow when the appointment is set up, but someone on the clinical staff would have to review them & the above model might have to be followed at the time of the encounter so that the most current information is available. Other models would also be developed through experience as such information is used.

Certain types of information related to specific practices may be more relevant initially than others. These information types might include:
  •         Social media dialogs that reveal a patient’s expectations, attitudes & personal limitations with respect to their healthcare
  •     Social media dialogs relevant behavioral healthcare
  •     Dermatological or trauma images taken at the time of injury or presentation.

The types of information seen as relevant & effective will expand as this data is used & accepted more.

Of course, there are issues & constraints related to this model as well. Right up front is the status of using this type of data for diagnosis & treatment. If a provider were simply reading a patient’s social media stream & attempting to treat them on that basis, it appears that they would be in violation of the HIPAA statutes. I believe that if a provider had consent from the patient (as with any other personal health information) to use such information, the HIPAA guidelines would be met (but I could be wrong). Then there is the question of what constitutes such information. If the relevant or interesting content is part of a stream that other people are participating in, then the consent is only good for the patient & not the other participants, however the most interesting content might be in the interaction that is lost if only a single person’s contributions are captured. What about individual postings that have comments from other people, like an image that a patient wants feedback on? There are many such issues.

Finally, there is the issue that no systems capable of providing this type of function are available currently in healthcare. All of the capabilities are present in other types of systems, but someone would have to build a prototype & pilot this type of use. Who would do this? Health & Human Services (ONC)? Commerce? the DoD? the VA? Kaiser Permanente? Partners? Hopefully someone would. I’m trying to start a pilot (through the RCHN Community Health Foundation) to at least determine people’s attitudes about the use of social media content in clinical workflows. Stay tuned.

Coming next:
  •  A deeper look at analytics in healthcare
  • Some opinions on current trends
  • &, the Future might even make another guest appearance.


Please also see my writing for the RCHN Community Health Foundation at: http://www.rchnfoundation.org/?page_id=484





[1] c.f - Makoul G, Clayman ML. 2006. An Integrative Model of Shared Decision Making in Medical Encounters. Patient Educ Couns.60:301–12. or Elwyn, G. et al. 2012. Shared Decision Making: A Model for Clinical Practice. J.Gen’l. Int. Med. 27(10) 1361-67,

Wednesday, January 15, 2014

Search, Analytics & Healthcare

I’ve written several posts now about the importance of analytics in healthcare & how healthcare organizations, especially those with constrained resources, can move towards the use of analytics without a large amount of expense (except in effort). I’ve also written a little about what the analysis of available &/or large-scale data sets might consist of & how it can be used to provide leverage for such organizations. Now I’d like to write about the evolution of analytics & how search has also evolved in parallel, & what the implications of this might be for healthcare.

Some time ago, I wrote an essay titled “If Search is the Answer”[1]. In it, I proposed that search was not only an important functional capability in our current & near-future work lives, but that it actually was the principle around which our work was organized. Now it appears that our use of constantly connected devices is resulting in our work lives & our lives increasingly merging & that search has become an important, if not the important, organizing principle in general for us. Search is much more than typing some keywords into Google or Bing, etc. It really spans a range of capabilities that includes not only naïve searches, but also semantic searches of all kinds. The endpoint of the search range, at this time, is analytic query, that is, the posing of questions that require quantitative or semantic analysis, or both of a body of information. This body of information has grown so that we might be talking about gigabytes (109 bytes) to petabytes (1015 bytes) of things such as healthcare records, financial models, academic publications etc.

Let’s look at two different examples of search evolution – the first is Facebook Graph Search. Facebook has always provided search for people based on names, profiles etc. Graph search is different in two ways: first, it utilizes a semantic engine that allows natural language queries & evaluates these queries to be able to use both the exact meanings & interpretations of the meanings of the words used, & second, it uses the structure of the semantic graph built by the underlying Facebook engine so that it understands not only the content of user profiles, but the relationships of that profile with other user’s profiles. It returns results from both within Facebook & from the web, based on results from Bing (Microsoft) & now also from Russian search engine Yandex (http://www.yandex.com/). Of course, it only has a semantic graph (today) from Facebook content. Sample queries could be such requests as “find the pictures of all of my friends who visited San Francisco this year” or “find people who liked the movie Fruitvale Station & live in Oakland”. Semantic search is not new; the concepts were first developed by Alan Collins & M. Ross Quillian (both then at BBN Technologies) & enhanced by many people mainly working in advanced database query. What’s different about Facebook graph search is the reach that it has; Facebook has 1.2B monthly users.

The second example is IBM Watson. Watson is a cognitive system that is a good deal more than what was exhibited on Jeopardy. Watson is a reasoning system that performs not only semantic analysis of natural language, but also hypothesis generation for answering questions, evaluation of potential responses & synthesis of a “best” response. It uses large amounts of information & is designed to be able to evaluate petabyte level information sources in order to generate hypotheses & potential solutions.  It ranks these solutions for presentation, & it remembers the hypotheses it previously generated & how successful they were for specific queries. It uses this information to optimize how it answers similar queries, thereby “learning” from experience. One relevant example query might be “Find all the patients with similar medical profiles & diagnoses & rank the success of the treatment they received from most to least successful”.

OK – so what about healthcare? Search will continue to evolve toward more & more connected search; that is search organized in some way such as relationships in a social network or relationships in a collection medical records etc. Whether that connection is defined by parameterized graphs (as in Facebook Graph Search) or by semantic query interpretation with hypothesis generation & experienced-based learning (as in IBM Watson), near-future search provide a way of using our own concepts & needs to organize & generate knowledge from large bodies of information. Healthcare analytics can be thought of as a kind of search. I have recently been involved with a project that sought to determine the cost per medical encounter classified by service category (medical, dental, behavioral, enabling, ancillary, etc.) at a number of Community Health Centers. This analysis could be expressed as an analytic query; in fact most analyses could be expressed as analytic queries & could be posed to systems such as Watson, ParAccel Analytics Platform or any of the Hadoop-based analytic packages. The accuracy & validity of the answers would depend on a number of factors including (at least): the quantity of the information available, the quality of the information available, the ability to express the query appropriately in the system, the ability of the system to interpret the query appropriately & the ability of the system to present the results in an understandable way. If we specialize the query we specified earlier to “find all the patients with the diagnosis of non-Hodgkin’s lymphoma expressed in the skull, characterize their symptoms for similarity & rank the success of their treatment from most to least successful”, we’ll understand that the results might be different if we had 750,000 patient records (a Health Center Controlled Network) to analyze than if we had 9,000,000 patient records (Kaiser Southern California). What if we could analyze even larger numbers of records? How good could our results be? Let’s remember that quantity does always result in quality & the results that we get are only as good as the questions we ask. For specific clinical queries, though, we can get very good results, good enough that we can find treatments that would not be obvious or even identifiable by other means except serendipitously. Good enough, also, that we can determine that we’re asking the wrong questions. This type of diagnosis & treatment planning is in the future (the relatively near-future) for most clinicians, but somewhat less ambitious queries can be done today in administrative & financial as well as clinical areas.

The evolution of search in terms of the types of systems that can be queried is leading to an evolution in how we use administrative, financial & clinical information in healthcare. As search is increasingly organized around concepts that reflect relationships in the real world, it will become possible to ask questions that provide answers some of the most complex issues we face such as improving clinical diagnosis & treatment. In parallel, as the tools we use for search become more powerful, but with easier to use “query interfaces”, asking these questions, & productively applying the results will become easier & easier. Search, & the attendant concept of discovery, is increasingly becoming the organizing principle for much of our work in healthcare.




[1] The title is a homage to Danny Bobrow’s 1985 paper If Prolog is the Answer, What’s the Question” IEEE Trans Softw. Eng. 11(11) – perhaps the most insightful paper on the logic of AI languages ever published, with the possible exception of Doug Lenat’s paper on why AM worked (Lenat, D.B. & J.S. Brown. 1984. Why AM and Eurisko appear to work. AI 23(3):269-294.) My essay at http://posttechnical.com/?page_id=58

Monday, January 6, 2014

A Path to Analytics



Everyone agrees that “analytics” are/will be important for healthcare organizations, especially organizations with limited &/or constrained resources such as community health centers (CHCs), acute care hospitals, many rural hospitals & clinics of all kinds.  These organizations are facing many changes currently & are in the process of evolving to new organizational (participants in HIEs, ACOs, HCCNs etc.) & sustainability (service providers, data providers) models. What this means & how to do it are hotly discussed topics, however, with no apparent tactic or strategy that seems feasible. There is no big bang in this effort. This capability will not spring forth complete & productive if such organizations make the correct invocation or even spend a large amount of money. There does, however appear to be a set of steps that could provide an actual path for healthcare organizations with limited resources to follow to begin to productively use analytics & to evolve a more & more effective capability in this area. For simplicity, I’ll use CHCs as the example as I know them best.

So first, let’s define our terms: what are analytics, and why are they important? In this context analytics are essentially the extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling to evaluate healthcare performance. In the health center context, UDS is a form of analytics as are the calculations done for sliding scale determinations, analysis of administrative and demographic data to improve the effectiveness of scheduling, and the analysis of cost data. Analytics are important not only within the health center for planning, quality improvement & clinical programs, but to help the center evaluate and engage in activities in the broader healthcare landscape.  Analytics are important for planning community health initiatives and contributing to public health programs, and they are essential for participation in health insurance exchanges and ACOs. Finally, analytics applied to clinical data can lead to better diagnosis, more effective treatment, enhanced care coordination and improved outcomes. Analytics, simply, is the systematic analysis of data focused on answering a specific question. It’s not magic; many healthcare organizations are already doing (some of) it.

Analysis of analytic procedures used by health centers & other resource-constrained organizations show the following common elements:[1]
·      use of currently available data, primarily directly from the EHR , but also from data aggregates, often provided by PCAs, networks, HIEs etc.
·      use of relatively simple analysis and visualization techniques, mainly through the EHR or the database that stores the aggregates (usually MySQL, MS SQL Server or Oracle, but in some cases MS Excel)
·      the development of simple linear models, such as analysis of variance, analysis of covariance, various types of regression, etc.,  or other straightforward statistical models done through the database, through MS Excel or less often through an analysis package (like SAS) or a business intelligence tool
·      and, most importantly, asking the right questions, that is, ones that are important for administrative and clinical operations and planning, .
Complex analytics, multi-layered analytics and highly designed data warehouses may not be initially necessary, at least for many questions, and moreover, not appropriate if the questions that are asked aren’t relevant or don’t require them and the underlying data isn’t complete and reliable. Recent experience with a data warehouse & analytics effort performed by a PCA & a contracted entity to perform the analysis have shown some of the issues with this approach including:
o   needing to hand select & normalize data due to the lack of ETL (extract, transform, load) activity (with the inevitable subjective component)
o   use of cumbersome or inappropriate tools for data organization & analysis
o   lack of skill in analytic planning & performance[S1] 

These issues can be addressed, & these common elements can be transformed into a program to evolve a path to analytics that can be piloted & then followed by health centers. The primary aspects of  the proposed “Path to Analytics” program would be[S2] :
1.     Developing a landscape analysis:
1.1.   what is being done within the organization already & by CHCs, PCAs, HCCNs, ACOs, HIEs & organizations also by with extensive resources such as Kaiser Permanente, Partners Healthcare, The Mayo Clinic, Geisinger Health System etc.
1.2.   landscape analysis is essential for developing context & particularly to have a range of examples for what similar organizations are already doing & what more advanced organizations show is possible
1.3.   this analysis can be done as a dialog with other organizations & can surface potential collaboration efforts as well as analysis examples
2.     Initiate a training program for analysis planning & analysis operations: what question(s) to ask, how to address them analytically, what tools to use, setting goals & metrics
2.1.   this training is essential for developing the skills not just in performing analytics & interpreting results, but also in how to develop relevant analytic hypotheses
2.2.   much training material is available online, especially from open source vendors
2.3.   commercial vendors & consultants may also provide low-cost or free training for other considerations (use in advertising, recommendations for additional work, etc.)
3.     Perform analytics planning: what question(s) to ask? how to ask them? what tools are compatible with answering them? 
3.1.   Selection of infrastructure & analytic tools as well as consultants &/or vendor partners if appropriate
3.1.1.  infrastructure tools include data & information storage & management applications as well as import/export & normalization applications
3.1.2. analytic tools include actual analysis packages & front end applications for query building & presentation of results
3.1.3. organization & storage of data: evaluation of current storage mechanisms & analysis of movement to contemporary mechanisms (Hadoop, etc.), extraction of data from clinical & administrative systems (PM, EHR, Labs, Registries, etc.), ETL & normalization of data as necessary
3.1.3.1.substantial amounts of data/information in healthcare is stored in databases underlying clinical & administrative applications such as practice management, EHR, lab & registry applications. Most of these have relational databases as the underlying data storage medium. Decisions will have to be made whether to maintain this storage infrastructure or move to applications that are not datatype dependent & can manage & search very large amounts of data.
3.1.3.2.If relational or other structured mechanisms are maintained, extraction of data may include substantial ETL & normalization efforts.
3.2.   Contemporary analytic solutions that may not require data warehousing & ETL:
3.2.1.  Hadoop & Hadoop-like solutions (open source file systems)
3.2.1.1.Disco (standard file system, Python for query & reduce, Nokia Research)
3.2.1.2.Spark (in-memory query, UC Berkeley)
3.2.1.3.Storm (real-time, i.e. not batch like Hadoop, Twitter)
3.2.1.4.HPCC (separate ecosystem: Thor, Roxie, Pig,… LexisNexis)
3.2.1.5.Etc.
3.2.2. Select question(s) for initial analysis
3.2.3. Plan extraction (if necessary), query strategy, data analysis & visualization/reporting
3.2.4. Create & review analysis plan
4.     Perform analysis & interpretation: operation of analytic process
4.1.   carrying out the analytics plan, this may require several iterations of the analysis
5.     Begin outreach & communication: How to convey results? Who to tell? Communication process, etc.

The endstate of this plan will be as follows:
·      One  or more of the organization’s sites will have completed the full life cycle of an analytics project that will be strategic/productive for them.
·      Their personnel will have worked, potentially with an innovative vendor & expert consultants in this area, on the issue/problem identification, analytic planning, analytic performance, interpretation & evaluation & results dissemination phases of an analytics project.
·      This project will have been specifically designed to use current (& possibly appropriate vendor-contributed) resources, so that subsequent projects will be feasible & more easily done given this experience.
o   Appropriate resources in this sense are contributed resources that use an organization’s existing hardware & data management/aggregation capabilities (as opposed to the many analytics packages that require advanced hardware & data management capabilities, as well as highly trained personnel)
The intent of this plan is to allow a healthcare organization with constrained resources to carry out an analytics project (or more likely set of projects) that will set them up for the productive use of analytics in their everyday operations. Of course, this only works if it is part of a larger operational & strategic plan that has buy-in from both the top level of the organization & from the clinicians in the organization.

Analytics will be essential for healthcare organizations as they evolve to meet the challenges of new organizational & sustainability issues. Constrained organizations must be able to meet these challenges on their own terms in order to remain viable & beginning the analytics with what already exists, & adding a little more, will allow them to begin to develop this productive & strategic capability.



[1] Personal communications to D. Hartzband by a variety of CHC, PCA-based &/or other administrators & providers