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Readers Write 4/16/12

April 16, 2012 Readers Write No Comments

Submit your article of up to 500 words in length, subject to editing for clarity and brevity (please note: I run only original articles that have not appeared on any Web site or in any publication and I can’t use anything that looks like a commercial pitch). I’ll use a phony name for you unless you tell me otherwise. Thanks for sharing!

Making More Meaningful Use of Data Through Device Integration
By Stuart Long

4-16-2012 8-06-17 PM

Far and away, the main theme of Meaningful Use is an increased focus on making health information exchange not simply a capability, but a reality. As providers seek reimbursement for technology implementations designed to do just this, they need to take a step back to understand what is necessary to go beyond incremental improvements in order to see the larger picture – which means going further than Stage 2 to Stage 3 and beyond.

There has been recent discussion around the importance of medical device integration (MDI) as a necessary component on the path toward achieving health IT (HIT) initiatives such as Meaningful Use, HIE, and ACOs, among others. Healthcare providers need to understand the impact medical device integration can have across the entire hospital enterprise – “the big picture.”

While not addressed in Stage 2 (which takes effect in 2014), medical device interoperability is a stated 2015 objective. Stage 3 criteria are obviously yet to be detailed and finalized, but one of the criteria is for medical devices to be interoperable with EMRs and clinical information systems.

The theory of medical devices being interoperable is a good one. However, the chance of this actually being achieved across all device manufacturers is not realistic under the stated timeframe. Only a small fraction of devices today can send interoperable HL7 data. This means that many of the devices already installed within the hospital are not interoperable. Therefore, hospitals may be required to purchase new devices to meet the objective. With already strained budgets and resources, many hospitals would not be able to do so.

The most realistic means to meet the interoperability objective now and in the future is by implementing a vendor-neutral connectivity solution that would convert all data from all connected devices to HL7 so multiple people receiving information system(s) can accept it. Such a solution would enable interoperability, allow a hospital to use the equipment they have in place today, and minimize the points of integration for easier management, flexibility, and scalability – key ingredients to deriving real value out of required technologies like EMRs, CPOE and others.

Beyond Meaningful Use, the question is: how can hospitals fully leverage MDI to deliver the even greater benefit of transforming patient safety and outcomes? Imagine the ability to take collected data and compare, contrast, and analyze it from multiple sources, and then deliver it back to caregivers in a meaningful way. Imagine the ability to effectively manage smart pump connectivity and bi-directional communication. These are all possible through a middleware, vendor-neutral device integration solution.

However, let’s be realistic about the timeframe to make such possibilities a reality. For true end-to-end and bi-directional communication to become a reality, there are multiple factors that will have to come in to play. Multiple vendors with varying degrees of responsibility and intellectual property will need to communicate and operate with one another in order to make the data collected meaningful and to ensure that such data is presented back to the caregiver or other healthcare professionals in a meaningful way.

While this will take time, there really is only one way to facilitate this exchange of data – through a middleware provider who has established relationships with all the vendors in the mix: device manufacturers, information system providers, system integrators, and predictive outcome vendors. Having middleware that is vendor neutral gives hospitals the advantage of being able to bridge the gap between these worlds.

The point is, device integration is evolving. It is going beyond the simple connection of devices to systems. The next evolution will be using the data collected so it can be compared, analyzed, and delivered back to the healthcare provider and healthcare executives in ways that will truly transform patient care and outcomes. While it will take time, it isn’t a matter of whether it can be done — just when it will be done.

The beauty is hospitals can realize all the many benefits of device integration today (improved patient care, reduced errors, improved decision making, and even Meaningful Use) and position themselves to then realize the many benefits coming in the future. It’s a win/win, really, because device integration aligns with the ever-growing strategic approach to technology investments and implementations — to increase efficiencies and improve patient care.

Meaningful Use requirements will come and go, but hospitals will still remain. Decisions and investments made now will have a long and lasting impact on the future of healthcare. The best approach is to create an agile, scalable healthcare environment that can adapt to the changing needs of patients for years to come. Medical device integration is one technology that aligns with all of these objectives and more.

Stuart Long is president, North America of Capsule Tech, Inc. of Andover, MA.

Clinical Intelligence to Improve Quality and Reduce Costs
By Michael Weintraub

4-16-2012 7-54-00 PM

The business model for healthcare is changing very quickly and most providers do not have the information resources to support value and risk-based accountable care. What is needed now is longitudinal information that is patient / population centric, across the continuum of care, outcome and health status oriented. It must support performance improvement and cost management, particularly for disease states such as congestive heart failure, hypertension, diabetes, asthma and others, where better management impacts health status and reduces total costs.

Accountable care requires clinical intelligence – information resources and analytical tools – to improve care to populations, over time and across the care continuum. Analytics is a tool for extracting useful properties from data, but intelligence is about making sense of the data and figuring out what to do about the findings.

Quality improvement in recent decades has been aligned with a volume driven fee for service business model. Claims based data analytics and process measures were adequate, though their value in improving care has been disappointing despite the commitment and best efforts of so many. As Chassin and Loeb conclude, “Health care quality and safety today are best characterized as showing pockets of excellence on specific measures or in particular services at individual health care facilities.” 1

As we move toward a value-based system with accountability over time, the focus of analytics is shifting as well. Historically the field of “analytics” only encompassed scorecards focused on traditional quality measures (e.g. aspirin on arrival for MI patients). But as the business model of health care shifts from fee-for-service to fee-for-value, organizations have also had to shift their analytic focus from “service” in the form of traditional process-based measures to “value” in the form of population health. This shift has driven expanded requirements for more robust clinical intelligence and predictive analytics to measure, understand and drive improved clinical performance tied directly to the bottom line.

Clinical data is the anchor for clinical intelligence and vanguard IDNs, hospitals, and medical groups are using clinical intelligence (CI) solutions that unlock the value of digital clinical data. Adoption of HIT is an enabling but not sufficient prerequisite for CI. Data warehousing and registries may also be enabling, but they are not CI. CI requires four advanced capabilities: data management, data quality, analytics, and shared learning.


Data Management

Even organizations with the most comprehensive EHRs find their data difficult to access and extract for analysis. Data formats and definitions are not standardized across IT applications or across entities even in the same enterprise. Extracting, organizing, and normalizing clinical, financial, and operational data from disparate systems and across the care continuum — inpatient and ambulatory — is key to unlocking intelligence in the data. Data management functions can be performed behind the scenes on a near real-time basis avoiding costly interfaces. They should tap valuable unstructured data using natural language processing to enhance the value of the extracted and normalized database for population management.

Data Quality Services

One of the persistent concerns of those who use data or are the subject of that data is concern about its accuracy and validity. These concerns are well grounded. The explosive growth of digital information with poor data governance has led to a state of disorder that has done little to improve trust and willingness to act on data.

This problem is compounded exponentially when trying to mine clinical data from EMRs. Unlike the well-understood structures and nomenclatures that support ICD, DRG, and CPT coding, clinical data are unstructured and unlimited in terms of their heterogeneity. CI solutions solve this problem by performing forensics that clean, validate, and map the data. These data quality processes provide insight into the areas ripe for data quality improvement in EHR and other data sources and enables monitoring data quality over time. The result of data management and data quality is a continuously refreshed database ready for use.

Analytic Technologies

CI employs analytic tools that are clinically and statistically rigorous and transparent so it is easy to access and understand the underlying data. Innovations in advanced data visualization and analysis guidance such as report libraries support a broad range of uses from clinical performance profiling to dashboards and analyses of at risk populations. For at risk patients and populations — for example, CHF patients — CI uses predictive analytics to identify where intervention may prevent hospitalization. Valid comparative data for benchmark analyses is an essential component of CI and a prerequisite for sustainable performance improvement. Smart analytic tools also help support employees who are learning to work with expanded data sets and new tools.

Shared Learning Resources

Over and over, it has been shown that quality and performance improvement benefits from collaborative learning. Using normalized and comparative data, CI leaders engage with one another through learning communities, such as those being convened through the American Medical Group Association (AMGA). With CI, the clinical comparative data and analytics are the glue for the community of stakeholders actively engaged in learning from one another.

Leading healthcare organizations preparing for value and risk-based accountable care understand they must move beyond limited purpose process measures and claims data to CI. They are leveraging their investments in HIT and unlocking the power of clinical data for population management and health system improvement.

[1] Chassin, M. and Loeb, J. “The Ongoing Quality Improvement Journey: Next Stop, High Reliability.” Health Affairs, 30, no.4 (2011): 559-568

Michael Weintraub is president and CEO of Humedica of Boston, MA.

How are you Managing your Revenue Cycle?
By John O’Donnell

4-16-2012 8-01-11 PM

The complexity of managing the revenue cycle has never been greater than in today’s healthcare environment. From the economic impact on an organization’s bottom line to the continued advancement of healthcare reform, the need to stay three steps ahead has never been more important for your organization’s financial health.

Staying ahead means knowing your strengths and weaknesses. Do you have the right talent? Do you know what the market conditions are doing to your revenue cycle? How do you approach declining reimbursements without impacting quality or strategic initiatives? These are not easy questions to answer.

Knowing what your organization does well and what it does not do well is one way to determine how to best approach your revenue cycle.

Take Business Intelligence (BI), for instance. It’s not just a term for reporting. It applies to the overall approach to your revenue cycle. BI can help you evaluate areas with the greatest impact to your cash—like denials management and follow-up. As you examine these areas, BI will begin to display a picture with areas of concern.

You may come to realize that outsourcing portions of your revenue cycle might be an option. For example, converting to a new billing system is going to impact A/R and denials no matter how good your organization. You cannot install and manage the old A/R at the same time.

Leaders need to look at what makes good business sense for the organization — especially regarding denials management — and ultimately, what’s good for the patient. Can you financially support growth if your cash flow is being impacted?

Cost pressures from staffing and IT costs are all having dramatic effects on the providers, not to mention ICD-10. The implications of ICD-10 on the billing process itself are staggering with regard to workflow, systems, and reimbursement. Documenting the clinical process correctly is critical.

Physician alignment is one area that will be crucial in transforming your revenue cycle. Whether inpatient or outpatient, the revenue cycle will impact physician compensation. This means you have to include physicians in any associated initiatives. Bring them into discussions about charge capture. Educate them on the impact on denials and eligibility. Have the physician sit down next to you as you both look at options in managing the revenue cycle.

The management of the physician practice does directly impact all aspects of your revenue cycle, and ultimately your cash flow.

The old manual models are a thing of the past. Technology is woven into our daily lives and needs to be integrated into the revenue cycle. This does not mean a minimal touch approach of writing off denials in advance. It means using people and technology to limit the denials ahead of time.

Accountability will force providers and the business office to work side-by-side to maximize reimbursements, especially as reform advances. Healthcare reform / accountable care organizations — it’s all here and it’s still advancing, whether you’re good, bad, or indifferent about it.

Today’s current economic factors are in some cases crippling providers. Throw in reform and without question a transformation of the current model is needed. Changing from fee-for-service to accountability is going to impact cash flow.

I believe this transformation is forcing mergers and acquisitions across the spectrum, which will impact both your inpatient and outpatient revenue cycles.

For example, if your hospital adds new physician groups to the mix, great. That will feed the inpatient cycle. But what does that mean to your existing revenue cycle? Does that mean a best-of-breed or an integrated system approach? And how do you scale the operations to support growth? You have to look at different options.

We’ve all heard the real estate mantra, “location, location, location.” Well, with your revenue cycle it’s all about cash, cash, cash. Without it, buildings don’t get built, physicians don’t get paid, and the patient is left looking for care elsewhere.

In the end, it’s about knowing how to scale the operations to meet the needs of the organization to support financial stability and growth. It’s also about using BI to monitor performance. None of this means your cash has to be impacted. You just have to know and understand your options.

John O’Donnell is president and CEO of SPi Healthcare of Tinley Park, IL.

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