Six Core Principles for Transforming Healthcare – People, Technology, Data & Analytics

Digital technologies are changing the landscape of healthcare service delivery and raising patient expectations on where, when and how they engage with providers – and payers. Leading organizations are responding to these challenges and opportunities by implementing patient-centric communications and analytical tools and changing how they deliver core services – transforming their business models, operations and the patient experience in the process. To understand the legitimate potential offered by these tools, we need to unpack the buzzwords and examine the benefits and risks of specific digital capabilities – and then consider what they enable in a healthcare service delivery setting.

The following six core principles should be at the heart of every digital transformation initiative, large or small. While we have found these primary drivers to be applicable across various industry settings, here we outline their specific relevance to Healthcare.

1. Business Driven. Many digital technology initiatives in healthcare are driven by one or more core elements of the Triple Aim:

· Improve the health of populations – this principle is driving virtually every organization to identify and track populations of high-risk, over-utilizing patients; establish agreed-upon outcomes goals for defined segments and strata with similar characteristics or needs; and measure the impact of care plans tailored for each individual patient;

· Reduce the per-capita costs of care – value-based reimbursement programs and other risk-based arrangements are focusing attention on both clinical outcomes and financial results – driving the need for self-service analytics for patients, providers and payers – to measure the actual costs of care delivery for each patient;

· Improve the patient’s experience of receiving health care services – increasing transparency and coordinating patient-focused care across an expanding set of partners and providers helps to deliver the right care at the right time in the right setting – increasing patient satisfaction and improving compliance with care plans.

All the above elements are driving the need for better integration of primary service delivery processes and the resulting data streams – motivating an increasing availability of business intelligence (BI) and analytics capabilities and an omni-channel communication platform across the entire enterprise value chain. Digital technologies must be part of every aspect of the overall business-level strategy.

How are you anticipating the needs for and incorporating the capabilities of digital devices and data streams into your business execution and communications strategies?

2. Data is a Core Asset. Organizations that define, measure and adjust their operations using diverse and relevant data sets will realize many performance advantages – to the benefits of all stakeholders.

· Assembling Good Data – capturing enterprise information in digital format – and verifying the quality of those data sets against defined standards for completeness, accuracy and veracity – is an absolute foundation for preparing and enabling digital transformation. The core data systems for the execution of primary transactions and analysis of results must be credible and trustworthy – and this is only achieved – like any relationship – over a period of consistent behavior and positive results.

· Not a Simple Task – for many, this is a major challenge and a significant hurdle to overcome. Most operations are dependent upon data sets that originate in multiple legacy source systems – many of which are too narrowly focused or too closely aligned with aging or inflexible business applications. Understanding the actual contents of these older systems is challenging – envisioning their utility and engineering their transformation for novel purposes represents the “heavy lifting” of data integration. These efforts are difficult to quantify based on a direct ROI – and they are very often on the critical path to deploying and making effective use of newer digital technologies. However, opening these core assets to more transparent use by diverse participants will very often yield unanticipated benefits.

· Incremental Strategy – many organizations will not be able re-architect their data systems from the ground up – in these cases, a more incremental approach is much more viable. Most organizations will begin with a more focused implementation, building the data supply lines to capture and move data from core operational sources into a data warehouse or set of data stores optimized for BI and analytics.

· Managing Data as an Asset – proactive data governance that designates authoritative sources, establishes and enforces quality criteria, defines and assigns roles and responsibilities for managing defined data sets, and facilitates the use of data for various purposes is a critical aspect of any successful implementation.

· Anticipating Scale – the incorporation of so-called “big data” is also growing in importance in healthcare. The volumes, variety and value of these expanding and emerging data sets is driving further elaboration of the data flows, validation criteria, storage approaches and dissemination for novel use cases and analytical applications.

3. Actionable Analytics. Digital interactions – whether improved access to diverse data sources or primary transactions – are most valuable when self-service users can make timely and informed decisions and take appropriate actions based on what the data is indicating.

As the scope, diversity and ubiquity of digital devices continues to grow, the capture and dissemination of data will spread – and more users will be better informed about both the specific details and the broader context of their operating choices.

· Patients can access their care plans – and be completely up to date on their responsibilities for complying with expectations on medications, lab results, diet, exercise, follow-up appointments and monitor their overall progress toward agreed-upon clinical goals;

· Providers can access populations – and can stratify sub-segments of their panels according to clinical risk and compliance – tailoring their communications and interventions to keep patients on-track with their outcomes goals;

· Payers can review patient populations and provider networks – identifying attributed patient groups against value-based performance goals and profile provider effectiveness in meeting clinical and financial goals on risk contracts and alternative payment models.

All these capabilities empower the various user groups to more clearly understand and localize the issues and factors underlying excellent or poor performance – and focus the reinforcing or remedial actions to the benefit of all stakeholders.

4. Patient-Centered Experience. A key driver and a widely recognized benefit of the increasing availability of digital technologies is their ability to both stimulate demand and meet the rising expectations of patients for convenient access to all forms of healthcare information and services through their hand-held or wearable devices.

· Ubiquity – the emergence of the “connected anywhere, information everywhere” operating experience has given patients greater power and influence in engaging and steering their relationship with providers. So-called “activated patients” are more equipped to make informed choices and take the initiative to research their conditions, identify and understand their care alternatives, communicate and coordinate with care providers, exchange stories and find support from other patients in shared-need communities, set agreed-upon goals for their care with their providers, and measure their results.

· Flexibility – providers can no longer hold fast to rigid or single-stream operating models – imposing their internal structures, processes and workflows onto patients from the inside out. For digitally-enabled patients, the care experience is becoming much more of a self-directed journey. Providers who recognize this reorientation to facilitate the “Patient Journey” and unbundle and organize their delivery of services according to this revised model will realize greater patient satisfaction with their own care experiences, better compliance with care plans, and improved outcomes – both clinical and financial.

· Adaptability – similarly, payers are coming under increasing pressure to unbundle and adapt to the disaggregating needs and demands of their patients (members). Patients are seeking customized configurations of benefits packages that are more cost-effective and focused on their specific anticipated needs for services. These trends will continue to play out as more patients enter the individual market for health insurance products and payers are forced to adapt and devise new benefits plans.

5. Agile Technologies, Agile Processes. Agility must be a core value throughout the transformation effort – it must pervade every aspect of envisioning, defining, designing and implementing solutions in this continually evolving setting. The unbundling of service components and their flexible deployment and execution on-demand to patients and other users will create new challenges for providers.

· Feedback and Response – having an agile structure will enable more responsive delivery models – and capturing data at each point of interaction and each touch point along the Patient Journey enables near-real-time analysis of service delivery, care compliance, and their impact on outcomes. It allows feeding of detailed care experience data back into processes and workflows to enable greater personalization, better communication, and more accurate and effective segmentation for population analytics.

The commitment to an agile operation carries additional demands and benefits that must be considered as part of the transformation strategy:

· De-Coupling – tightly coupled databases, applications, custom code, execution logic and various other technical components can complicate the process of revising or enhancing services and their operation – an agile architecture will mandate an explicit de-coupling and un-bundling of tightly-bound components.

· Rapid Application Development – the technical environment and the operational culture must encourage and enable experimentation – where minimally vetted ideas can be prototyped and evaluated – facilitating an ongoing and in some sense relentless exploration of new areas for improvement or innovation.

· Infrastructure – the cloud explicitly provisions a clearly defined, precisely tuned and proactively managed capacity for services delivery and data access – ready to invoke and activate (or deactivate) as the demand specifically ramps up and down – the responsive and adaptive provisioning of this capacity of computing resources increases both the effectiveness and the efficiency of the business operations and the satisfaction of stakeholders.

· Cloud Orchestration – these unbundling and decoupling features combine to enable and facilitate a more agile operation. The execution model for the primary data sources and system services becomes one of flexible activation and deactivation of cloud-deployed capacities at a more granular level – tuned to the needs and demands of the external users rather than the constraints of internal operations.

6. Security & Access Control – the increased openness of these services demands more rigorous and reliable levels of security – including data security, application security, data encryption, compliance with regulations, and more informative monitoring of the ongoing state of the systems. Threats to on-line computing resources continue to rise as the incidence of hacking, data stealing, and denial of service attacks increases in number and sophistication. Added attention to risk management, strict adherence to appropriate security standards, and regular audits must be part of any such initiative.

The increasing availability of digital technologies is reinforcing expectations of timeliness, flexibility and convenience with patients, care givers, providers and payers in an evolving ecosystem of service delivery and information exchange. The relentless focus on quality and outcomes, cost control, value creation, and satisfaction will continue to drive innovation in service delivery across an expanding and diversifying network of healthcare industry participants. Organizations and individuals that respond and adapt will realize distinct advantages in both clinical and financial performance.

Moving from Volume to Value: How Do We Get There?

volume to valueListen to any healthcare pundit or industry observer longer than their opening paragraph and you’ll hear them use the current buzz phrase: Healthcare needs to move from volume to value.  See, there it is already.

We pretty much know where the volume comes from.  If a buyer, any buyer, agrees to pay an acceptable fee for each unit of a needed service, the service provider will soon recognize that the delivery of more units of those services to satisfied buyers yields more payments.  Simple enough, and healthcare service providers have responded as one would expect in this environment.  The fees paid for each unit of service motivate the healthcare provider to maximize throughput – to the limits of their capacity to deliver a satisfactory service – and this leads to an increased volume.  Yes, there’s also quality and necessity and regulation, but right now we’re talking about volume.

So, what about the value part?  What is going to drive value?  And value to whom?  And who is going to measure the value?  And decide how much to pay for it?  Unfortunately, several tenets of basic economics that ordinarily drive value (and operate in virtually every other transactional setting) are disrupted in the healthcare marketplace as payers and providers of every shape and size, employer- and other group-inspired benefit plans, preferred provider and referral networks, watchdog quality and safety groups – as well as the inherent complexity of the subject matter – all serve to distance the patient from a free and informed buying decision.

By the time the ultimate treatment decision is made, it has been framed and prodded by so many ancillary parties that the patient, sitting alone with the provider, can almost feel the other observers in the room; those who will decide after the fact, or who have decided well before the fact, whether this decision is appropriate and how each party in the transaction will be compensated, billed, measured, rewarded or penalized according to a growing litany of performance measures.  If the patient doesn’t feel all of this, the provider often does.

The complexity and confusion arise in part because each of the above fundamental elements has been intermediated – to one degree or another.  The patient – the ultimate target of the treatment – is not the only buyer.  Buying decisions affecting this single transaction were defined, negotiated and contracted months or years in advance, and are being monitored against a wide range of both clinical and financial measures before, during and after the single transaction between a given patient and a given provider.  The terms of these agreements can and do influence the chain of decisions that culminate in the choice of treatment and in the cascade of financial events that will promptly follow.  No wonder it’s confusing.

So then, how does value get defined?  Several common themes emerge as both payer and provider organizations strive to identify the appropriate fundamentals, define a useful and informative notion of value, and introduce that notion of value into the decision processes they share with their patients.

The common elements that lead to a determination of value seem to go something like this:

  • Who are my patients?  A fundamental question, but not always trivial to answer accurately or in a useful way that enables and extends visibility over a population.  Providers need to be able to identify each patient that is legitimately under their care and they must have access to a complete record of the care these patients have received as a baseline for measuring future performance.  Once this record is assembled the pattern of problems, interventions and care relationships can be discerned and used to both characterize and engage each patient.

Providers need to identify the core characteristics of their panel of patients so they can both tailor individual treatments and evaluate patient experiences and outcomes comparatively against similar patients they are treating or that are being treated by other providers.

In an accountable care world, if providers are assigned responsibility for patients retrospectively using a plurality of care or other statistical model, it doesn’t mean they have control over the care those patients are receiving. They can hardly be measured fairly on the outcomes those patients have experienced. They need to know the specific treatments these patients have received, their level of compliance, and what other providers they have seen, at what locations, and with what frequency. This increased understanding of their basic patient panel will begin to reveal the true nature of the relationships they (or others) have with these patients and will often constitute the first wave of relevant analytics into the value being delivered.

  • What outcomes are we targeting for these patients?  What are the care plans that will get them there?  How long have these targets and plans been in place, for which patients, and what results are we seeing?The segmentation analytics that was started with the patient panel can now be extended, as specific performance targets are defined for individual patients and the projection of these targets is aggregated into clinically coherent segments, yielding outcomes and results that perhaps for the first time give visibility and insight into how well the relevant population is being managed.

Care teams and practice management can now monitor the clinical, operational and financial performance measures of the segments that drive significant costs and consume substantial resources, enabling the exploration of new deployment models.

  • What is our baseline? For the patients’ and other payers’ expenditures for our services and for the actual costs we incur to deliver those services?  Virtually all risk-based contracts establish a baseline of expenditures using some form of statistical measure (e.g., weighted average) over a defined historical time frame.  Projecting the dynamics of patient mix, service mix, fee structures and delivery resources over the anticipated life of the contract provides a segment-able baseline for measuring and tracking contract performance and assessing value. From this foundation, organizations can apply complementary analytics so that under-performing practice areas or population segments can be localized and improvement programs can be appropriately focused and funded.  Over-performing segments can be examined and highlighted as potential sources of best practices targeted for broader dissemination.
  • Who is accountable, and for what?  As the payment structure for many conditions moves to more of an episode-based model, the deployment and coordination of care delivery resources takes on added significance.  Roles and responsibilities must be defined for the delivery of episode-focused clinical services across the network of care settings. Proactive coordination of transitions in care and the associated communications, hand-offs and follow-ups must be defined and written into performance contracts along with explicit adherence measures.

These metrics will begin to form the basis for concrete and measureable accountability models and will likely be a consideration when shared gains and losses are assessed retrospectively.  Evolution toward more proactive accountability models is likely to follow.

Accountability models based on the actual outcomes realized, as distinct from adherence to best practices, can be differentiated through analytics, enabling some flexibility for care redesign (potentially including patient choice) or other measured innovations undertaken by providers.

  • Are we correctly and accurately reconciling the various activities, billed services, payments, resource alignment and costs with our agreed-upon models for accountability?  This is non-trivial even within a single enterprise.  And now we have various ACO or ACA models where new participants are collaborating at levels they have never attempted before, and entering into risk agreements based on shared performance metrics.  Some organizations are experimenting with formal value stream maps where benefits and costs are explicitly modeled.  Others are punting any envisioned gains (or losses) to an aggregate ‘shared benefit’ to be ‘addressed later.’One key consideration is implementing at least some accounting (defining and tracking) of the revenues and the actual costs associated with care delivery to specific segments with different characteristics (e.g., populations, locations, groups, payers, service partners or venues).  These costs must not be (but often are) confused with the amounts the provider would like to charge; or the allowed amounts the payer will agree to; or the actual payment amounts received from all parties; or even the various provisional ratios used to approximate the real costs.  Accountability models will need to evolve much further if they are to offer any real operational decision-making value.

No one disputes that the changes underway in healthcare have the potential to be transformational, to varying degrees.  The complexity and diversity of the responses that will be required by various organizations is still taking shape and there are many variables that will determine the success that any given enterprise will achieve.

The core principles outlined here are being adopted and applied in diverse healthcare organizations to answer a few fundamental questions about the value they offer that, ironically, have been posed and answered for all time in other industries and economic settings.  Who is our customer?  What do they need or want?  Why are we the best organization to meet their needs?  How can we communicate the benefits and costs to all the parties who are involved in the decision to buy?  Can we deliver?  How can we measure these factors both as a baseline and on an ongoing basis so we can provide convincing evidence that we offer the best proposition of value to all concerned?  Healthcare organizations that can answer these questions and address the numerous issues that arise in their pursuit will have a leg up on everyone else and will both deliver the best value and enjoy the greatest success.

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.