The Eyes Have It

No matter what technologies are used, the end goals for any organization are adoption and behavior change to achieve desired clinical and financial outcomes. Well-designed workflows that satisfy the needs of users lead to high levels of adoption of information systems. High levels of adoption that do not facilitate behavior change replicate systems and the undesirable outcomes achieved before technology deployment. Therefore, techniques that encourage behavior change, such as clinical decision support tools and performance scorecards, must also be embedded seamlessly in the clinical workflow to be effective.

Tools exist for achieving effective clinical workflow, although we continue to learn and refine best practices. Throughout the 1990s, payers employed clinician profiling reports or scorecards in an attempt to identify outlier providers and effect change in clinician practice patterns. Few of these efforts proved successful due to problems with data sources and the delivery and content of the scorecards.

With the expansion in the use of electronic medical records, the data source for scorecards that can influence clinician behavior moves to a much more robust data source, a data warehouse populated with clinical information gathered from multiple clinical systems. In addition, analytical tools now exist that can easily comb through enormous data sets and generate insightful results presented in attention grabbing, meaningful graphics.

Excerpts from: The Eyes Have It. PSQH, March/April 2013

Photo Courtesy of  Don Guerwitz Photography - At the Catania Market, Sicily, Italy

Next Generation CDSS: Patient-Centered Workflow

In the design of successful health IT implementations, patients matter. Although the importance of addressing the workflow needs of clinicians cannot be overstated, focusing on patient needs helps ensure newly designed workflows leverage the full capabilities of IT tools and embedded clinical decision support systems (CDSS).

In addition, this delivers the clinical and financial outcomes desired by organizations. Entities that ignore the needs of clinicians in designing health IT-driven workflows can expect to experience either low levels of health IT adoption among clinicians, suboptimal patient care results, or both.

Excerpts from: Next Generation CDSS: Patient-Centered Workflow. Health Technology Online, January/February 2013, p.10.

Photo Courtesy of  Don Guerwitz Photography – Guardians of the Gate. Angkor Thom, Siem Riep, Cambodia

We Know What to Do

Almost eight years ago Richard Hillestad and his colleagues from the Rand Corporation predicted that electronic medical record systems (EMRs) would generate cumulative efficiency and safety savings of $142 to $371 billion during a 15-year period, an average of $81 billion annually (Hillestad et al., 2005).

Just seven years later, Arthur Kellermann and Spencer Jones from Rand revisited Hillestad’s work and concluded that the increase use of health IT made the quality and efficiency of healthcare only marginally better. At the same time, yet, aggregate expenditures on health care in the United States have grown from approximately $2 trillion in 2005 to roughly $2.8 trillion today.

This Kellerman report evaluates four assumptions made in the original article by Hillestad and attributes the shortfall in observed versus projected results to shortcomings in four areas. Hillestad assumed the following in making his projections: 1) robust interoperability and interconnections of health IT systems, 2) wide adoption of health IT systems by clinicians, 3) effective use of health IT systems to impact care, and 4) changes in incentives and reimbursement systems that emphasized quality rather than revenue.

No surprises here. Anyone who toils in the health IT field knows that these four items represent the key challenges that we all work to overcome as we deploy our health IT applications. Counter to the pessimistic view that the billions of dollars spent on EMRs and other health IT systems are wasted resources, these investments offer a powerful force that, when the conditions are right, will significantly impact quality, safety, and cost.

Excerpts from: We Know What to Do. PSQH, January/February 2013

Photo Courtesy of  Don Guerwitz Photography – Temple Ruins. Sanka, Nam Belu River, Myanmar (Burma)

‘Show Me the Money’ Revisited

In the immortal words of Ronald Reagan, “There you go again.” Something that can be explained simply is twisted to look infinitely complicated with plots and subplots that would make J.K Rowling proud. The recent controversy over the use of EMRs to increase reimbursements to providers suggests intrigue, fraud, and bad intent. In contrast, if you learn how the money flows, you will better understand the true reasons for the outcomes seen in organizations using EMRs.

A New York Times report published in September 2012 documented an increase of $1 billion in Medicare reimbursements in 2010 over the amount paid five years earlier. The report partly attributed this payment increase to changes in billing codes assigned to patients in emergency rooms.

Such stories send shivers through the EMR community. To date, hospitals, EMR vendors, and the government struggle to demonstrate the value of EMRs in enhancing patient care and delivering cost savings. With billions of federal dollars earmarked to payment incentives for the use of EMRs, government officials anticipate some kind of return on this investment. Government EMR advocates did not expect to see an increase in reimbursements by public and private payors to providers through “enhanced” billing practices (code optimization [legal] or code maximization/up-coding [illegal]) activities.

Compared to paper records, EMRs allow for more rapid and complete documentation. In addition, EMRs slow the documentation process. Wait, how can EMRs both speed up and slow down documentation? It all depends upon deployment of the EMR and the constructed documentation workflows.

Unfortunately, EMRs focus on two important objectives at the same time– 1) facilitate clinical documentation to deliver patient care, and 2) facilitate clinical documentation to optimize coding for reimbursement. Documenting for patient care does not closely parallel documenting for reimbursement. As long as reimbursement is tied to documentation, EMR documentation workflow will suffer from inefficient documentation workflows, inaccurate documentation of care from global templates, and accidental (or deliberate) upcoding for reimbursement.

Excerpts from: ‘Show Me the Money’ Revisited. PSQH, November/December, 2012

Photo Courtesy of  Don Guerwitz Photography – Daybreak at the Ganges, Varanasi, India

Super-Organism Focused Healthcare

About 10 trillion cells make up the human body. The joining of eggs and sperm at the time of fertilization brings together 23,000 genes. In a healthy gut alone, more than 100 trillion bacteria thrive.

Scientists estimate that the microbiome—the term used to describe all the bacteria and other organisms that live on and within us—collectively represent more than three million genes. These bacteria live symbiotically with us, providing a variety of beneficial protections while we offer them raw materials and a nice, warm shelter in which to grow and reproduce. For most of medical history, physicians studied disease states without consideration of the microbiome. Only recently have researchers viewed the human body as a super-organism, influenced as much by the genetics of the host as by the permanent bacterial residents.

The microbiome offers humans an expanded ability to thrive within their environment by offering additional functionality—such as breaking down complex carbohydrates—that would be difficult to code within our limited number of genes. Through evolution bacteria chose us, and we chose bacteria, establishing a powerful, mutually beneficial relationship.

Even today, the new medical knowledge produced in even narrow disciplines exceeds the capacity of any physician to assimilate and apply effectively. Adding the complexity of the microbiome only makes the task of assimilating the relevant medical knowledge further out of reach.

Health information technology offers clinicians tools to manage this avalanche of information. As new knowledge is obtained, this information can be codified in guidelines, order sets, and searchable knowledge bases, relieving physicians of the impossible burden of assimilating all this new information.

Excerpts from: Super-Organism Focused Healthcare. PSQH, September/October, 2012

Photo Courtesy of  Don Guerwitz Photography – Mandalay Monk, Myanmar (Burma)

Patient-Centered Workflow

In the design of successful healthcare information technology implementations, patients matter. Although the importance of addressing the workflow needs of clinicians cannot be overstated, focusing on patient needs helps ensure newly designed workflows leverage the full capabilities of information technology tools. In addition, this delivers the clinical and financial outcomes desired by organizations. Entities that ignore the needs of clinicians in designing HIT driven workflows can expect to experience either low levels of HIT adoption among clinicians, suboptimal patient care results, or both.

The Institute of Healthcare Improvement —led by founder and former administrator of the Centers for Medicare and Medicaid Services, Don Berwick, MD—displays this mantra throughout its facility:

“Every system is perfectly designed to achieve exactly the results it gets.”

Therefore, organizations that utilize new information technologies to mimic the existing workflow of clinicians deliver results no better than outcomes previously reached. In some cases, the inherent complexity of the information technology when deployed within a paper-based workflow can deliver results worse than originally obtained.

To effectively implement HIT, organizations must understand in-depth the capabilities of the available information technology, the requirements of the practicing clinicians, and the expected outcomes of all impacted stakeholders (i.e., patient, clinician, organization). Readily available healthcare information technologies offer invaluable tools such as single sign-on (SSO), roaming desktops, location awareness, and fast-user switching to support impactful patient-centered workflows.

Patient-centered workflow requires stringing together individual steps, the linking of processes, and the bridging of activities by multiple caregivers to create an effective and efficient orchestration of resources to enhance the health of the patient.

Excerpts from: Patient-Centered Workflow. PSQH, July/August, 2012

Photo Courtesy of  Don Guerwitz Photography – Monastery Buddahs, Bangkok, Thailand

Print Me a Pill

During the first Star Trek series released in the mid 1960s, the creators introduced viewers to several magical devices – the Communicator, the Padd, the Replicator, and the Transporter. Although building the latter device requires the repeal of several of the laws of physics, the other three commonly exist today for the public to use. Smart phones are the Communicator of today allowing us to speak, text, or email to anyone around the world who might have a similar device. The Padd is my Nook – it even looks like it and makes the same sound when dropped on a desk – or your iPad or similar tablet device. Finally, the Replicator is nothing other than a three dimensional (3D) printer, a device just entering the world of consumer products.

All 3D printing works from a digital file or blueprint that directs the building of the object. Some printers use tiny nozzles that deposit layers as thin as 0.1 mm in thickness from material contained in their “printer cartridges.” Other 3D printers use laser beams or tiny droplets of glue to fuse thin layers of plastic or material dust into solid objects.

Bioprinting represents the next step in 3D printing. Instead of materials, living cells fill the cartridges of the 3D printer. Cell by cell and layer by layer, these bioprinters deposit specific living cells chosen to perform a particular function in a pattern that allows them to perform a designed task.

3D printing offers the opportunity to print pharmaceuticals specifically produced to meet the needs of individual patients. A 3D printer with cartridges loaded with the pharmaceuticals required by the patient could print a pill that contains exactly the right amount of each of a patient’s medications, thereby customizing the drug treatment for the patient.

Excerpts from: Print Me a Pill. PSQH, May/June, 2012

Photo Courtesy of  Don Guerwitz Photography - Conch Fisherman, Grenada

Big Data Drives Big Change

The digital age is the age of big data where every piece of technology captures data available for later use. The McKinsey Global Institute (MGI) describes data generated in this way as digital “exhaust data,” data that are created as a by-product of other activities.

The rapid expansion in the use of EMRs and digitally-driven technology—MRI scanners, body sensors, automated lab tests—brings the era of big data to healthcare. MGI estimates that big data presents a $300 billion potential annual value to the U.S. healthcare system. The five broad areas to deliver that value are: 1) clinical operations, 2) payment/pricing, 3) R&D, 4) new business models, and 5) public health. Sub-areas include comparative effectiveness research (CER), clinical decision support, remote patient monitoring, health economics, and personalized medicine.

The four large data sources for healthcare include clinical, pharmaceutical, administrative, and consumer.

New analytic tools such as Semantic Web 3.0—linked data—offer ways for machines to analyze these data sets leveraging approaches impossible using standard relational databases and statistical methodologies. These new tools permit researchers to work around the barriers presented by data sets’ non-conformance to standards for data collection or storage.

Similar to the use of metadata, Semantic Web techniques allow the assignment of descriptors to each data point, providing a context and meaning to the data. This allows machines, applying powerful statistical techniques, to analyze the disparate data sets in ways not available to humans alone due to the data sets’ size and complexity.

Organizations that properly collect, analyze, and utilize big data will achieve a significant competitive advantage over those organizations that fail to recognize the opportunity big data presents.

Excerpts from: Big Data Drives Big Change. PSQH, January/February 2012

Photo Courtesy of  Don Guerwitz Photography – Sahara Dunes. Niger River, Mali

It’s All About Jobs

What would Steve do? Steve Jobs, the 20th century’s greatest and most successful innovator, engrained that mantra into the heads of every Apple employee. Only those staff members who thought through problems the way Jobs did would offer solutions that were acceptable to their boss. Jobs relied upon his own research and intuition, not focus groups, to guide him. When asked about the research that went into the design of the iPad, Jobs replied “None, it’s not the consumer’s job to know what they want.”

Although physicians employ the iPad in many clinical settings, the tablet computer functions as a front end to existing EMR and other clinical applications. The iPad is not an innovation in and of itself but a tool to innovation, and few healthcare information technology (HIT) vendors actively leverage the “innovation” inherent in the iPad in their clinical applications.

What Would Steve Do?

Therefore, we must ask ourselves, what would Steve do in healthcare? First, Jobs would not be constrained by current practice. Like hockey, solutions come from skating to where the puck will be rather than where it is. In addition, he would consider all problems together in an effort to create an “ecosystem” that binds one product with another, the same way Apple now threads together the iPhone, iPad, and iMac with iTunes and iCloud. Jobs cherished style and ease of use, combining them with function and utility. Any Jobs-inspired healthcare application must be intuitive to use, support efficient workflow, and facilitate the delivery of safe, high-quality care to the patient.

Excerpts from: It’s All About Jobs. PSQH, November/December 2011

Photo Courtesy of  Don Guerwitz Photography – Fishing in the Moat, Hue, Vietnam

Web 3.0 Data-Mining for Comparative Effectiveness and CDS

“Turbulent times” accurately describes the state of the American healthcare system. The list of critical challenges is well known—upward spiraling healthcare costs now approaching 17% of GDP, healthcare payment reform, shortage of clinical professionals, aging population, and the economic downturn. While current investments in health information technology (HIT) begin to deliver increased reimbursements to providers, these same at-risk organizations, along with payors, seek better ways to leverage HIT to enhance quality care and reduce costs.

Although much effort focuses on improvement of clinical workflows, an opportunity exists to transform healthcare delivery by implementing evidence-based clinical decision support at the point of care. Such clinical content delivered effectively within new, efficient clinical workflows directs patients toward evidence-based therapeutic plans that produce desired clinical and financial outcomes. While informaticists work on developing these clinical workflows, the lack of clinical knowledge limits the ability of organizations to leverage HIT in order to personalize therapeutic care plans.

Identifying Affordable Therapies

Comparative effectiveness research, supported by data mining, allows organizations to identify affordable therapies that enhance patient care. With the implementation of HIT, data warehouses contain petabytes of searchable clinical, outcomes, genomic, and financial data across multiple patient populations. Bringing together this data using sophisticated knowledge analytic tools and domain-specific interfaces allows researchers to discover relationships among multiple variables gleaned from previously unconnected databases.

In turn, this new clinical knowledge enables clinicians to personalize treatment for patients based upon their genetic background by linking it to descriptive patient data and outcomes. Personalized medicine transcends analysis of a population-based cohort by placing the patient within a sub-population that better reflects the expected outcome from a prescribed treatment. Embedding this personalized medicine knowledge within an EMR’s clinical decision support module facilitates the delivery of these evidence-based best practices at the point of care.

Semantic Web

Sophisticated software indexes the databases on metadata that “describe” each data point. Although the indexing allows for rapid retrieval of the data, it more importantly builds links among each data point based upon the descriptive information contained in the metadata. Discovery of these relationships is impossible without semantic web technology and the ability of computers to utilize it to read and understand metadata. Experts can utilize semantic web technology to query multiple large data sets to explore comparative effectiveness hypotheses.

Excerpts from: Web 3.0 Data-Mining for Comparative Effectiveness and CDS. PSQH, September/October 2011

Photo Courtesy of  Don Guerwitz Photography – School Outing. Hanoi, Vietnam