Included amongst the segments of healthcare such as post-acute care that until recently had been mostly overlooked, specialty pharmacy now is in the spotlight as a key area of healthcare utilization and spend in the U.S. Critical, expensive and often life-sustaining medications for high complexity disease states, as well as care management programs that help patients through their healthcare journey, are at the core, driving nearly $175 billion in drug spend for the 2 percent to 3 percent of the U.S. population considered medically complex. Specialty pharmacy operations typically involve a cross-functional staff of insurance experts, patient care coordinators, nurses and pharmacists that interact with patients and stakeholders to ensure therapeutic success in a historically fragmented, manual process-driven model.
Challenges in specialty pharmacy operations
As with many aspects of the healthcare system, specialty pharmacy operations are fraught with many pragmatic, economic, and clinical care challenges.
Operational, pragmatic challenges include:
Multiple fax and phone communications between prescriber and specialty pharmacy supporting referrals, prescriptions, authorizations and patient care coordination
Challenging and fragmented patient engagement combining traditional phone-based communication with other methods such as texting with mixed results
Overlapping prescriber and patient communications among health plan, pharmaceutical manufacturer patient service hubs, prescribers and specialty pharmacy
These process challenges are creating an economic strain for the pharmaceutical industry, the payer, the provider, and most importantly the patient – where insurance benefit and funding source determinations often create confusion between overall coverage and patient out-of-pocket costs. This is compounded by complex coordination of benefits, billing and payment processing of medical and pharmacy claims, as well as other sponsored funding sources. Increasing patient cost share can make specialty drugs unaffordable for many patients which impacts medication adherence and ultimately patient outcomes.
The resulting clinical challenges make it difficult for critical patient care information to be easily shared (e.g. labs, patient assessments, medication profiles, side-effects, etc.). Additionally, treatment objectives often overlap among specialty pharmacy channel providers, resulting in crossed communications and patient confusion. In the end, key success metrics (both economic and clinical) are not easily measured, and often not operationally and clinically aligned.
The power of data accessibility and real-time analytics
Compressed specialty pharmacy margins require significant technology investment to offset operating costs and increasing service expectations. Technological advances help to address several of these challenges and as a result drive improvement in patient care and satisfaction, lower operating costs and more informed clinical decision-making.
Several of these technological advances that are showing early evidence of changing the historical paradigm include:
Interoperability with EHRs and other critical patient history data sources providing access to holistic views of patient medical records which can improve patient engagement, therapeutic interventions and reduce unnecessary procedures
Sophisticated workflow software driven by data-informed electronic protocols to support overall multi-party process efficiencies
Robust and timely analytics that provide comparative and predictive insights that influence optimal patient care at the lowest cost as well as provide more timely, accurate patient insights that drive patient success, including medication adherence
Integrated patient engagement technologies that improve patient interactions when and how the patient wishes to engage
Impact across the continuum
The application of advanced capabilities in connectivity and analytics in the specialty pharmacy space creates a more efficient system and a better result for all involved. Successful implementation of these technologies accelerates patients onto the most appropriate therapy, optimizes patient treatment plans and improves the overall patient experience which support medication adherence goals. It can also help establish innovative and more productive relationships between health plans, employers, providers, specialty pharmacies, pharmaceutical manufacturers and patients.
By Ken Perez, vice president of healthcare policy, Omnicell, Inc.
Ken Perez
Paying for high-cost drugs based on the patient outcomes they produce—an approach known as outcomes-based pricing—is gaining momentum as health plans seek to slow the growth of healthcare costs in the face of rapidly escalating drug prices.
Under outcomes-based pricing, health plans and drug manufacturers agree to a contract in which the revenue the manufacturer receives is adjusted based on how well the medication performs in a real-world population. In practical terms, in the event the patient outcomes are less favorable than expected, the manufacturer must issue a refund or rebate to the health plan, which in effect constitutes a price adjustment.
Aetna, Anthem, Cigna, Harvard Pilgrim and UnitedHealth Group have all signed outcomes-based contracts with drug makers. According to Avalere Health, a healthcare consulting and research firm, one in four health plans has at least one outcomes-based contract, and another 30 percent of health plans were negotiating one or more outcomes-based contracts as of early 2017.
Several of the early outcomes-based deals are for treating common, high-cost conditions for which there is a lot of outcomes data, such as high cholesterol and diabetes. According to the Centers for Disease Control and Prevention, over 100 million American adults have cholesterol levels above healthy levels, and similarly, more than 100 million American adults have diabetes or prediabetes.
In addition, pharmaceutical firms with new cancer drugs that have little data proving their longer-term outcomes value should be motivated to enter into outcomes-based agreements.
Given the Trump administration’s anti-regulation bent and focus on spurring drug price competition through expedited approval of generics and biosimilars, the Department of Health and Human Services is unlikely to experiment with outcomes-based pricing during the next few years. Thus, commercial health plans should remain the key promoters of outcomes-based pricing for the foreseeable future.
Guest post by Alexandra Roden, content editor, Connexica.
Alexandra Roden
Just a few years ago, big data and the Internet of Things (IoT) were terms generally unheard of. This year they continue to revolutionize technology and the ways in which we acquire and process data, but what do they mean for the healthcare industry?
Xenon Health describe IoT as “a phenomenon through which the operational aspects of the physical world become increasingly integrated with digital platforms, enabling information to move seamlessly toward the computational resources that are able to make sense of it.” Essentially, IoT goes hand-in-hand with the mobile age and the diversity of data that is currently being retrieved from agile and mobile locations.
Big data is a related concept – it addresses the ever-increasing amounts of data that are created every second of every day and recognizes that these figures will only continue to grow. For example, in the “social media minute” every single minute there are 277,000 tweets are sent, Whatsapp users share 347,222 photos and Google receives more than 4,000,000 search queries. These figures are remarkable even for those of us caught up in the social media hype, and most shocking of all is the realization that the global Internet population now represents 2.4 billion people. That’s a lot of people creating a lot of data – the question now is how we can utilize this data in a meaningful way.
IoT has revolutionized many industries and will continue to do so in the foreseeable future, but what about healthcare? Organisations within this industry tend to adopt new technologies slowly, relying upon solid evidence and demonstrable impact and efficiency before committing to any such change. The shift towards IoT is, however, beginning to take place, and increasing amounts of available patient data are beginning to inform decision making processes within this sector.
Guest post by Jeff Kaplan, chief strategy officer, ZirMed.
Jeff Kaplan
The 40,000 healthcare and healthcare IT professionals who gathered at the Sands Expo in Vegas brought a different vibe for HIMSS 2016. The halls buzzed with activity and an overall optimism that belied any of the potential causes for uncertainty—politics, a down stock market, increases in uncompensated care, the movement toward fee-for-value, or the staggering shift in patient responsibility.
For those who attended HIMSS 2015 in Chicago, the difference was visible in vendor messaging and audible in conversations during the conference. Among all attendees the optimism seemed well founded, grounded in reality. We all see significant opportunity to drive improvement in healthcare for our generation and generations to come. That’s why we came to HIMSS – we’ve placed our bets.
In that spirit, let’s talk about where healthcare is doubling down, where it’s hit a perfect blackjack, and which trends pushed as providers look for the next deal.
Double Down – Data Interoperability
Out of the gate at HIMSS 2016, there was increased focus and emphasis on the importance of data interoperability and integration. From booth signage to the increase in dedicated vendors to industry veterans evangelizing on the topic, you couldn’t miss the tells from all players—everyone wants to show they have a strong hand when it comes to interoperability. Epic’s Judy Faulkner made a play that Epic wasn’t just the leader of the interoperability movement – they were in fact the originator (see her interview with Healthcare IT Newshere).
Of course, wander off into other parts of the exhibition hall and it wasn’t long before you heard the all-too-familiar complaints about closed-system platforms – that they limit innovation by outside companies and technologists who can build applications to add additional value. In the era of Salesforce.com and other open platform successes, many HIMSS attendees spoke of their hope that companies like Cerner and Epic will follow suit.
Blackjack – Data Analytics
Over the last year vendors heard providers loud and clear – healthcare providers need hard ROI on any new initiatives, especially as many have EHR/HCIS sunk costs in the tens of millions of dollars. They need a sure thing—and the changes evident at HIMSS 2016 reflected that shift. Buzzwords like “Big Data” thankfully went to the wayside and were replaced with meaningful discussion around data analytics and data warehousing. Providers know they’ve amassed a wealth of clinical and financial data—now they’re looking for ways to increase the quality of patient care while driving down costs.
Guest post by Lauran Hazan, director of healthcare analytics, STANLEY Healthcare.
Across nearly every industry, Lean process improvement and analytics have radically changed the way that businesses operate. Now, with the advent of big data and accompanying business insights, we’ve moved beyond troubleshooting problems to data-driven design and predictive analytics. The impact of these processes and technologies is felt at every level of the manufacturing supply chain. What happens when all of these innovations hit healthcare?
We’re already seeing many of them in action in hospitals across the world, which are now able to analyze the movement of patients, clinicians and equipment, thanks to RTLS and RFID – among the first Internet of Things (IoT) technologies. The central value proposition of IoT analytics and data visualizations in healthcare is that by providing clinicians and other users with actionable insight into their everyday processes, they will be empowered to understand and modify their behavior, and improve efficiency and the patient experience.
We know this technology works – revealing inefficient workflows, missing or insufficient levels of equipment, patients who have been waiting too long, and more. But acting on these insights to generate change requires more than technology. It needs visionary leadership to create cultural change, grounded in objective data and the real-time feedback it provides.
It’s no easy feat, and we’ve seen industrial engineers working to create change in healthcare for years. What’s different now is the data, which moves us beyond gut instinct or individual experience. Analytics in healthcare – based on objective and comprehensive IoT data – supports a constructive conversation about change, and can be used by staff at all levels to study the impact of an experimental process improvement. Hospitals can enable highly skilled workers to lead from within, rather than managing them top-down. They can leverage the experience and scientific mindset of clinical staff to identify new areas for growth, experiment to improve, measure success and continue to innovate with each new win.
That last point is perhaps the most important. For us to truly change healthcare, hospitals must develop a continuous cycle of improvement. This is what it means to be a Lean hospital in today’s data-empowered industry. Once the organization changes a practice or habit, it can study the impact of that change and then uncover other opportunities to improve further. The next set of practice changes may involve different measurements and metrics as the process of discovery continues.
Over the past few years, healthcare technology has seen many advances. We’ve achieved mass-market adoption of EHRs, many organizations are making meaningful progress on data aggregation and warehousing information from multiple diverse systems, and wearables and other sensors show much potential to unlock personal information about each patient. The pace of change in healthcare is quickening, with each new technology or initiative sending off a chain of reactions across the entire ecosystem, ultimately improving patient care.
I see three trends driving the industry toward change:
Analytics will help predict population heath management
One of the persistent industry challenges is the “datafication” of healthcare. We’re amassing more and more data now than ever before. And new sources (like wearable devices) and new health factors (like DNA) will contribute even more. This data explosion is putting increased pressure on healthcare organizations to effectively make this data useful by delivering efficiency gains, improve quality of care and reduce overall healthcare costs.
Navigating this digitized healthcare environment will require increasingly sophisticated tools to help handle the influx of data and make the overload of healthcare information useful. In 2016, the industry will begin to take concrete steps to transition to a world where every clinician will see a snapshot of each of their patients to help them synthesize the critical clinical information they need to make a care decision. Moreover, hyper-complex algorithms will allow providers not only to know their patients, but to accurately predict their healthcare trajectories. By giving providers insights into how each patient is trending, clinicians will be able to make better-informed, precise decisions in real-time.
Consolidation leads to new healthcare models, improved outcomes
New models for effective population health management continue to drive change across healthcare systems. These models incentivize stakeholders to optimize costs, identify organizational efficiencies and improve decision-making processes to deliver better care at a lower cost through an emphasis on care coordination and collaboration.
Guest post by Scott Jordan, co-founder and chief innovation officer, Central Logic
Scott Jordan
Gone are the days when IT department gurus ran lengthy reports, sifting through numbers and analyzing data until the wee hours of the morning, all in the quest of fancy profit center reports to impress the C-Suite. Especially in hospital settings where lives are on the line, data in 2016 must be delivered in real time, and even more importantly, must be relevant, connected, and able to be understood, interpreted and acted upon immediately by a myriad of users.
Data That’s Right
Today, having the right data intelligence that is actionable is paramount. It’s no longer enough for analytics to only interpret information from the past to make the right predictions and decisions. With the changing healthcare landscape, it’s increasingly important that data intelligence must also be relevant and the tools agile enough to provide an accurate assessment of current events and reliably point to process and behavior changes for improved outcomes … in real time.
All tall order for any IT solution, much less one in healthcare where robust security parameters, patient satisfaction concerns and HIPAA regulations are just the tip of the iceberg a health system must consider.
Data That’s Connected
The good news is data technology tools now exist that offer interoperability features – from inside and outside a hospital’s four walls – this allows providers to exchange and process electronic health information easily, quickly, intuitively and accurately, with reliably replicable solutions.
When users can see the full complement of a patient’s health record, they can more accurately improve care coordination and save lives. Specifically, connected patient records can:
avoid duplication of diagnostic procedures,
properly evaluate test results and treatment outcomes, regardless of where care was delivered,
share basic patient data during referrals and get information after specialist visits,
view medications, regardless of where prescribed, avoiding drug interactions, medication abuse, etc., and
view allergy and pre-existing condition information, especially valuable to Emergency Department transfers.
Guest post by LeRoy E. Jones, chief executive officer, GSI Health, LLC.
The health IT revolution is here and 2016 will be the year that actionable data brings it full circle.
Opportunities to achieve meaningful use with electronic health records (EHRs) are available and many healthcare organizations have already realized elevated care coordination with healthcare IT. However, improved care coordination is only a small piece of HIT’s full potential to produce a higher level synthesis of information that delivers actionable data to clinicians. As the healthcare industry transitions to a value-based model in which organizations are compensated not for services performed but for keeping patients and populations well, achieving a higher level of operational efficiency is what patient care requires and what executives expect to receive from their EHR investment. This approach emphasizes outcomes and value rather than procedures and fees, incentivizing providers to improve efficiency by better managing their populations. Garnering actionable insights for frontline clinicians through an evolved EHR framework is the unified responsibility of EHR providers, IT professionals and care coordination managers – and a task that will monopolize HIT in 2016.
The data void in historical EHR concepts
Traditionally, care has been based on the “inside the four walls” EHR, which means insights are derived from limited data, and next steps are determined by what the patient’s problem is today or what they choose to communicate to their caregiver. If outside information is available from clinical and claims data, it is sparse and often inaccessible to the caregiver. This presents an unavoidable need to make clinical information actionable by readily transforming operational and care data that’s housed in care management tools into usable insights for care delivery and improvement. Likewise, when care management tools are armed with indicators of care gaps, they can do a better job at highlighting those patients during the care process, and feeding care activities to analytics appropriately tagged with metadata or other enhanced information to enrich further analysis.
Filling the gaps to achieve actionable data
To deliver actionable data in a clinical context, HIT platform advancements must integrate and analyze data from across the community—including medical, behavioral, and social information—to provide the big picture of patient and population health. Further, the operational information about moving a patient through the care process (e.g., outreach, education, arranging a ride, etc.) is vital to tuning care delivery as a holistic system rather than just optimizing the points of care alone. This innovative approach consolidates diverse and fragmented data in a single comprehensive care plan, with meaningful insights that empowers the full spectrum of care, from clinical providers (e.g., physicians, nurses, behavioral health professionals, staff) to non-clinical providers (e.g., care managers, case managers, social workers), to patients and their caregivers. Armed with granular patient and population insights that span the continuum, care teams are able to proactively address gaps in patient care, allocate scarce resources, and strategically identify at-risk patients in time for cost-effective interventions. This transition also requires altering the way underlying data concepts are represented by elevating EHR infrastructures and technical standards to accommodate a high-level synthesis of information.
The health IT revolution is here and 2016 will be the year that actionable data brings it full circle.