Healthcare Relationship Management: Closing the loop

In a previous post, I posed the question — “Are Patients Really Just Customers?” Well, no. However, healthcare organizations can benefit greatly by applying marketing discipline to new patient outreach campaigns:

• Identify the Key Influencers
• Relationship Management via Marketing Communications
• Identify Your Target, Craft Your Message, Define Your Measures
• Closing the Loop – Integrating Key Touch-Points with the Patient – Integrating Data Sources
• Evaluate Your Effectiveness, and Refine Your Actions with Analytics
• Progressing Along the Maturity Model

I’ve already covered the first three  principles, now I will show how you can close the loop on those marketing efforts, to better understand where your patients are coming from.

Closing the Loop – Integrating Key Touch-Points with the Patient – Integrating Data Sources
Start by identifying the diverse and numerous touch-points you already have with the patient.

 A key to launching and managing the effectiveness of your campaigns is embedding the capacity to “close the loop” on the patient’s actions and capturing the key influences on those actions.  Your call center scripts can incorporate context-specific prompts (as appropriate) during visit scheduling dialogues to determine, “How did you come to choose [our facility] for your care?”  Registration desks and clinic intake surveys can ask seamlessly and discreetly about the factors that lead patients to a specific facility or service, or the source of the referral.  Direct referrals under managed care plan policies and directives can further automate the tie back to specific outreach and education programs.

Use the response data to tune your message – reaching the right audience, at the right time, with the right message.

Evaluate Your Effectiveness, and Refine Your Actions with Analytics
Is this program and approach having the desired impact?  What is our data telling us?  How should we adapt our outreach efforts?

The key element to closing the loop on marketing effectiveness is assembling the data to help you understand what is working, and what is not.  Your analytics environment needs to answer these key questions:

• How many new patients have we seen: at our primary hospital(s); at our local clinics; through our ambulatory and/or primary care and/or specialty groups?  What is the trending of this patient volume over time?
• Which marketing programs and campaigns have we run targeting these entities (e.g. physicians, communities, geographies, parent groups, schools, managed care groups, employers); over what time periods; and including what specific activities?  To which of these campaigns can we attribute our patient volumes?  What evidence do we have to suggest this relationship?
• What patterns do we see in referrals, and in their response to our marketing programs?  Which activities are most frequently associated with a referral coming from:
  • a first-time referral from a physician (new to the area, or long-time resident): inside an affiliated physician group; inside an independent physician group; a primary care physician; a specialist
  • a physician who has referred patients previously; or
  • a family member of the patient; a community member (e.g. teacher, clergy, neighbor) of the patient?
• Which media elements have been most frequently, or infrequently, associated with successful, or unsuccessful, results?  Which sequence or co-occurrence of activities or campaign elements has been most frequently, or infrequently, associated with successful, or unsuccessful, results?
• From which geographies are we seeing these various patients and referrals coming?  What marketing programs have we run in these geographies, targeting these patient or physician populations?  What else do we know about these targeted populations?
• What is the ROI of these programs and/or campaigns?  Which have been most “effective” (greatest number of targeted results: new patients, new referrals, repeat referrals) or “efficient” (best results for dollars and effort expended)?

Progressing Along the Maturity Model
How do we get started?  How do we measure progress?

Organizations will “grow” along a maturity scale in their application of relationship management as a key component in their marketing communication and outreach efforts.  “Think big, but start small” is a viable approach.

The XRM platforms available today are incrementally deployable, enabling “Out of the Box” implementation using core capabilities and minimal direct integration with other transactional systems across your enterprise.  Building your marketing analytics capability on a stable and scalable technology platform will facilitate an orderly evolution, progressively extending the data collection capabilities at the various points of contact with patients (and other key constituencies); ultimately leading to a more integrated approach to the measurement and management of marketing effectiveness, and a direct tie to the desired behaviors of patients and providers.

Healthcare Relationship Management: Are Your Patients Really “Just Customers”?

Are your patients really “just customers”?  How do you get them to buy, and who really makes the buying decisions?

To answer these questions, the following steps outline a logical process for developing and validating an effective marketing program, aligning both internal and external resources to the maximum benefit:

1. Identify the Key Influencers
2. Relationship Management via Marketing Communications
3. Identify Your Target, Craft Your Message, Define Your Measures
4. Closing the Loop – Integrating Key Touch-Points with the Patient – Integrating Data Sources
5. Evaluate Your Effectiveness, and Refine Your Actions with Analytics
6. Progressing Along the Maturity Model

I will cover all points over the course of 2 posts.

Identify the Key Influencers
Healthcare providers are well aware of the realities: key influencers of patients’ choices for specialists and specific health facilities are their primary care physicians, or the patients’ satisfaction with prior services; and key influencers of patients’ choices for primary care physicians are the patients’ friends and family members, or their own health plans.

With this in mind, leading hospitals and health systems are striving to balance, and focus, their marketing efforts to reach these key groups with the information they need to make their healthcare choices.  Are you marketing direct to your patients?  Or to the key constituencies who influence your patients’ decisions to seek care?

Relationship Management via Marketing Communications
Healthcare organizations of every size and shape can begin immediately to leverage the capabilities available in straight-forward relationship management platforms: to target the audience; structure multi-channel campaigns; plan and execute the specific communications and outreach activities; and track both the costs and results of their entire marketing program.

Identify Your Target, Craft Your Message, Define Your Measures
Who are we trying to reach?  What do we need them to know?

You need to define your audience, including the precise demographics, and their preferences for how they want to be reached, and the methods they use when seeking care.

What is the service or disease setting we are targeting?  Who is the gender or age group that we need to respond to this outreach initiative?  Is this for preventive or screening services; or for advanced care needs?  Are we promoting a leading-edge treatment or a badly needed diagnostic technology?  Are we announcing regional or national performance scores on quality of care, case volumes, outcomes, or rankings?  Are we trying to influence new members of the community, or long-term residents?  Which specific geographic location or community are we targeting?  What specific needs will the target population have, if they’re candidates for the service?

What other information can be brought together to leverage your campaign?

If you’re targeting a specific service or disease setting, how many people in your markets are eligible for the requisite insurance coverage?  Which local employer groups have employee populations in the target demographic?  Are local colleges or universities sources of potential patients for this service?  Have health plans with local groups recently restructured their coverage for the targeted service areas?

And you need to be sufficiently nimble to test your message, and your content, with targeted segments; measure their response; and tune your approach accordingly.  Trying out new media channels and environments, such as professional or other social media platforms, can lead to new opportunities for engaging prospective patients and providers in productive dialogues, feeding new initiatives.

How do we measure what’s happening?

A key consideration will be to capture and monitor the primary indicators of the desired behavior and outcomes from the targeted groups.  What is the desired behavior we are seeking?  Is it increased referrals from specific specialist groups or locations?  Is it changes in key clinical measures in specific patient populations?  Is it measureable improvements in patient satisfaction, and their assessment of our service?  Are we seeking to tie market response all the way to changes in clinical outcomes?

Accurate and timely data capture at the point of contact (or evaluation) with the patient will be a key aspect of your program to measure the effectiveness of these campaigns.

What’s Next
The greatest value comes when the marketing effort “closes the loop” on the responses and desired behaviors you need from patients and key influencers.  Next time we’ll look further into the mechanisms for capturing these key events, demonstrating the ROI, and moving the organization along a maturity cycle for increasing effectiveness and efficiency. Stay tuned…

Move the Quality Focus to Patient Outcomes

I had the privilege to attend the Microsoft Connected Health Conference in Bellevue, Washington on May 19-20.  Microsoft changed the format of their education sessions this year to a panel discussion including short presentations.  This new format included a moderator and several views of the topic from industry experts and key people from healthcare organizations.  One of my favorite sessions was titled “Capturing Value Across the Continuum: Healthcare Quality and Outcomes.”  If you have been following the Edgewater blogs on improving Core Measures then you understand my interest.

The real take-away on this topic was the understanding that the focus on quality in healthcare has been centered more on improving business processes than improving patient outcomes.  The panel consisted of Kim Jackson, Director of Data Warehousing, St. Joseph Health System, Kevin Fahsholtz, Senior Director with Premier, Dr. Floyd Eisenberg, Senior Vice President for Health Information Technology at the National Quality Forum and Dr. Richard Chung of the Hawaii Medical Services Association.  The panel represented a Hospital Provider (Kim), an Analytics and Benchmarking company (Kevin), a healthcare standards organization (Dr. Eisenberg) and a Payer organization (Dr. Chung),all of the key aspects of the Healthcare Quality continuum and was focused on the real world challenges of improving the quality of healthcare.

The key idea of improving patient outcomes dominated the hour long discussion.  Kim White noted that “the burden (of collecting data) for a hospital is overwhelming, and measuring is overtaking the work.”  Dr. Eisenberg agreed that there was a need to move the focus of the quality measures to outcomes and away from the small process details.  He went on to say that the real issue is the definitions of the data and that the definitions need to be standardized.  In his role, Dr. Eisenberg is working to create a standard data model for quality measures and key definitions to the standards for care.  Dr. Chung pointed out that we need to change our “culture of care delivery” along with the awareness of the data.  Dr. Chung believes that providing visibility of the quality data helps set up a culture of change.  His experience shows that separating the data from the application software allows new understanding.
All of the panelists agreed that a key issue is developing the “single version of the truth” and eliminating conflicting information.  Kim White presented that using Microsoft’s Amalga UIS product allowed St. Joseph Health System to unite their data, reorganize their data and prioritize it.  She pointed out that consolidating data sources from eight locations created this “single version of the truth” and reduced the administrative burden for tracking core measures.

Our experience in improving core measures parallels this panel discussion.  Success in improving healthcare quality and outcomes involves plain old hard work – collecting the right data, with the right definition, at the right time in the process and providing it to the right people.  The need to extend the data collection to tracking outcomes beyond reporting requirements is the right idea at a right time in healthcare.  Let’s not settle for the minimal reporting requirements, but truly track outcomes and develop the feedback loops necessary to keep them successful and improving.  It is, after all, about the patient and not mere statistics.

Implementing Healthcare Service Lines – Inherent Data Management Challenges and How to Overcome Them

Service Line management provides the healthcare industry the ability to determine which of its diverse services are profitable and how the market share of a given service compares to competing providers.  Service Lines are typically limited to a handful of well defined, mutually exclusive categories or groupings of individual services or interventions.  For example, a provider may choose to categorize clinical transactions (encounters, ambulatory or hospital visits, episodes, longitudinal courses of care) into Service Lines such as Oncology, Cardiovascular and Orthopedics.  Since no such Service Line designation exists in standard transactional encoding systems or taxonomies, Service Line assignment are derived based on attributes such as primary diagnosis codes, procedure codes, and other patient attributes such as age, gender or genetic characteristics, to name a few.

From a data management perspective, Service Line management presents a number of interesting challenges.  To illustrate these challenges, consider a large, multi–hospital, multi-specialty, multi-care-setting health system servicing millions of patients annually as they attempt to gain a single, consistent view of Service Lines across all facilities and settings.  Then consider the typical data management obstacles that arise in any one of these settings such as poor data quality, potentially caused by inconsistent coding practices or lack of validation within and across the various source systems in use.  It turns out that for such a provider, the challenges are not insurmountable if the solution adheres to the proper architectural approach and design principles.

Here are some challenges to consider when implementing Service Lines within your healthcare organization:

• It’s not likely that a single set of attributes will provide the flexibility, or for that matter, the consistency across an enterprise that will be needed to define a Service Line.  The approach will have to take into account that business users will almost reflexively define Service Lines on the basis of one or more complex clinical conditions, including the primary treatment modalities.  For example, Oncology = DRG codes BETWEEN 140.00 and 239.99 OR Service IN (RAD, ONC) OR Physician IN (Frankenstein, Zhivago) OR …
• Service Lines will likely overlap on any given healthcare transaction, either within the Service Line or across Service Lines, as a consequence both of the inherently multi-disciplinary care that is delivered, and the traditional department or specialty alignment of critical staff.  A patient that has a primary diagnosis code of lung cancer may be discharged after having a lung transplant procedure by a cardiovascular or thoracic surgeon. The patient transaction is arguably a candidate for both Service Lines, but by definition, only one Service Line may prevail, depending upon the analytic objectives and context.
• Given the often unavoidable Service Line overlap, the need to resolve conflicts within and across Service Lines exists to meet the ultimate requirement that a transaction (case, episode, etc.) is ultimately assigned to one and only one Service Line.  However, there is value in capturing all Service Lines that apply to a given transaction, and specifically giving users visibility into the reality that, given current operating definitions, overlap exists.  This both informs the end-user and enables him/her to take this into consideration appropriately for the analytic objectives at hand.
• Various models are possible for explicitly revealing and managing these important overlaps.  For example, the reporting structure might consider a three level hierarchy that explicitly represents and manages this overlap as a multi-level model:  level 1 resolves conflicts at the health system across all Service Lines; level 2 resolves conflicts between sub-categories within a Service Line; and level 3 allows overlap and may even accommodate multiple counting in well-circumscribed and special analytic contexts.
• Organizations in the early stages of measuring performance by Service Lines might be motivated to influence the definition of any given Service Line to extend its reach by attaching to transactions currently falling into the ‘Other Service Line’; or by providing a more granular view (e.g. categories and sub-categories) of existing Service Lines.  To support the inevitable evolution of these definitions, Service Line definitions and specifications should be implemented as adaptable business rules, capable of being changed on a frequent basis.

To address these challenges, listed below are some technical implementation techniques you may want to consider:

• Business rules should be implemented using a data model that allows for data-driven evaluation of rules, and should never be implemented as part of static code or ETL (extract, transformation, load).  Given the volume of data that will be processed, rule evaluation should be done within the database engine as a Join operation, rather than iterating record by record.
• Complexity of the business rules and the frequency with which they change could drive a decision to use an off-the-shelf business rule tool.  Finding a tool that meets your implementation timeline and budget will enhance the users’ experience; typically the financial planners and analysts that will create and modify the rules.  The key is to find a business rule tool that does not require rule processing to be programmatic or iterative.
• When evaluation of any business rule applies to a specific transaction, tag the transaction explicitly.  While the ultimate goal of the rules is to assign one and only one Service Line, tagging as a first step allows rules to be evaluated in isolation of one another and provides visibility to the level 2 and level 3 Service Line assignments described above.  A tag is equivalent to a provisional Service Line designation, although the ultimate Service Line assignment cannot occur until overlap conflict is resolved.
• With individual transactions specifically tagged, conflicts that occur can be resolved at each level of the Service Line assignment hierarchy.  To achieve the desirable level 1, 2 and 3 hierarchy behavior, conflict is resolved in terms of the competing individual precedence assigned to each of the relevant business rules.  For example, a simple business rule precedence scheme for level 1 may be to state that where Oncology and Cardiovascular overlap, Cardiovascular always takes precedence.  Other strategies and specifications for resolving such conflicts can implemented using a consistent representation.

In summary, the key requirement for Service Line implementation is adaptability.  Implementing a data-driven platform capable of evaluating the complex business rules and supporting the different behaviors of Service Line tags by explicit representation within the relevant reporting hierarchies will allow your healthcare organization to constantly evolve and gain new insights on the performance and improvement opportunities of your services lines.  In the final analysis, that’s what it’s all about.

Implementing Healthcare Service Lines – Gaining Competitive Advantage by Focusing and Optimizing the Enterprise Mission

Many hospitals and health systems are exploring integrated Service Lines as a novel organizational model to help bring focus to the diverse services they provide to patients and the various resources (labor, facilities, equipment, materials) they must manage in their relentless striving for ever higher quality and lower cost.

The notion of a Service Line is certainly not new, and there are countless examples from other industries where firms have made the transition from a “vertical” functional model, where resources are organized according to individual professional disciplines, to a more “horizontal” service line or product line model, where resources are directly aligned with the “outputs” that are produced and delivered.  Any one of us can easily call to mind large enterprises in many different industries whose product lines have a stronger identity than the overall firm itself.  However, to be clear, the primary emphasis in our present context is on the manner in which the diverse resources of the firm are organized to most effectively and efficiently deliver the products and services of the enterprise.  Identity and/or brand equity are simply one of the assets that is managed.

In the transition to a Service Line orientation, healthcare providers will very often move through a sequence of inquiries as they incrementally define and implement the emerging model, tailoring it to their unique and specific circumstances, challenges and opportunities.

Motivation -

• Why would we want to move to a Service Line model?
• What is it about our specific circumstances that suggest a Service Line model would be superior?
• What advantages would it bring?  What difficulties would it cause, or exacerbate?
• What are our unique opportunities or capacities in terms of clinical, operational or financial excellence, or challenge, that suggest Service Lines as a way to proceed strategically?
• Do we have best-in-industry care models, or process excellence, or evidence of best results?
• What other organizations are like us, what have they done, and what can we learn from them?
• What differentiates our particular strengths, weaknesses, opportunities or threats from existing experiences or illustrations?

Definition -

• What is the logical and most motivating model for defining our Service Lines?
• Can we best approach the universe of patient needs by primary disease type?
• Are we best aligned by major service or treatment modality, facilities, differentiating equipment or other assets?
• Is a combination of these best; aligned and integrated for each patient in a context of personalized medical care?
• Are there specific populations of patients, defined along some useful dimensions, to inform our service delivery or resource deployment?

Specification -

• How do we explicitly assign or allocate individual delivery events into one or another Service Line?
• What is the fundamental unit of assignment (e.g. a single case; a single encounter; an episode spanning admissions; longitudinal courses of care)?
• How do we address conflicts or overlap, where multi-disciplinary care or complex cases suggest more than one Service Line has a legitimate claim?
•  How are the resource and financial alignments defined and measured?

Metrics -

• What specific metrics do we use to direct our investments and measure the performance of those resources and the return on those assets?
• How do we devise a metrics strategy that integrates and leverages the complementary perspectives of clinical, operational, financial and administrative objectives?
• How does our metrics structure address and accommodate the structure of our enterprise (e.g. health system, hospitals, ambulatory groups, key stakeholders)?
• How do we assess comparative analysis and performance, both internally and externally?
• What are appropriate and useful benchmarks?

Organization -

• How do we organize our Service Lines?
• What model do we adopt (formal divisional, matrix, task force, hybrid)?
• How, in what form and to whom do we institute roles, authority, and accountabilities, and for what scope of mission and resources?
• How do we ensure clinical, operational and fiscal perspectives and guidance?
• What differences are necessary within or between facilities and other entities?
• What communications and information systems are needed?
• What incentive structures and programs are needed?

Change Management -

•  Where and in what form will we encounter resistance?
• What obstacles does our plan anticipate, and how will we overcome them?
• What limitations exist within our existing enterprise structure or resource base, and how must we address them?
• What forces are working in favor of the needed change, and how can we invigorate those efforts?

Ongoing Management -

• How will or must the focus, structures, resources and metrics change over time, and how do we anticipate and facilitate these challenges and opportunities in an evolutionary manner?
• How should we characterize the various individual Service Lines from a portfolio perspective; and how do we establish and implement appropriate investment strategies and accountabilities for individual Service Lines at their position in their life cycle and in the overall portfolio?
• What benchmarks or metrics can we adopt to enable an appropriate comparative assessment, both within the overall enterprise portfolio, and in our specific competitive context?

Building out integrated Service Lines is more than simply clarifying the operations of existing departments and specialty-focused medical services.  Trying to do better with a traditional alignment by professional specialty or discipline will likely not provoke the kinds of challenges to existing authority structures, realignment of complex multi-tiered cost allocations, and performance incentives needed to bring transformational improvements in quality and financial return.  The process must begin with a laser focus on patient outcomes and experience, and propagate backward to the fresh look that is required to evaluate the needed investments in the full context of distinctive strengths and competencies, competitive threats, market position, evidence of superior (actual or prospective) performance, demonstrable resources and capacity, and the strategic commitment to make it happen.

Implementing advanced analytics positions healthcare organizations to pursue a vision to:

• Facilitate measureable excellence in service delivery
• Provide insights and identify opportunities for market growth, business performance improvement, competitive advantage and return on investment
• Foster innovation in the design and pursuit of service lines and the efficient alignment of resources
• Promote the intelligent and proactive use of existing and emerging information assets from diverse sources
• Inform and empower leaders, strategists and decision makers at every level: enterprise, hospital, service line, care setting, department.

Electronic Medical Records ≠ Accurate Data

As our healthcare systems race to implement Electronic Medical Records or EMRs, the amount of data that will be available and accessible for a single patient is about to explode.  “As genetic and genomic information becomes more readily available, we soon may have up to 1,000 health facts available for each particular patient,” notes Patrick Soon-Shiong, executive director of the UCLA Wireless Health Institute and executive chairman of Abraxis BioScience, Inc., a Los Angeles-based biotech firm dedicated to delivering therapeutics and technologies that treat cancer and other illnesses.  The challenge is clear: how can a healthcare organization manage the accuracy of 1,000 health facts?

As the volume of individual data elements expands to encompass 1,000 health facts per patient, there is an urgent need for electronic tools to manage the quality, timeliness and origination of those data.  One key example is simply making sure that each patient has a unique identifier with which to attach and connect the individual health facts.  This may seem like a mundane detail, but it is absolutely critical to uniquely identify and unambiguously associate each key health fact with the right patient, at the right time.  Whenever patients are admitted to a health system, they are typically assigned a unique medical record number that both clinicians and staff use to identify, track, and cross-reference their records.  Ideally, every patient receives a single, unique identifier.  Reality, however, tells a different story, because many patients wind up incorrectly possessing multiple medical record numbers,  while others wind up incorrectly sharing the same identifier.

These errors, known respectively as master person index (MPI) duplicates and overlays, can cause physicians and other caregivers to unknowingly make treatment decisions based on incomplete or inaccurate data, posing a serious risk to patient safety.  Thus, it is no wonder that improving the accuracy of patient identification repeatedly heads The Joint Commission’s national patient safety goals list on an annual basis.

Assembling an accurate, complete, longitudinal view of a patient’s record is comparable to assembling a giant jigsaw puzzle.  Pieces of that puzzle are scattered widely across the individual systems and points of patient contact within a complex web of hospitals, outpatient clinics, and physician offices.  Moreover, accurately linking them to their rightful owner requires the consolidation and correction of the aforementioned MPI errors.  To accomplish this task, every hospital nationwide must either implement an MPI solution directly, hire a third party to clean up “dirty” MPI and related data, or implement some other reliable and verifiable approach.  Otherwise, these fundamental uncertainties will continue to hamper the effective and efficient delivery of the core clinical services of the extended health system.

Unfortunately, this issue doesn’t simply require a one-time clean-up job for most healthcare systems.  The challenge of maintaining the data integrity of the MPI has just begun.  That’s because neither an identity resolution solution, nor an MPI software technology, nor a one-time clean-up will address the root causes of these MPI errors on their own.  In a great majority of cases, more fundamental issues underlie the MPI data issue, such as flawed registration procedures; inadequate or poorly trained staff; naming conventions that vary from one operational setting or culture to another; widespread use of nicknames; and even confusion caused by name changes due to marriages and divorces – or simple misspelling.

To address these challenges, institutions must combine both an MPI technology solution, which includes human intervention, and the reengineering of patient registration processes or other points of contact where patient demographics are captured or updated.  Unless these two elements are in place, providers’ ability to improve patient safety and quality of care will be impaired because the foundation underpinning the MPI will slowly deteriorate.

Another solution is the use of data profiling software tools.  These tools allow the identification of common patterns of data errors, including erroneous data entry, to focus and drive needed revisions or other improvements in business processes.  Effective data profiling tools can run automatically using business rules to focus on the exceptions of inaccurate data that need to be addressed.  As the number of individual health facts increases for each patient, the need for automating data accuracy will continue to grow, and the extended health system will need to address these issues.

When healthcare providers make critical patient care decisions, they need to have confidence in the accuracy and integrity of the electronic data.  Instead of a physician or nurse having to assemble and scan dozens of electronic patient records in order to catch a medication error or an overlooked allergy, these data profiling tools can scan thousands of records, apply business rules to identify the critical data inaccuracies, including missing or incomplete data elements, and notify the right people to take action to correct them.

The time has come in the age of computer-based medical records that electronic data accuracy is now a key element in patient safety; as critical as data completeness.  What better way to manage data accuracy than with smart electronic tools for data profiling?  Who knows?  The life you save or improve may be your own.

How does a data-driven healthcare organization work?

As the pressure increases for accountability and transparency for healthcare organizations, the spotlight is squarely on data: how does the organization gather, validate, store and report it.  In addition, the increasing level of regulatory reporting is driving home a need for certifying data – applying rigor and measurement to its quality, audit, and lineage.  As a result, a healthcare organization must develop an Enterprise Information Management approach that zeros in on treating data as a strategic asset.  While treating data as an asset would seem to be obvious given the level of IT systems necessary to run a typical healthcare organization, the explosion of digital data collected and types of digital data (i.e. video, digital photos, audio files) has overwhelmed the ability to locate, analyze and organize it.

A typical example of this problem comes when an organization decides to implement Business Intelligence or performance indicators with an electronic dashboard.  There are many challenges in linking data sources to corporate performance measures.  When the same data element exists in multiple places, i.e. patient IDs, encounter events, then there must be a decision about the authoritative source or “single version of the truth.” Then there is the infamous data collision problem: Americans move around and organizations end up with multiple addresses for what appears to be the same person, or worse yet, multiple lists of prescribed medications that don’t match.  The need to reconcile data discrepancies requires returning to the original source of information – the patient to bring it to a current status.  Each of us can relate to filling out the form on the clipboard in the doctor’s office multiple times.  Finally, there is the problem of sparseness – we have part of the data for tracking performance but we don’t have enough for the calculation.  This problem can go on and on, but it boils down to having the right data, at the right time and using it in the right manner.

Wouldn’t the solution simply be to create an Enterprise Data Warehouse or Operational Data Store that has all of the cleansed, de-duplicated, latest data elements in it?  Certainly!  Big IF coming up: IF your organization has data governance to establish a framework for audit-ability of data; IF your organization can successfully map source application systems to the target enterprise store; IF your organization can establish master data management for all the key reference tables; IF your organization can agree on standard terminologies, and most importantly, IF you can convince every employee that creates data that quality matters, not just today but always.

One solution is to understand a key idea that made personal computers a success – build an abstraction layer.  The operating system of a personal computer established flexibility by hiding the complexity of different hardware items from the casual user through a hardware abstraction layer that most of us think of as drivers.  A video driver, a CD driver, USB driver allows the modularity and allows flexibility to adapt the usefulness of the PC.  The same principle applies to data-driven healthcare organizations.  Most healthcare applications try to tout their ability to be the data warehouse solution.  However, the need for the application to improve over time introduces change and version control issues, thus instability in the enterprise data warehouse.  In response, moving the data into an enterprise data warehouse creates the abstraction layer and the extract, transform and load (ETL) process can act like the drivers in the PC example.  Then as the healthcare applications move through time, they do not disrupt the Enterprise Data Warehouse, its related data marts and, most importantly, the performance management systems that run the business.  It is not always necessary to move the data in order to create the abstraction layer, but there are other benefits to that approach including the retirement of legacy applications.

In summary, a strong data-driven healthcare organization has to train and communicate the importance of data as a support for performance management and get the buy-in from the moment of data acquisition through the entire lifecycle of that key data element.  The pay-offs are big: revenue optimization, risk mitigation and elimination of redundant costs.  When a healthcare organization focuses on treating data as a strategic asset, then it changes the outcome for everyone in the organization, and restores trust and reliability for making key decisions.

It’s our data that’s hurting us! – Transparency in a consumer-oriented healthcare marketplace

The Internet is really enabling the healthcare consumer to shop and compare like never before.  Why would there be a need to shop for the better hospital, doctor or nursing home? The need to shop for quality care has never been more important and the competition between hospitals and other healthcare providers is heating up.  The consumer wants the best surgeon to do their procedure, in the safest hospital and they are turning to healthcare rating sites in record numbers.  The irony of rating sites is that they are dependent on data that is provided by the hospitals, doctors and other allied providers – it is their very own data that they are being judged by.

Today, it is easy for the consumer to Google “compare doctors” or “compare hospitals” and locate numerous websites with detailed information for comparisons.  Two notable examples are leapfroggroup.com and ucomparehealthcare.com.  Leapfrog does not just rely on publicly reported data from regulatory agencies but extends the information with detailed surveys of hospitals on the key issues: central line infections and infection control, for example.  One comparison website reports that the Top 5% of its reporting hospitals have a 29% lower mortality rate.

No individual or healthcare organization wants an unfair report card.  With medical mistakes as a leading cause of death each year surpassing car accidents, breast cancer and AIDS, the report card also serves healthcare organizations as guidance on critical areas of improvement.  The process to collect regulatory reporting information in many healthcare organizations is tedious, time-consuming and often manual.  In the classic sense of “we have the data somewhere but not in the format that we need it.”  There are several key problems with collecting core measures and other key metrics for reporting:

• Key data elements for the calculations are paper-based or manually compiled
• Manual process fatigue from paper form processing
•  Automated reporting systems use sample patient populations that are too small resulting in a possible statistical errors
• Errors in the data transfer process, especially in the hand-off of information from one area of the hospital to another skew results
• Inability to track a diagnosis code early enough in the patient encounter to improve on the measure outcomes
• Lack of staff training on collecting the right information at the right time in the right format

Consumers use the reported results to compare hospital performance and make decisions about where to receive care.  As a result, healthcare organizations need to focus on data governance to address treating data as an asset, ensuring data quality and tracking the right key metrics.  Addressing this challenge will not only improve the ratings report card for healthcare organizations but will demonstrate the commitment to quality data as well as patient safety.  Better data equals better results.  In the consumer-oriented healthcare marketplace, transparency of key metrics will yield competitive advantage.

Driving Value from Your Healthcare Analytics Program –Key Program Components

If you are a healthcare provider or payer organization contemplating an initial implementation of a Business Intelligence (BI) Analytics system, there are several areas to keep in mind as you plan your program.  The following key components appear in every successful BI Analytics program.  And the sooner you can bring focus and attention to these critical areas, the sooner you will improve your own chances for success.

Key Program Components

Last time we reviewed the primary, top-level technical building blocks.  However, the technical components are not the starting point for these solutions.  Technical form must follow business function.  The technical components come to life only when the primary mission and drivers of the specific enterprise are well understood.  And these must be further developed into a program for defining, designing, implementing and evangelizing the needs and capabilities of BI and related analytics tuned to the particular needs and readiness of the organization.

Key areas that require careful attention in every implementation include the following:

We have found that healthcare organizations (and solution vendors!) have contrasting opinions on how best to align the operational data store (ODS) and enterprise data warehouse (EDW) portions of their strategy with the needs of their key stakeholders and constituencies.  The “supply-driven” approach encourages a broad-based uptake of virtually all data that originates from one or more authoritative source system, without any real pre-qualification of the usefulness of that information for a particular purpose.  This is the hope-laden “build it and they will come” strategy.  Conversely, the “demand-driven” approach encourages a particular focus on analytic objectives and scope, and uses this focus to concentrate the initial data uptake to satisfy a defined set of analytic subject areas and contexts.  The challenge here is to not so narrowly focus the incoming data stream that it limits related exploratory analysis.

For example, a supply-driven initiative might choose to tap into an existing enterprise application integration (EAI) bus and siphon all published HL7 messages into the EDW or ODS data collection pipe.  The proponents might reason that if these messages are being published on an enterprise bus, they should be generally useful; and if they are reasonably compliant with the HL7 RIM, their integration should be relatively straightforward.  However, their usefulness for a particular analytic purpose would still need to be investigated separately.

Conversely, a demand-driven project might start with a required set of representative analytic question instances or archetypes, and drive the data sourcing effort backward toward the potentially diverging points of origin within the business operations.  For example, a surgical analytics platform to discern patterns between or among surgical cost components, OR schedule adherence, outcomes variability, payer mix, or the impact of specific material choices would depend on specific data elements that might originate from potentially disparate locations and settings.  The need here is to ensure that the data sets required to support the specific identified analyses are covered; but the collection strategy should not be so exclusive that it prevents exploration of unanticipated inquiries or analyses.

I’ll have a future blog topic on a methodology we have used successfully to progressively decompose, elaborate and refine stakeholder analytic needs into the data architecture needed to support them.

In many cases, a key objective for implementing healthcare analytics will be to bring focus to specific areas of enterprise operations: to drive improvements in quality, performance or outcomes; to drive down costs of service delivery; or to increase resource efficiency, productivity or throughput, while maintaining quality, cost and compliance.  A common element in all of these is a focus on process.  You must identify the specific processes (or workflows) that you wish to measure and monitor.  Any given process, however simple or complex, will have a finite number of “pulse points,” any one of which will provide a natural locus for control or analysis to inform decision makers about the state of operations and progress toward measured objectives or targets.  These loci become the raw data collection points, where the primary data elements and observations (and accompanying meta-data) are captured for downstream transformation and consumption.

For example, if a health system is trying to gain insight into opportunities for flexible scheduling of OR suites and surgical teams, the base level data collection must probe into the start and stop times for each segment in the “setup and teardown” of a surgical case, and all the resource types and instances needed to support those processes.  Each individual process segment (i.e. OR ready/busy, patient in/out, anesthesia start/end, surgeon in/out, cut/close, PACU in/out, etc.) has distinct control loci the measurement of which comprises the foundational data on which such analyses must be built.  You won’t gain visibility into optimization opportunities if you don’t measure the primary processes at sufficient granularity to facilitate inquiry and action.

Each pulse point reveals a critical success component in the overall operation.  Management must decide how each process will be measured, and how the specific data to be captured will enable both visibility and action.  Visibility that the specific critical process elements being performed are within tolerance and on target; or that they are deviating from a standard or plan and require corrective action.  And the information must both enable and facilitate focused action that will bring performance and outcomes back into compliance with the desired or required standards or objectives.

A key aspect of metric design is defining the needed granularity and dimensionality.  The former ensures the proper focus and resolution on the action needed.  The latter facilitates traceability and exploration into the contexts in which performance and quality issues arise.  If any measured areas under-perform, the granularity and dimensionality will provide a focus for appropriate corrective actions.  If they achieve superior performance, they can be studied and characterized for possible designation as best practices.

For example, how does a surgical services line that does 2500 total knees penetrate this monolithic volume and differentiate these cases in a way that enables usable insights and focused action?  The short answer is to characterize each instance to enable flexible-but-usable segmentation (and sub-segmentation); and when a segment of interest is identified (under-performing; over-performing; or some other pattern), the n-tuple of categorical attributes that was used to establish the segment becomes a roadmap defining the context and setting for the action: either corrective action (i.e. for deviation from standard) or reinforcing action (i.e. for characterizing best practices).  So, dimensions of surgical team, facility, care setting, procedure, implant type and model, supplier, starting ordinal position, day of week, and many others can be part of your surgical analytics metrics design.

Each metric must ultimately be deconstructed into the specific raw data elements, observations and quantities (and units) that are needed to support the computation of the corresponding metric.  This includes the definition, granularity and dimensionality of each data element; its point of origin in the operation and its position within the process to be measured; the required frequency for its capture and timeliness for its delivery; and the constraints on acceptable values or other quality standards to ensure that the data will reflect accurately the state of the operation or process, and will enable (and ideally facilitate) a focused response once its meaning is understood.

An interesting consideration is how to choose the source for a collected data element, when multiple legitimate sources exist (this issue spills over into data governance (see below); and what rules are needed to arbitrate such conflicts.  Arbitration can be based on: whether each source is legitimately designated as authoritative; where each conflicting (or overlapping) data element (and its contents) resides in a life cycle that impacts its usability; what access controls or proprietary rights pertain to the specific instance of data consumption; and the purpose for or context in which the data element is obtained.  Resolving these conflicts is not always as simple as designating a single authoritative source.

Controlling data quality at its source is essential.  All downstream consumers and transformation operations are critically dependent on the quality of each data element at its point of origin or introduction into the data stream.  Data cleansing becomes much more problematic if it occurs downstream of the authoritative source, during subsequent data transformation or data presentation operations.  Doing so effectively allows data to “originate” at virtually any position in the data stream, making traceability and quality tracking more difficult, and increasing the burden of retaining the data that originates at the various points to the quality standard.  On the other hand, downstream consumers may have little or no influence or authority to impose the data cleansing or capture constraints on those who actually collect the data.

Organizations are often unreceptive to the suggestion that their data may have quality issues.  “The data’s good.  It has to be; we run the business on it!”  Although this might be true, when you remove data from its primary operating context, and attempt to use it for different purposes such as aggregation, segmentation, forecasting and integrated analytics, problems with data quality rise to the surface and become visible.

Elements of data quality include: accuracy; integrity; timeliness; timing and dynamics; clear semantics; rules for capture; transformation; and distribution.  Your strategy must include establishing and then enforcing definitions, measures, policies and procedures to ensure that your data is meeting the necessary quality standards. 

The data architecture must anticipate the structure and relationships of the primary data elements, including the required granularity, dimensionality, and alignment with other identifying or describing elements (e.g. master and reference data); and the nature and positioning of the transformation and consumption patterns within the various user bases.

For example, to analyze the range in variation of maintaining schedule integrity in our surgical services example, for each case we must capture micro-architectural elements such as the scheduled and actual start and end times for each critical participant and resource type (e.g. surgeon, anesthesiologist, patient, technician, facility, room, schedule block, equipment, supplies, medications, prior and following case, etc.), each of which becomes a dimension in the hierarchical analytic contexts that will reveal and help to characterize where under-performance or over-performance are occurring.  The corresponding macro-architectural components will address requirements such as scalability, distinction between retrieval and occurrence latency, data volumes, data lineage, and data delivery.

By the way: none of this presumes a “daily batch” system.  Your data architecture might need to anticipate and accommodate complex hybrid models for federating and staging incremental data sets to resolve unavoidable differences in arrival dynamics, granularity, dimensionality, key alignment, or perishability.  I’ll have another blog on this topic, separately.

You should definitely anticipate that the incorporation and integration of additional subject areas and data sets will increase the value of the data; in many instances, far beyond that for which it was originally collected.  As the awareness and use of this resource begins to grow, both the value and sensitivity attributed to these data will increase commensurately.  The primary purpose of data governance is to ensure that the highest quality data assets obtained from all relevant sources are available to all consumers who need them, after all the necessary controls have been put in place.

Key components of an effective strategy are the recognition of data as an enterprise asset; the designation of authoritative sources; commitment to data quality standards and processes; recognition of data proceeding through a life cycle of origination, transformation and distribution, with varying degrees of ownership, stewardship and guardianship, on its way to various consumers for various purposes.  Specific characteristics such as the level of aggregation; the degree of protection required (e.g. PHI); the need for de-identification and re-identification; the designation of “snapshots” and “versions” of data sets; and the constraints imposed by proprietary rights. These will all impact the policies and governance structures needed to ensure proper usage of this critical asset.

Are you positioned for success?

Successful implementation of BI analytics requires more than a careful selection of technology platforms, tools and applications.  The selection of technical components will ideally follow the definition of the organizations needs for these capabilities.  The program components outlined here are a good start on the journey to embedded analytics, proactively driving the desired improvement throughout your enterprise.

Is it Healthcare Reform or Healthcare Payer Reform?

Most people believe that “Healthcare Reform” means reform of the providers, but if you look at the system in total, is the problem with the providers or payers?   Some employers are seeing 20-50% increases in their annual rates; these increases are being passed along to employees in higher rates, decreased coverage, and higher co-pays and deductibles.  The providers claim they are being squeezed with increasing costs and decreasing reimbursements, so this begs the question, “what is happening to the dollars from the increased rates?”  The healthcare payers are realizing increased profits while covering fewer lives, a great business model in these economic times.

In a recent article by John D. Atlas on nj.com, he indicates that the five (5) largest health insurance companies scored record profits of $12.2 billion an increase of 56% over 2008.  The article further explains that while profits have increased, payers are covering 2.7 million less lives.  According the Center for Responsive Politics, the insurance industry has spent $77M lobbying to defeat the healthcare reform bill.  Ms. Kathleen Sebelius, the secretary of health and human services, recently took a shot at Anthem Blue Cross for the 39% increase in health care costs when the company made $2.7 billion in the 4^th quarter of 2009.   The payers are concerned about keeping their companies in business; guaranteed issue (GI) would put some of the national companies out of business.

What about the new technology and new treatments that are constantly being introduced which can extend lives?  Hospital stays are shorter, outpatient surgery is becoming more common for procedures that once required overnight stays.  New technology and procedures that are minimally invasive are replacing more complicated surgeries.  The immediate thought is that new technology and shorter stays should decrease the cost of healthcare; but what about the increased costs in research and development, technology, and training costs?    

How does this impact the average citizen?  Many studies have shown that people have been forced to reduce the amount of money they spend on healthcare by techniques such as using OTC drugs; home remedies instead of going to the doctor; skipping health checkups and cutting doses of medications; postponing needed healthcare; and not filling prescriptions.   Average citizens are feeling the pinch of the increased healthcare costs through decreased available care, and through less disposable income.

If the government isn’t successful in reforming healthcare through legislation, consumers will force the reform.  Currently consumers are in a silent rebellion, but savvy consumers are starting to shop around.  As the government continues to struggle to make progress, consumers are becoming more educated and “shopping” around for services.  I recently found that by signing up through a national chain’s prescription program that I was able to reduce my medication costs for three months from $105 to $10.  Everyone should review their healthcare options and become informed consumers by looking at their medication plans; asking more questions of physicians related to tests and procedures to understand why they are being done; and generally being a more informed consumer.  

Source: Edison/Mitofsky, National Exit Poll, sponsored by the National Election Pool. Conducted November 4, 2008.

According to The Henry J. Kaiser Family Foundation report in March of 2009, 16% of the U.S. economy is devoted to healthcare and the U.S. spends $7,400 per person each year.  In the exit polls after the 2008 Presidential election, 65% of respondents were concerned about healthcare.  I am confident that of the 33% not concerned, a percentage has moved to the “worried” category.

So what is the next step in Healthcare Reform?  The White House has sent out invites for the health-care summit on February 25 to select Democrats and Republicans in the House and Senate.  The President believes that through bipartisanship they can come to a resolution between the parties to move forward.   There is no doubt that the healthcare system is broken; the question is where, how bad, and how to fix it.  The tactical focus needs to be on reducing the rising costs for healthcare, and then strategically how to provide quality healthcare for citizens at a reasonable cost.  However, do we really want the federal government to be the one to take control of our healthcare? 

What can payers do to remain competitive and viable?  They can look for ways to cut or reduce IT systems costs through modernization, consolidation, or replacement.  Business process improvements can streamline the processes and reduce costs.  Provider, agent, and member portals can be implemented to provide a competitive advantage with better access to needed information.  Through an enterprise data strategy, Payers can leverage the data collected and develop reports and dashboards around key performance indicators (KPI’s), driving improvements throughout the enterprise’s operations.  These changes can put an organization on a path toward measurable improvement; not a bad first step toward badly needed reform.