Tackling the Tough One: Master Data Management for the Healthcare Enterprise

One of the big struggles in healthcare is the difficulty of Master Data Management.  A typical regional hospital organization can have upwards of 200+ healthcare applications, multiple versions of systems and, of course, many, many “hidden” departmental applications.  In that situation, Master Data Management for the enterprise as a whole can seem like a daunting task.  Experience dictates that those who are successful in this effort start with one important weapon: data and application governance.

Data and application governance can often be compared to building police stations, but it is much more than that.  Governance in healthcare must begin with an understanding of data as an asset to the enterprise.  For example, developing an Enterprise Master Patient Index (EMPI) is creating a key asset for healthcare providers to verify the identity of a patient independent of how they enter the healthcare delivery system.  Patients are more than a surgical case, an outpatient visit or pharmacy visit.  Master data management in healthcare is the cornerstone of moving to treating patients across the entire continuum of care, independent of applications and location of care.  Bringing the ambulatory, acute care and home care settings into one view will provide assurance to patients that a healthcare organization is managing the entire enterprise.

Tracking healthcare providers and their credentials across multiple hospitals, clinics and offices is another master data management challenge.  While there are specialized applications for managing doctor’s credentials, there are not enterprise-level views that encompass all types of healthcare professionals in a large healthcare organization and their respective certifications.  In addition, this provider provisioning should be closely aligned with security and access to protected healthcare information.  A well designed governance program can supervise the creation of this key master data and the integration across the organization. 

An enterprise view of Master Data provides a core foundation for exploiting an organizations data to its full potential and offers dividends beyond the required investment.  Healthcare organizations are facing many upcoming challenges with reference data as a part of master data management, especially as the mandated change from ICD-9 to ICD-10 codes approaches.   Hierarchies are the magic behind business analytics – the ability to define roll-up and drill-downs of information.  Core business concepts should be implemented as master data – how does the organization view itself?  The benefits of a carefully defined and well governed master data management program are many: Consistent reporting of trusted information, a common enterprise understanding of information, cost efficiencies of reliable data, improved decision making from trusted authoritative sources, and most importantly in healthcare, improved quality of care.

Data and application governance is the key to success with master data management.  Just like an inventory, key data elements, tables and reference data must be cataloged and carefully managed.  Master data must be guarded by three types of key people: a data owner, a data steward and a data guardian.  The data owner must take responsibility for the creation and maintenance of the key asset.  The data steward will be the subject matter expert that determines the quality of the master data and its appropriate application and security.  Finally, the data guardian is the information technology professional that oversees the database, the proper back-up and recovery of the data assets and manages the delivery of the information.  In all three roles, accountability is important and overseen by an enterprise information management (EIM) group that is composed of key data owners and executive IT management.

In summary, master data management provides the thread that ties all other data in the enterprise together.  It is worth the challenge to create, maintain and govern properly.  For success, pick the right people, understand the process and use a reliable technology.

Clinical Alerts – Why Good Intentions Must Start as Good Ideas

As the heated debate continues about ways to decrease the costs of our healthcare system while simultaneously improving its quality, it is critical to consider the most appropriate place to start – which depends on who you are. Much has been made about the advantages of clinical alerts especially with their use in areas high on the national radar like quality of care, medication use and allergic reactions, and adverse events.   Common sense, though, says walk before you run; in this case its crawl before you run. 

Clinical alerts are most often electronic messages sent via email, text, page, and even automated voice to notify a clinician or group of clinicians to conduct a course of action related to their patient care based on data retrieved in a Clinical Decision Support System (CDSS) designed for optimal outcomes. The rules engine that generates alerts is created specifically for various areas of patient safety and quality like administering vaccines to children, core measure compliance, and preventing complications like venous thromboembolism (VTE) (also a core measure). The benefits of using clinical alerts in various care settings are obvious if the right people, processes, and systems are in place to consume and manage the alerts appropriately. Numerous studies have been done highlighting the right and wrong ways of implementing and utilizing alerts. The best criteria I’ve seen used consider 5 major themes when designing alerts: Efficiency, Usefulness, Information Content, User Interface, and Workflow (I’ve personally confirmed each of these from numerous discussions with clinicians ranging from ED nurses to Anesthesiologists in the OR to hospitalists on the floors). And don’t forget one huge piece of the alerting discussion that often gets overlooked…….the patient! While some of these may be obvious, all must be considered as the design and implementation phases of the alerts progress.

OK, Now Back to Reality

A discussion about how clinical alerting can improve the quality of care is one limited to the very few provider organizations that already have the infrastructure setup and resources to implement such an initiative. This means that if you are seriously considering such a task, you should already have:

• an Enterprise Data Strategy and Roadmap that tells you how alerts tie into the broader mission;
• Data Governance  to assign ownership and accountability for the quality of your data and implement standards (especially when it comes to clinical documentation and data entry);
• standardized process flows that identify points for consistent, discrete data collection;
• surgeon, physician, anesthesiology, nursing, researcher, and hospitalist champions to gather support from various constituencies and facilitate education and buy-in; and
•  oh yeah, the technology and skilled staff to support a multi-system, highly integrated, complex rules-based environment that will likely change over time and be more scrutinized………

◊◊Or a strong relationship with an experienced consulting partner capable of handling all of these requirements and transferring the necessary knowledge along the way.◊◊

I must emphasize the second bullet for just a moment; data governance is critical to ensure that the quality of the data being collected passes the highest level of scrutiny, from doctors to administrators. This is of the utmost importance because the data is what forms the basis of the information that decision makers act on. The quickest way to lose momentum and buy in to any project is by putting bad data in front of a group of doctors and clinicians; trust me when I say it is infinitely more difficult to win their trust back once you’ve made that mistake. On the other hand, if they trust the data and understand the value of it in near real time across their spectrum of care, you turn them quickly into leaders willing to champion your efforts. And now you have a solid foundation for any healthcare analytics program.    

If you are like the majority of healthcare organizations in this country, you may have some pieces to this puzzle in various stages of design, development, deployment or implementation. In all likelihood, though, you are at the early stages of the Clinical Alerts Maturity Model

 

and with all things considered, should have alerting functionality in the later years of your strategic roadmap. Though, there are many  projects with low cost, fast implementations, quick ROIs, and ample examples to glean lessons learned from like, Computerized Physician Order Entry (CPOE), electronic nursing and physician documentation, Picture Archiving System (PACS), and a clinical data repository (CDR) to use alerting as a prototype or proof of concept to demonstrate the broader value proposition. Clinical alerting, to start, should be incorporated alongside projects that have proven impact across the Clinical Alerts Maturity Model before they are rolled out as stand-alone initiatives.

From Free Text Clinical Documentation to Data-rich Actionable Information

Hey healthcare providers! Yeah you the “little guy”, the rural community hospital; or you the “average Joe”, the few-hundred bed hub hospital with outpatient clinics, an ED, and some sub-specialties; or you the “behemoth”, the one with the health plan, physician group, outpatient, inpatient, and multi-discipline, multi-care setting institution. Is your EMR really just an electronic filing cabinet? Do nursing and physician notes, standard lab and imaging orders, registration and other critical documents just get scanned into a central system that can’t be referenced later on to meet your analytic needs? Don’t worry, you’re not alone…

Recently, I blogged about some of the advantages of Microsoft’s new Amalga platform; I want to emphasize a capability of Amalga Life Sciences that I hope finds its way into the range of healthcare provider organizations mentioned above, and quick! That is, the ability to create a standard ontology for displaying and navigating the unstructured information collected by providers across care settings and patient visits (see my response to a comment about Amalga Life Science utilization of UMLS for a model of standardized terminology). I don’t have to make this case to the huge group of clinicians already too familiar with this process in hospitals across the country; but the argument (and likely ROI) clearly needs to be articulated for those individuals responsible for transitioning from paper to digital records at the organizations who are dragging their feet (>90%). The question I have for these individuals is, “why is this taking so long? Why haven’t you been able to identify the clear cut benefits from moving from paper-laden manual processes to automated, digital interfaces and streamlined workflows?” These folks should ask the Corporate Executives at hospitals in New Orleans after Hurricane Katrina whether they had hoped to have this debate long before their entire patient population medical records’ drowned; just one reason why “all paper” is a strategy of the past.   

Let’s take one example most provider organizations can conceptualize: a pneumonia patient flow through the Emergency Department. There are numerous points throughout this process that could be considered “data collection points”. These, collectively and over time, paint a vivid picture of the patient experience from registration to triage to physical exam and diagnostic testing to possible admission or discharge. With this data you can do things like real or near-real time clinical alerting that would improve patient outcomes and compliance with regulations like CMS Core Measures; you can identify weak points or bottlenecks in the process to allocate additional resources; you can model best practices identified over time to improve clinical and operational efficiencies. Individually, though, with this data written on a piece of paper (and remember 1 piece of paper for registration, a separate piece for the “Core Measure Checklist”, another for the physician exam, another for the lab/X-ray report, etc.) and maybe scanned into a central system, this information tells you very little. You are also, then, at the mercy of the ability to actually read a physicians handwriting and analyze scanned documents of information vs. delineated data fields that can be trended over time, summarized, visualized, drilled down to, and so on.

Vulnerabilities and Liabilities from Poor Documentation

Relying on poor documentation like illegible penmanship, incomplete charting and unapproved abbreviations burdens nurses and creates a huge liability. With all of the requirements and suggestions for the proper way to document, it’s no wonder why this area is so prone to errors. There are a variety of consequences from performing patient care based on “best guesses” when reading clinical documentation. Fortunately, improving documentation directly correlates with reduced medical errors. The value proposition for improved data collection and standardized terminology for that data makes sense operationally, financially, and clinically.   

So Let’s Get On With It, Shall We?

Advancing clinical care through the use of technology is seemingly one component of the larger healthcare debate in this country centered on “how do we improve the system?” Unfortunately, too many providers want to sprint before they can crawl. Moving off of paper helps you crawl first; it is a valuable, achievable goal across that the majority of organizations burdened with manual processes and their costs and if done properly, the ROI can be realized in a short amount of time with manageable effort. Having said this, the question quickly then becomes, “are we prepared to do what it takes to actually make the system improve?” Are you?

Healthcare Analytics to the Rescue!

Why does my health insurance cost so much?

It’s that time of the year again. No, I am not talking about the holidays. It’s the time of the year, when you figure out how much more money you need to make, in order to afford the rise in your healthcare costs. It’s Annual Enrollment time! But as most folks have already realized, there probably won’t be any raises, bonuses, etc., this year to help off-set the rise in healthcare premiums. The economy is experiencing its biggest downturn since the Great Depression and yet our quoted health insurance cost for next year is rising at a double-digit pace. How is that possible?

“Over the last decade, employer-sponsored health insurance premiums have increased 131 percent”.

My wife and I calculated that pre-tax, she would need to earn another $ 1,200 a year this year to off-set the rise in the monthly premiums being charged for an HMO plan with family coverage. Currently, we belong to the #1 ranked Health Plan in the country, which is increasing its rates to the tune of $100 a month for the  same level of coverage as last year. Unfortunately, we have been experiencing this trend for more than the past 20 years.

I realize its not a simple answer, and there are several external factors including rising pharmacy costs, inflation, etc. However, one could argue that since the economy is in a tail-spin, unemployment is sitting just under 10%, and the federal government is wasting time and my tax dollars trying to create a new public option for health coverage, that the best option for insurers is to hold premiums steady and to finally get a handle on what are the true drivers of cost and utilization. Thus, they would not risk losing its most important constituents, their employer groups and members, who every year are now faced with the idea of reducing their level of healthcare coverage just to make ends meet.

If the #1 health plan in the country is raising their premiums by $100 a month for a basic HMO plan, can you imagine what the lower ranking health plans will charge to their members? There are no quick-fix-it solutions for the healthcare industry. However, with so many inefficient processes, fraud, overhead, flawed reimbursement methodologies, expensive compliance and technology projects, etc., the industry is ripe for opportunities to become more analytics focused. With today’s business intelligence and data warehousing technologies available, health plans now have the ability to create high-value metrics that involve integration of disparate data sources from key areas such as: sales & marketing, operations (ex. Claims processing), and cost and utilization across members, providers, and employer groups.

Despite the quoted savings achieved by health plans from a variety of medical management programs, disease management, formularies, network discounts, etc., why is it never passed onto a subscriber’s premium? Are health plans not evaluating the right metrics? Pushing the boundaries for increasing the use of payer analytics will allow health plans to truly understand the drivers of cost and utilization and thus to migrate their business model to become more predictive in nature. Maybe this is wishful thinking, but a health plan could actually reduce their monthly premiums if they can drive out the unknown costs and inefficiencies. A futuristic but intriguing thought would be to have benefit plans that are created and priced for each member, which is based on both historical utilization and predictive analytics to determine the monthly premiums.

At a minimum, can we stop the double-digit price increases?

The Promise of the Real Time Web for the Enterprise

Real Time Web is the latest trend to capture the media’s attention over the past few months, and indeed seems to encapsulate well the effect that Twitter and the social networks are having on the flow of information. The ability to get up-to-the-second information about people, news and activities around the world is a foundation for a new wave of startups and services and is being integrated into search and other services.

As many users of the real time web will attest, its constant stream of information can be overwhelming and disjointed but at its best, it allows awareness and insight to emerge as the confluence of information takes a clearer shape.

Can this be useful in the enterprise? (I’ll be careful about using the term “The Real-Time Enterprise” that Gartner coined a few years ago; it means something else).

Companies generate huge amounts of data that rarely sees the light of day. Let’s consider the following scenario – you are an account manager for several key accounts in a particular vertical. What information are you getting? Most likely direct and indirect emails consist of 90% of the information while the rest is verbal, non-documented conversations. But what if you could get real-time updates on the following:

• Client specific news
• Client brand related blog posts, discussions, videos and tweets in real time
• Vertical news
• Client services updates about milestones reached
• Customer support alerts about open service tickets and their resolution status
• Internal discussions and email regarding the client
• External email communications with the client by different team members
• Etc..

Not all of these would constitute information that someone will send a specific email on. Being aware of the stream of news, discussions and information can be invaluable for an agile and responsive approach.

Our current document and email centric information systems are not built to provide this level of constant details. Using the new generation of web mashups and aggregation tools are beginning to offer reasonable solutions.

As Jennifer Martinez had recently observed in GigaOm, there is a huge potential for tools that will help sift and provide context for all of these huge streams of data.

What surprises me is that most of the discussion looks at this as a new phenomenon while there is an industry that has been using this method very successfully for a long time. The Bloomberg (and other) terminals provide bite size financial information in a continual stream that can be filtered, sorted and analyzed. It combines company news, industry news, transactions, price changes, etc., in a way that for a novice seems indecipherable but for the experienced broker is a goldmine.

Providing the right tools are put in place, the potential business value seem significant:

• Accelerating cycles of decision making
• Pushing all relevant information to you rather than pulling from multiple sources is a great time saver
• Decreasing the unbearable email load
• Increasing and broadening awareness to domain knowledge

For more information on the real time web and the type of tools that exist around it, ReadWriteWeb has compiled a great list of top 50 real time web companies and services.

The Microsoft Clinical Information Feedback Loop

Microsoft recently purchased Rosetta Biosoftware from Merck & Co. for its Amalga Life Science platform; with this move, Microsoft is starting to differentiate itself from its competition by offering its integrated information solutions, which include HealthVault, Amalga UIS and Amalga Life Sciences, to both providers and producers. In its crosshairs are huge budgets available from Pharma for infrastructure solutions for drug R&D and clinical trials. Microsoft is posed to attract a whole new audience of customers from Pharma to integrated health systems that have their own research entities. If done correctly, Microsoft’s new strategy could become a model for improving the efficiency of clinical research, by drastically reducing the most costly resource needed for clinical trials, time.

The current Amalga UIS is fundamentally what I like to call a PDA (no not Public Display of Affection, rather a Patient Data Aggregator). There are three core components that include:

1. Data Aggregation and Distribution Engine (DADE) – sits on top of healthcare provider sources and listens for HL7 messages; then puts them through transformation and parsing scripts in preparation to be stored in Amalga and sends them to a data store;
2. Data Store – receives the messages from DADE; is a basic core storage engine and is a database with a set of tables specific to segments within the HL7 messages; and
3. Front End – a web-based presentation layer that was originally designed for patient level data viewing and has plug in capability to provide more appropriate tools for analysis.

The current needs of data integration seem to be met by this solution, and the high degree of customization that can accommodate an implementation makes it even more attractive. Microsoft’s footprint in healthcare is getting bigger; they must understand, though, that this space has many stakeholders. While addressing all their needs is nearly impossible (just ask our hard working politicians’ trying to pass healthcare reform legislation), the last people they want to alienate are those they’ve already convinced that Amalga is the healthcare platform of the future, most notably some high profile integrated health systems across the country.

Integrated health systems (IHS) often provide a combination of services including care delivery, research, education, and even  their own health plan (think KP, John Hopkins, Geisinger, and Sentara). These entities have a unique opportunity to leverage the MS offerings by creating a continuous feedback loop of information from patient to provider to researcher that improves the quality and accuracy of the data throughout the process. Let’s start with the patient:

• Patient information in HealthVault – As patient’s progress from being baby boomers (less tech-savvy) to Generation X & Yer’s (tech-hungry), clinical information will no longer be in the sole possession of the doctors. Rather, the demand will be for online, mobile, 24×7 access that is shared and can be updated real-time as health data is gathered by both patients and their doctors. Patients, thus, become a stand-alone data quality tool as they become more comfortable verifying, updating, and changing the information in their medical records.
• Research information in Amalga Life Sciences – Researchers are all too familiar with the tedious, error-prone process of identifying patients with the correct diagnosis and conditions as candidates for clinical trials. As patients become more empowered with their medical records, they make the segmentation of populations a much simpler process.
• Clinical information in Amalga UIS – Amalga UIS is a mechanism for driving continuous improvement in clinical care by integrating data across the enterprise. One way to improve care is by incorporating best practices identified through clinical research. The information learned from improved research methods are then implemented directly into the standard delivery of patient care offered by provider institutions.

The Amalga UIS is currently operational in 12 domestic organizations. Because most of these clients are IHS’ and have research entities, they are in the best position to capitalize on the Amalga Life Sciences offering. These will also be the locations where the ROI MS is hoping will be formulated for less prestigious organizations to eventually imitate. It begs the following question, though, that some of the current customers will ask, “How can the existing components of Amalga Unified Intelligence System (UIS) be leveraged in this new offering to make it attractive to the widest audience possible and more importantly, be affordable?” Well, if you can articulate the argument above, and identify the huge benefits that can come from the Microsoft Feedback Loop, your argument might be easier to make than you think. And don’t forget, this feedback mechanism is built on the fundamental principle that all stakeholders must have the collective groups’ best interest in mind; so don’t forget to share what you find with your neighbor.

Begin at the end … ensuring data quality success!

posted with permission from Data Quality Chronicle

Due to the fact that data is there before a data quality project, and it is there after a data quality project, data quality is not as clear an impact on the business as a traditional application development project.  This is particularly true of customer data management oriented data quality projects where the primary objective is to “de-dup” or consolidate the data.  After all, in the end there is just less data.

When this is looked upon purely from a software perspective there’s not much difference.  Sure, there are cost savings associated with the reduction in the storage requirements.  There might even be some increased performance in dependent applications due to the reduced volume.  However this is hardly a justification for the investment that a typical data quality initiative requires.  This is particularly inconvenient considering most of the investment is in software and other technology related resources.

However consider the impact of a data quality project which consolidates customer data from a business perspective and see a different side of things.  Consider the benefits of less, unnecessary, possibly inaccurate customer data.

• fewer mailings to reach the same customer providing a direct cost savings
• fewer mailings to reach the same household providing a direct cost savings
• fewer mailings required overall providing a direct cost savings
• fewer failed mailing attempts due to address validation providing a direct cost savings
• fewer customer service requirements due to single view of the customer providing a direct cost savings
• more accurate perspective of customer product portfolio providing a direct increase in marketing opportunities

Now (re)consider the substantial impact that can be realized from a consolidation effort.  Furthermore as long as data quality initiatives are implemented into ongoing operational data services, these cost reductions extend into the future producing benefits in the long term.  This further justifies the cost of implementing data quality services into an organization as a long term solution.

This is why it is critical to the success of a data quality project to have clear goals that are aligned with a business initiative.

However this is not the end of the line when it comes to ensuring success.  To do this you have to start with goals like the ones listed above and define ways in which these types of goals can be measured.

For example the first bullet point is a data quality goal tied to the business initiative of reducing duplicate customer data. To support this, a data quality matching process can be defined that uses criteria to identify redundant customer transactions and consolidate them into a survivor record.  The affect the data quality initiative has on this business process can be measured in terms of the reduction in total mailings required to complete a marketing campaign.  More importantly, it can be measured in terms of a reduction in total dollars required to fund the new and more concise direct mailing campaigns.  Now the data quality process and its results can be linked directly to a reduction in budget.  Clearly metrics like these make it obvious that a data quality initiative that merely reduces data has a tremendous amount of value.

If you define a list like this with business stakeholders driving the process, before the data quality project is implemented, there will be a clear path to success as well as an easy way to quantify it once the solution is deployed!

Physicians Insist, Leave No Data Behind

“I want it all.” This sentiment is shared by nearly all of the clinicians we’ve met with, from the largest integrated health systems (IHS) to the smallest physician practices, in reference to what data they want access to once an aggregation solution like a data warehouse is implemented.  From discussions with organizations throughout the country and across care settings, we understand a problem that plagues many of these solutions: the disparity between what clinical users would like and what technical support staff can provide.

For instance, when building a Surgical Data Mart, an IHS can collect standard patient demographics from a number of its transactional systems.  When asked, “which ‘patient weight’ would you like to keep, the one from your OR system (Picis), your registration system (HBOC) or your EMR (Epic)?” and sure enough, the doctors will respond, “all 3”. Unfortunately, the doctors often do not consider the cost and effort associated with providing three versions of the same data element to end consumers before answering, “I want it all”.  And therein lies our theory for accommodating this request: Leave No Data Behind. In support of this principle, we are not alone.

By now you’ve all heard that Microsoft is making a play in healthcare with its Amalga platform. MS will continue its strategy of integrating expertise through acquisition and so far, it seems to be working. MS claims an advantage of Amalga is its ability to store and manage an infinite amount of data associated with a patient encounter, across care settings and over time, for a truly horizontal and vertical view of the patient experience. Simply put, No Data Left Behind.  The other major players (GE, Siemens, Google) are shoring up their offerings through partnerships that highlight the importance of access to and management of huge volumes of clinical and patient data.

Why is the concept of No Data Left Behind important? Clinicians have stated emphatically, “we do not know what questions we’ll be expected to answer in 3-5 years, either based on new quality initiatives or regulatory compliance, and therefore we’d like all the raw and unfiltered data we can get.” Additionally, the recent popularity of using clinical dashboards and alerts (or “interventional informatics”) in clinical settings further supports this claim. While alerts can be useful and help prevent errors, decrease cost and improve quality, studies suggest that the accuracy of alerts is critical for clinician acceptance; the type of alert and its placement and integration in the clinical workflow is also very important in determining its usefulness. As mentioned above, many organizations understand the need to accommodate the “I want it all” claim, but few combine this with expertise of the aggregation, presentation, and appropriate distribution of this information for improved decision making and tangible quality, compliance, and bottom-line impacts. Fortunately, there are a few of us who’ve witnessed and collaborated with institutions to help evolve from theory to strategy to solution.

Providers must formulate a strategy to capitalize on the mountains of data that will come once the healthcare industry figures out how to integrate technology across its outdated, paper-laden landscape.  Producers and payers must implement the proper technology and processes to consume this data via enterprise performance management front-ends so that the entire value chain becomes more seamless. The emphasis on data presentation (think BI, alerting, and predictive analytics) continues to dominate the headlines and budget requests. Healthcare institutions, though, understand these kinds of advanced analytics require the appropriate clinical and technical expertise for implementation. Organizations, now more than ever, are embarking on this journey. We’ve had the opportunity to help overcome the challenges of siloed systems, latent data, and an incomplete view of the patient experience to help institutions realize the promise of an EMR, the benefits of integrated data sets, and the decision making power of consolidated, timely reporting. None of these initiatives will be successful, though, with incomplete data sets; a successful enterprise data strategy, therefore, always embraces the principle of “No Data Left Behind”.

Enterprise 2.0 Conference 2009 – What’s New?

It was interesting to visit the Web 2.0 conference last week and see the progress and trends compared to my last year impressions.

Here are some of my thoughts:

• SharePoint is the de-facto standard for Enterprise 2.0 While other vendors have compelling products and features, a CIO that is looking for an internal, comprehensive, secure and maintainable solution has almost only one choice (unless you are on an IBM stack..). Other tools focus on providing point solutions, hosted environments, plugging current SharePoint holes and extending functionality. Microsoft had the biggest and most impressive presence and were heavily promoting the next version SharePoint 2010 that will be launched in the SharePoint conference in October. (Some preliminary details here).
• The field has definitely matured over the last year. There are more case studies and research about usage, benefits and trends although most companies are not sharing explicit ROI numbers. Some vendors have disappeared while others are growing and establishing themselves at a level where they may be considered long term players and safe for the enterprise.
• The experts are still frustrated with the slow rate of adoption and the seeming growing gap between the prevalence of social tools and technologies used by marketing and sales to communicate externally Vs. they almost complete absence internally. The rapid adoption of tools like Facebook and Twitter for customer communication not just in retail but in Healthcare and other industries creates glaring discrepancies where the same companies have no tools internally and sometimes even block their own marketing teams from external use of these tools under some outdated IT policy.
• IT is still not part of the discussion. That is unfortunate because as Steve Wylie, the conference director expressed in a guest post at ZDNET last week, large scale adoptions will not happen without IT.

  “While we could argue that this is a very new market and that businesses take time to change, I also believe that Enterprise 2.0 will be challenged by large-scale adoption until corporate IT is fully on board.  Early adoption has been largely driven by business users and department-level managers.  They had a problem to solve and were fed up waiting for IT to provide the solutions they needed.  They took matters into their own hands by finding workable, web-based solutions and even celebrated this new found freedom from IT.  With a few exceptions, IT took a reactive posture to Enterprise 2.0 and viewed it as a threat to be managed, secured and even blocked in some cases.”
  

• Tactical view. One of the most frequently asked questions was “what is the best way to get started?”. The pretty universal answer for vendors and corporate users was to approach it in a tactical manner and find a specific business problem you can solve using collaboration tools. Be it an HR portal to boost morale, tools to help virtual project teams work more efficiently, sales best practices portal or any of many other ideas. Define a narrow business case and implement. So far, trying to approach this in a strategic manner makes finding ROI a herculean task and as noted above, puts IT on the defensive. I hope that this trend will start to change as these tactical solutions rarely provide long term sustainable benefits.
• Rise of the Community Manager. The most active forum was the one where the newly created function – community managers shared their challenges and tricks for getting people to take part in the social activity. First, It is great to see that many leading organizations have realized the importance of such a task although many had it as a secondary responsibility they volunteered to do rather than a full time position. Creating and maintaining a vibrant and active internal community requires skill, passion and process and the focus should rightfully be as much on that as on the tools that enable the community.

Additional impressions:

Enterprise 2.0 2009 Conference: Aggregate and Organize

The Patient Centered Medical Home (PCMH)

In a recent article released by IBM, an argument is made for a transition in the U.S. healthcare system to a team-based approach based on the Patient Centered Medical Home (PCMH) model. A strong case is made from a description of the model, its’ players, technology, and benefits. The critical change that must be established first, though, is the healthcare systems’ evolution to a data-driven system. The access to, higher quality and integration of data, across disparate silos of information, will provide the foundation for this change. Only then can the position of Dr. Douglas Henley, EVP and CEO of the American Academy of Family Physicians, “ A smarter health system is one based in comprehensive patient centered primary care which improves patient/physician communication, the coordination and integration of care, and the quality and cost efficiency of care” be achieved.

The quality and cost of care is what we hear the most about in news headlines. However, the success of each piece of Dr. Henley’s statement is based on the ability of a team of providers to access accurate and updated patient data across care settings and over time in order to proactively suggest lifestyle improvements and reactively diagnose and recommend appropriate treatments.  Fundamentally, each decision maker and operating entity needs a data strategy for how it will achieve the ambitious and often ambiguous goals it likes to claim.

I’ll recite a popular management mantra I’ve heard numerous times, “you can’t manage what you can’t measure.” The healthcare system is a data rich environment. Cleaning, manipulating, and leveraging  the huge volume of data available will become the critical success factors that will enable the linkage between education, research, the delivery of care and its outcomes, to benchmark and monitor the performance of the continuous improvements necessary to bring costs down and quality up.  

Players in the healthcare world will soon find out (if they haven’t already) a principle all those in the data world already know:

• Good data, appropriately aggregated and manipulated, drives accurate information;
• Accurate information is not a luxury that most decision makers have;
• The executives, managers, physicians, nurses, nurse practitioners, educators, pharmacists, researchers, and other stakeholders that do have access to accurate information are in a position to leverage and evolve this data and information from satisfying compliance and regulatory requirements to enabling an organizational knowledge-based asset.

Actionable data will drive the improvements that you see scattered across headlines and mentioned in political speeches in the past and no doubt, in the future.

Image courtesy of Texas Family Physician