Readers Write: The ABCs of Using NLP for SDOH

The ABCs of Using NLP for SDOH
By Marty Elisco

Marty Elisco, MBA is CEO of Augintel of Northbrook, IL.

It has been established loud and clear: social determinants of health (SDOH) have a huge impact, even more than physical health, in determining the overall well-being of individuals. Yet obtaining an understanding of how specific SDOH factors affect individual patients is extremely difficult because SDOH data is not methodically collected by clinicians and social workers. This is a problem.

Unfortunately, it is just too difficult and time-consuming for clinicians to make sense of all the SDOH data because most SDOH data is buried in patient notes. This ultimately inhibits their ability to consume the data to inform decisions about individuals receiving care.

Natural language processing (NLP), a key discipline of AI that uses computers to understand the written word, tackles this problem head on. I encourage hospitals and health and human services organizations to explore NLP in their practices, particularly as technological innovation in this area is rising across healthcare.

Listed below are the simple ABCs of why health and human services organizations and hospitals should adopt NLP to make sense of SDOH. But before diving into the list, I want to emphasize: the technology is now here. NLP has matured significantly over the last five years, and it is now a proven method to extract key concepts in narrative healthcare data, such as SDOH, from text.

Cost Savings and More Efficient Care

Clinicians and case workers spend an overwhelming amount of time combing through narrative data — for example, reading typed or handwritten patient notes and case notes — to understand the status of their patient and think through potential courses of treatment. All of this time spent reviewing unstructured data is time that could be better spent in any number of ways, such as spending more time with patients.

The beauty of NLP is that it automatically highlights impactful indicators and trends across case or patient notes, thereby quickly revealing SDOH to the case workers and clinicians on the case. An NLP platform relieves health and social services workers of the time it takes to comb through the staggering amount of records by readily highlighting SDOH across a case.

Improved Outcomes

NLP empowers caseworkers and clinicians with the information they need to make impactful decisions and allow supervisors to maximize quality of care delivered. This is because NLP provides a deeper understanding of a patient or case.

The Gravity Project is a national public collaborative creating diagnostic codes for SDOH factors with the goal of having those codes incorporated into the existing list of medical diagnosis codes. The idea for the Gravity Project originated in 2017, and prior to then, hospitals and health and human services organizations had no way of incorporating SDOH into their care besides entering it in free-form into patient notes, even though it is now widely understood that a range of social, environmental, and economic factors impact health status often greater than the actual delivery of health services. NLP can extract the information in unstructured data and translate that to Gravity codes to support the diagnostic process. These diagnoses can lead to treatments and interventions that improve outcomes.

Risk Mitigation

NLP enables organizations to quickly identify patients at the highest level of risk so interventions can be provided. I firmly believe that you can only truly identify risk by understanding what is included in the narrative data. Most risk stratification systems today simply look at claims data to do this. But claims data is an incomplete picture. If care coordinators had a full picture through SDOH, then they would have a much better tool to identify those who are at most risk, and where early interventions can be referred to prevent serious health conditions from occurring.

The case for using NLP is as easy as ABC. Described above are three tangible benefits to hospitals and health and human services organizations for incorporating NLP into their practices. All players in the healthcare ecosystem share the ultimate goal of improving outcomes while saving costs, and NLP is a surefire way to do just this.

Readers Write: The Changing Dynamics of Today’s Healthcare IT Labor Market

The Changing Dynamics of Today’s Healthcare IT Labor Market
By Mike Silverstein

By Mike Silverstein is managing partner of HIT & Life Sciences for Direct Recruiters, Inc. of Solon, OH.

Whether you are a health system, a health tech company, or an employee of either, the last two years have been a roller coaster. COVID-19 has had rippling impacts across all aspects of the HIT talent market, and the potential economic correction is compounding those ripples.

From March 2020 until June 2020, if you had a job and your employer would let you work from home as COVID-19 spiked, you considered yourself lucky. As an HIT recruiter, it was a scary time that brought me back to my start in 2008, when there were more candidates than positions, and my HIT software company clients were canceling hiring plans indefinitely.

Everyone was trying to adapt to a fully remote working situation. Health systems around the country were pausing elective positions and moving all available administrative roles from on-premise to remote. What is interesting is this felt like something very temporary at the time, but it has proved to be a historic inflection point in employees’ relationships with their employers and patients’ relationships with their doctors.

Today, if you are a health tech company and you don’t offer a virtual work environment or at least a hybrid schedule, it is almost impossible to help you find top talent. If you are a health system / health plan and don’t offer telehealth visits, live chat, virtual scheduling, online payments, and on-demand answers to clinical questions (aka digital front door), your patients/members are going to find a provider / payer that does. This consumerism dynamic signaled an opportunity for an industry disruption that attracted billions of dollars of private investment in the space and led to the craziest talent land grab I have experienced in my 14 years in healthcare recruiting.

Interviews moved to video, speed became a necessity, and companies who had coffers of fresh funding changed the playing field. If you were an experienced healthcare professional, you had an unparalleled opportunity to leverage your career and have multiple employers bidding for your services. If you were the incumbent, you quickly had to adapt from your employees being grateful for keeping them employed to figuring out what you are going to do to allow them to grow professionally, both in responsibility and finances, while allowing them to be with family and have personal flexibility.

In the past handful of weeks, I have started to feel those dynamics change once again. As inflation has driven up the price of our day-to-day and the Fed has raised interest rates to combat it, employers are starting to draw some lines in the sand. Salary offers are starting to level out and working from an office is creeping its way back into job requirements. The workforce still feels like they have the upper hand, but I think we are going to see a correction like what looms in the housing market. Companies are beginning to get advised by their investors to tighten their belt, and I have heard from several industry leading startups and growth companies that fundraising has gotten more challenging with valuations coming down to earth. From a labor market perspective, that means fewer open roles in the back half of the year, and as a result, a leveling out in terms of job offers.

As a guy who makes a living by matching candidates with employers, this is a little nerve-wracking. The good news is, I think healthcare has changed for good, and because it isn’t getting any cheaper, tech companies that can help lower the cost of healthcare, provide easier access, and improve the quality will continue to have a very bright future and need talent. I also believe at my core that talent wins the day and is always a phenomenal investment. The companies that will thrive regardless of what the economy does are the ones who hire the best people and focus on giving them great support.

There are still countless problems to solve in our industry, and the net-net is healthcare continues to be a terrific place to work, earn, and invest in as we head into the second half of 2022.

Readers Write: Four Keys to Patient Engagement for Complex Care Plans

Four Keys to Patient Engagement for Complex Care Plans
By Jeff Pigatto

Jeff Pigatto is VP and global head of Salesforce practice at Infostretch of Santa Clara, CA.

For 78% of healthcare providers, the COVID-19 pandemic has made patient engagement more important than ever, according to an industry report. Industry leaders recognize the need to improve patient engagement to reduce patient leakage, especially for those with complex care plans. Without robust engagement, patients are more likely to fall through the cracks.

To boost patient engagement, leaders need a plan. I’ll offer four key elements of a patient engagement strategy.

Use a cloud-based single system of engagement

Patients with complex treatment plans often face uncoordinated care, even when they are seeing in-network providers. Some providers, with the help of expensive back-office operations, still rely on paper-based systems to record patient information. Others may use digital tools, but they often depend on local storage and lack key system integrations. In both cases, providers can’t efficiently share patient data. As a result, the patient experience suffers.

Without streamlined data-sharing tools, patients often have to complete similar intake forms at separate care centers. That’s a tedious process,  and a vulnerable one. Complex care patients often have emotionally fraught conditions. When they have to divulge sensitive information again and again, they may grow frustrated, uncomfortable, and unsatisfied. That creates a problem for providers. Dissatisfied patients may turn to other care options or may not receive the care they need, which worsens the patient leakage problem and impacts revenue generation.

With a cloud-based data-sharing system, providers can ensure that all in-network providers have the same access to patient data. This limits duplicate form completion, meaning patients have to divulge information less often. Key software integrations can further simplify patient data management. The result for in-network providers is a streamlined patient experience that’s more compelling than out-of-network options.

Offer proactive patient interaction

A provider will often issue an in-network referral and assume patients will follow through. But patients are human. Schedules quickly change, and people can be forgetful. If providers don’t engage in proactive and consistent outreach, patients will receive slower access to the care they need. That means providers lose out on revenue. With proactive patient interaction, providers can maintain patient engagement while minimizing gaps in care.

An effective patient interaction model includes:

• Scheduling appointments immediately after referral.
• Enabling form completion before care visits.
• Providing a pre-appointment patient checklist.
• Sending regular reminders about care visits and uncompleted forms.
• Emailing patients follow-up actions after each visit.

With consistent updates, patients will know that providers are serious about their care experience. What’s more, they’ll be more likely to remember the steps needed to stay in network.

Emphasize patient education

For many complex care patients, it’s expensive to manage their long-term health. Between repeated clinical visits, treatment, and therapeutics, the costs quickly add up. Over time, patients may see providers as putting profit over care. They might start looking for a more human-centered wellness experience,  and that might be out of network. But consistent patient education can help. When patients feel empowered to manage their health, they can:

• Stabilize or improve their conditions.
• Follow their care plans more effectively.
• Reduce the risk of readmissions or emergency room visits.
• Lower the overall cost of care.

Providers benefit, too. Through patient education, they can prove they’re focused on helping patients heal. That approach could be exactly what patients need to stay with their current provider.

Here’s what patient education looks like in practice. Consider a patient who’s on a weight management plan for diabetes. Every few weeks, their provider can send plain-language materials showing how exercise helps improve insulin sensitivity. If the patient has a smart watch, their provider can suggest downloading a step-tracking app that syncs with the provider’s patient data management system. Then, the provider can use that data to keep tabs on anomalies. If the patient’s steps drastically dip between months, the provider can ask about barriers to wellness management  and help strategize solutions. The result: the patient’s long-term health will likely improve, insurance claims and out of pocket expenses will reduce, and providers can maximize their value.

The patient engagement tactics we’ve looked at so far work together to prevent patient leakage, but they can be tedious to manually implement and maintain. That’s why I recommend a fourth key element of a patient engagement strategy:

Automate patient journeys

When providers use digital tools to automate every stage of the patient journey, they can save time, reduce human error, and minimize manual labor. In the long term, automation can help providers save on labor costs. Patient journey automation might look like:

• Automatically contacting patients to schedule their next care visit.
• Automatically delivering a pre-visit checklist at Day 7 before each visit.
• Sending automated patient follow-ups at Day 1, Day 7, and Day 30 after each visit.

Automating patient journeys can support existing patient engagement efforts to help providers reduce leakage.

Pandemic pressures have made patient engagement a cornerstone of efficient access to complex care. But the pandemic is also expanding the traditional range of complex care patients. In fact, new research suggests that between 20 and 25% of those who catch SARS-CoV-2 will have some form of long COVID. It can last several months and may require a complex care plan. The prevalence of long COVID amounts to what some call a “mass disabling event.” Alongside existing complex care patients, providers must invest in a long-term patient engagement strategy that accounts for an expanding chronically ill population.

Readers Write: Payers Are Approaching a Moment of Reckoning on Fraud, Waste, and Abuse

Payers Are Approaching a Moment of Reckoning on Fraud, Waste, and Abuse
By Ketan Patel, MD

Ketan Patel, MD is chief medical officer of SyTrue of Stateline, NV.

Payers are poised to face a new operating environment with significantly more scrutiny over fraud, waste, and abuse (FWA) in the wake of COVID-19.

Two years ago, the federal government created the Medicare Advantage (MA) Risk Adjustment Data Validation (RADV) program to beef up audits of MA insurers. For 2022, CMS also doubled its budget for fraud, waste, and abuse (FWA) investigations, and the Department of Justice just announced charges against 21 defendants accused of various healthcare fraud schemes involving the COVID-19 pandemic. Meanwhile, payers are working to reconcile billions of dollars in COVID-related medical expenses and correctly identify risk for the surging number of long COVID patients.

These factors have converged to generate significant potential headwinds for payers and will create the following two new realities:

• Payers will be forced to sift through increasingly huge volumes of clinical records to identify potential fraud and waste, as well as confirm bill accuracy to properly compensate providers.
• At the same time, as we head into the third year of the pandemic, payers will uncover an unprecedented amount of FWA related to COVID-19.

How successfully payers manage these challenges will be determined by their ability to replace time-consuming and expensive manual processes with artificial-intelligence-based tools that comb patient records to identify potential fraud, assess patient and population risk, and confirm payment accuracy.

In the past, payers depended on expensive and time-consuming chart reviews to find and extract key unstructured data from patient records, such as information that reveals the need (or lack thereof) for a patient to undergo various COVID-related tests. More recently, though, payers have turned to natural language processing (NLP) as an alternative to manual chart reviews. NLP is an AI-based technology that enables computers to “read” and understand text by simulating humans’ ability to interpret language, but without the limitations of human bias and fatigue.

With NLP, payers can retrospectively analyze longitudinal health data to find a particular piece of clinical information about a single patient or identify subsets within populations that require further exploration. Given today’s environment of increased FWA scrutiny, NLP is poised to play an increasingly important part in helping payers pinpoint instances of FWA.

The following are three ways payers can leverage NLP to improve FWA detection:

1. Detect patterns. In cases of FWA, there is often a pattern of repeatability in the data, such as a large number of patients meeting the same prior authorization requirements. NLP helps payers detect these patterns that lack the natural variability found in legitimate patient records.
2. Identify outliers. In the same respect, NLP can help payers spot unusual data that may be representative of fraud, such as expensive tests for which there is no medical necessity. With its ability to accurately analyze unstructured data to identify anomalies within records, NLP can quickly verify the presence, or lack of, critical data.
3. Improve scale. While even the most hard-working humans possess limitations on their ability to perform a high amount of chart reviews in a narrow timeframe, NLP automates the process, enabling substantial improvements in scalability. Because some complex medical records may consist of thousands of pages, NLP can drive significant savings in time and money in reviews.

For payers, the time to prepare for increased FWA scrutiny is now.

Readers Write: How One ACO Used Analytics to Promote Health Equity: Lessons for the ACO REACH Model

How One ACO Used Analytics to Promote Health Equity: Lessons for the ACO REACH Model
By Michael Meucci

Michael Meucci is chief operating officer of Arcadia of Boston, MA.

A greater focus on promoting health equity is at the heart of changes CMS recently made to its direct contract model, now labeled as the Accountable Care Organization Realizing Equity, Access, and Community Health (ACO REACH) Model.

While an emphasis on health equity is long overdue, this shift creates challenges for ACOs in measuring, monitoring, and improving health equity – something that can’t happen without leveraging advanced analytics to account for those factors impacting a patient’s ability to effectively manage their health, otherwise known as social determinants of health (SDoH).

A CMS Direct Contracting Entity, Massachusetts-based Community Care Cooperative (C3) incorporated advanced analytics and tight partnership with community agencies into its health equity program under the MassHealth Medicaid ACO program, driving significant improvement in the health of its diabetes patient population, in addition to a reduction in its total cost of care, positioning C3 for eventual success under the new model.

The US Centers for Medicare and Medicaid Services (CMS) launched the ACO REACH model in February, highlighting the organization’s commitment to “promoting value-based care that improves the healthcare experience of people with Medicare, Medicaid, and Marketplace coverage.” To that end, CMS requires all model participants to develop and implement “robust” health equity plans to identify underserved communities, in addition to implementing initiatives that “measurably” reduce health disparities within their patient populations.

Next, CMS placed a high priority on ensuring that medical providers play a prominent role in ACO REACH participating organizations, requiring that at least 75% control of each ACO’s governing body must be held by participating providers or their designated representatives. That number is a significant jump from the 25% requirement held by the ACO REACH model’s predecessor, which was known as the Global and Professional Direct Contracting Model.

Additionally, the new ACO REACH model is designed to deliver better protection to patients through more ACO participant vetting, monitoring, and greater transparency. CMS will look to accomplish that by asking for more information on applicants’ ownership, leadership, and governing boards to gain better visibility into ownership interests to ensure participants’ interests align with CMS’ vision.

The new model’s first performance year begins on January 1, 2023, with the model planned to run for four performance years through 2026. Applications to participate in the first year were due near the end of April 2022.

For patients, the promise of the model is better care, but with a greater focus on addressing SDoH, such as barriers to transportation, nutrition, and healthcare. For providers, the ACO REACH model offers the potential of a more predictable revenue stream and the ability to use those funds more flexibly to meet their patients’ needs.

Community Care Cooperative is an ACO that formed when the state of Massachusetts launched the MassHealth ACO program. MassHealth, which combines the state’s Medicaid and the Children’s Health Insurance programs, has emphasized engaging with community partners to help treat the whole patient, including addressing social needs that are barriers to care. C3 was created by a network of Federally Qualified Health Centers (FQHCs) to better serve their communities by providing more opportunities for individuals to receive coordinated, holistic, and culturally appropriate care in the communities where they live and work.

Incorporated in 2016, C3 serves over 170,000 MassHealth members with a total cost-of-care budget of $1 billion at 18 statewide health centers. In early 2020, inspired by the national conversation around equity, C3 launched a health equity program aimed at addressing physical and behavioral health needs, in addition to SDoH such as nutrition and housing. Earlier this year, C3 submitted its application to CMS to become a REACH ACO.

C3 started its health equity initiative in 2020 by collecting self-reported SDoH data from members, including race, ethnicity, and language information. While self-reported data may not be perfect, it is a good starting point to begin understanding the challenges facing a population of patients.

Next, C3 formed a diversity, equity, and racial justice committee to examine its patient data to investigate areas for improvement, in addition to thinking about ways to most effectively use the data in its possession. For example, the committee investigated whether the racial and ethnic makeup of patients referred to outside social services agencies was representative of the group’s overall patient population, in addition to the racial breakdown of immunization rates for two-year-olds.

To promote greater transparency, C3 has established a scorecard of key metrics pertaining to not just the usual operational numbers such as cost and utilization, but also data pertaining to health equity, such as comparisons of hypertension control by patients’ race and ethnicity. At each leadership meeting, these scorecards are posted for each of C3’s 18 health centers, prompting discussions of how to improve the metrics.

Perhaps most importantly, the attention to detail around data has led C3 to establish several experimental “flex” programs under Medicaid that are also known as “Section 1115 Demonstrations,” in which C3 partners with various social-services organizations (SSOs) that specialize in addressing SDoH, such as helping patients obtain housing or groceries.

For example, in one demonstration, C3 partnered with an SSO that delivered nutritious meals to patients’ homes. The program yielded impressive results: 68% of members with diabetes who received home-delivered meals had lower HbA1c scores in their post-enrollment tests compared with their pre-enrollment tests. Similarly, the percentage of diabetes patients with HbA1c scores that indicate their diseases are well-controlled grew significantly as a result of the home-delivery program, from 38% prior to the program to 71% after.

Additionally, the home-meal delivery program led to a substantial drop in the cost of care. In the six months after enrollment, total healthcare costs for the 456 patients enrolled in the program dropped by an average of more than 30%, from $17,902 to $12,349, compared to the six months prior.

C3’s experience with using analytics to improve health equity offers an example that ACO REACH participants can emulate. In the future, C3 looks to leverage the cost savings its programs generate to launch expanded initiatives that promote greater health equity.

Christina Severin, president and CEO of Community Care Cooperative, contributed to this article.

Readers Write: Real-World Data Connects the Patient’s Past, Present, and Future: A Systems-Level Approach to Effective, Holistic Cancer Care

Real-World Data Connects the Patient’s Past, Present, and Future: A Systems-Level Approach to Effective, Holistic Cancer Care
By Miruna Sasu, Ph.D.

Miruna Sasu, PhD, MBA is president and chief executive officer of COTA, Inc. of New York, NY.

Too often, fragmentation across the care continuum prevents the delivery of timely, tailored cancer therapies. By leveraging real-world data to inform our decision-making at the systems level, we can ensure that cancer patients have access to personalized, effective treatments.

For the typical cancer patient, the road to remission is anything but a straight line. From getting the right diagnosis to accessing the most effective therapies, patients face a fragmented and disjointed journey that can be filled with roadblocks and detours.

Part of the problem is the nature of cancer itself. It adapts and evolves to evade treatment, driving oncologists and life sciences companies to continually develop innovative therapies and update their standards of care.

But equally problematic is the way we direct patients along their journey. In too many cases, we cannot access the data-driven insights that we need to make timely decisions with our patients. We struggle to overcome systemic barriers, such as competing incentives and overly narrow methods of care delivery. And we don’t have the shared infrastructure in place to continuously learn from our patients and enhance future decision-making based on the lessons of the past.

Fortunately, we can change the status quo if we adjust our notions of what it means to work together at a systems level  and if we leverage emerging assets, such as real-world data (RWD), to create a more comprehensive, predictive, and personalized pathway to better cancer care for all patients.

Healthcare is an industry of extreme specialization, which brings both benefits and challenges to patient care.

Naturally, it’s crucial to have experts with deep experience in very specific fields to ensure that people with complex conditions get the care they need. But specialization can make it more difficult for patients to get the right care at the right time.

For example, if a patient goes to a podiatrist for pain in their foot, the podiatrist will do everything she can to examine the relevant structures.

If they finds nothing remarkable, however, they likely won’t suspect that the problem might actually be referred pain from ovarian cancer. And chances are, they won’t have access to information about the patient’s mother’s BRCA-1 mutation, which potentially raises the risk of that cancer in the person they are treating. The patient will go home with a recommendation to rest and ice their foot, not a referral to an oncologist, and it may be weeks or months before they get the correct diagnosis.

Both the patient and the podiatrist did everything “right” in this situation, yet the outcome is still suboptimal for everyone involved.

That’s because both our care practices and our patient data are viewed through an overly narrow lens, causing us to miss the big picture and make connections that may fall outside of the traditional site-specific approach to medical care.

In cases like these, what we need is a generalist: a holistic, comprehensive view of the patient, their history, their clinical and non-clinical risks, and all of the other factors that may point to the correct diagnosis or a favorable response to a certain therapy.

Data can be that generalist. By combining RWD from electronic health records; claims; medical devices; patient reports; and other sources with clinical trial information, registry data, and additional inputs, we can begin to develop the systems-level thinking we need to effectively diagnose and treat patients with cancer.

To maximize the value of our data to inform care decisions, we need to reexamine the fundamental architecture of our operating environment.

Life sciences companies, clinical providers, payers, and regulators struggle with trust issues and conflicting incentives that inhibit collaboration and prevent us from working together efficiently as a coordinated system.

If patient data is to be the generalist that unlocks silos in care, we need to stop treating it as proprietary, competitive leverage and start viewing it as a shared resource that can actively save patient lives.

In order to successfully make this shift, we must transcend our individual motivations and more effectively share precise and applicable data-driven insights across the divide so that everyone can benefit from what RWD can tell us about the right patient care.

With this approach, we can begin to take that holistic, bird’s-eye view of patient care that is crucial for identifying and treating cancer as quickly as possible. We can start to build cohorts of similar patients based on rich and comprehensive information about their treatment paths and outcomes. Then, we can predict the experiences of future patients and get them on therapy sooner, make the next correct treatment decision, or enroll them in promising clinical trials.

The result will be better experiences and outcomes for patients and more fuel for innovation for life sciences companies and providers, including a more robust and targeted pipeline for filling clinical trials.

If used correctly, RWD can be the bridge that connects the isolated corners of the care environment and leads us along a smoother, faster, more personalized pathway to high-value cancer care.

RWD will be crucial for understanding how to efficiently pivot for the patient as their story evolves. As we integrate RWD into our decision-making processes, we will need to work together to make certain that it is created, collected, and curated correctly while paying the utmost attention to patient privacy and data security.

We know this won’t be an easy task, especially if we let historical divisions influence our relationships with one another. We know that we have a great deal of work ahead of us to realign incentives, develop our real-world data assets, and set appropriate guardrails for a newly collaborative industry.

However, it can be done. If we can put aside our personal viewpoints and look at the cancer journey through the eyes of a frustrated, frightened patient, we will be able to successfully focus on our shared mission to find new treatments for cancer, improve patient experiences, and ultimately save lives.

Readers Write: Answering the Call of Nurses Month: Arming Nursing Schools to Fill the Practice Gap

Answering the Call of Nurses Month: Arming Nursing Schools to Fill the Practice Gap
By Julie Stegman

Julie Stegman is vice president of the nursing segment of health learning, research, and practice business at Wolters Kluwer.

The theme of Nurses Month this year is “Nurses Make a Difference.” But they can only continue to do so if they are supported in their roles, and that starts with education.

The ongoing nursing shortage has devastated hospitals across the nation, affecting patient care and driving high rates of burnout among those still practicing. And as the population continues to grow and age, demand for healthcare services will only increase. Reports project that 1.2 million new registered nurses (RNs) will be needed by 2030. To address today’s nursing crisis and empower nurses to continue making a difference, we need a collaborative approach that brings practice and academia together to improve new nurses’ confidence and competence, overall nurse retention, and to produce more nurses ready for the field, eager and product to care for patients.

While practice adjustments such as more flexible work schedules, cross training, and alternative care models can help address the current shortage by better supporting and thereby retaining nurses in the field, academia also has a significant role to play. Training new nurses efficiently and effectively is essential to meet the demands of practice today and for years to come. Yet a survey by the Association of Colleges of Nursing found over 80,000 qualified BSN applicants have been turned away from nursing school due to budgetary constraints and a lack of faculty, clinical sites, and classroom space.

During the pandemic, these challenges were exacerbated as hospitals and academic medical centers closed their doors for educational purposes because they did not want students using the limited personal protective equipment they had on hand, or to be exposed to COVID-19. Sites that had previously closed their doors to students are now becoming more available, but the underlying challenge of a lack of clinical sites continues to limit nursing school applicants.

While the adoption of simulation and other virtual technologies was already underway in nurse education before COVID-19 hit, the pandemic accelerated rapid adoption of virtual simulation, virtually overnight, to help fulfill the necessary clinical time requirements for graduation. This shift was a necessary one, as virtual simulation has proven its value as an essential resource for nursing schools to bridge the gap between classroom and clinical practice, including the use of high-fidelity manikin-based simulation, to ensure professional competency for nurses about to enter the field. It also provides an essential training resource for nurses to learn how to personalize and individualize care based on patient needs and clinical cues.

Simulation programs have offered a vital stand-in for real-world clinical sites that have been unable to take on nursing students during the pandemic. By mirroring real clinical practice, virtual simulation teaches nursing students to recognize and analyze cues such as pain, paleness, urticaria — effectively to take action and respond to unfolding visual and audio responses from the patient to improve clinical reasoning skills in a safe virtual environment. Simulated nursing education programs also offer end-to-end practice instruction, including reflective practice and debriefing after the simulated interaction is complete.

While this technology has been in use for nearly a decade, the last two years have accelerated adoption of virtual tools in and out of classrooms. Simulation can offer a sustained impact on nursing by addressing the shortage of clinical sites that has been a limiting factor to nursing school admission.

While our frontline nurses are continuing to provide care throughout this pandemic, healthcare systems are embracing the opportunity to innovate and modernize their practices to better support their nurses. At the same time, academia continues to innovate to ensure the ripple effects of the pandemic don’t impact the critical nursing education system. Effecting change at the education level is crucial and will positively affect the nursing profession as a whole, creating more practice-ready nurses who are equipped to manage the demands of real-world practice. Staring Nurses Month in the face, we need to enact immediate change at both the practice and academic level to create a more resilient nursing workforce and continue delivering the best care possible to patients.

Readers Write: How Automation Can Transform Healthcare Delivery

How Automation Can Transform Healthcare Delivery
By Lisa Weber, MSHA, MEA

Lisa Weber is director in industry solutions practice at UiPath of New York, NY.

A recent survey found that 90% of clinicians agreed that quality measures, including patient satisfaction, have driven change in healthcare in the last decade. The desire for better quality of care and patient experience is clear, but many healthcare organizations struggle with where to start. Consider automation.

One of the major barriers to providing the best care is the crushing amount of tedious, administrative work tasked to clinical and administrative healthcare workers. It is hard to think about a doctor’s office without hearing the constant click of a computer keyboard by every type of healthcare worker. Whether it is updating patient records, scheduling follow-up appointments, or simply taking notes, it can seem like everyone is spending more time looking at a screen than looking at the patient, which can be frustrating for both the patient and staff.

Integrating automation tools, such as software robots, can help healthcare organizations improve inefficiencies, alleviate healthcare provider workloads, and transform healthcare delivery by reclaiming time for patient engagement. The saved time ultimately leads to better, more personalized patient care. Doctors, nurses, and supporting staff would rather devote more time to patients and less to navigating and maintaining online records.

Software robots—think of them as digital assistants—can take over day-to-day tasks that involve accessing, entering, and updating systems and processes just as a human would. Much of the routine and repetitive work that medical professionals dread doing – such as data entry, revising records, checking records for compliance, and scheduling appointments – are perfectly suited tasks for digital assistants. They not only give healthcare workers ample time back in their day, but also boost productivity and workplace satisfaction, accuracy of data, and improved patient experiences.

Specific capabilities of digital assistants for the medical field include completing tasks like:

• Preparing charts ensuring that all the relevant clinical data (from multiple sources, including other physicians) is available and current.
• Making sure all paperwork is completed, signed, and up to date.
• Verifying insurance coverage and collecting any due amounts.
• Scheduling follow-up appointments, labs, and other testing.
• Initiating prior authorizations and physician referrals.

During the height of the pandemic, a hospital’s infection control department was struggling to keep up with the hundreds of people coming in every day for COVID-19 testing. As fast diagnosis and response are crucial in preventing the spread of COVID-19, nurses at the hospital needed digital assistance to not only streamline testing, but also to take the pressure off already overworked staff. Using software robots, COVID-19 test result information was processed in a fraction of the time, disseminating patient results in minutes. Overall, the hospital saved three hours a day by using automation to distribute COVID-19 test results.

Utilizing digital assistants significantly reduces the administrative workload of healthcare providers, meaning they have more time for patient engagement and other tasks that make better use of their talent and expertise. These positive effects start to snowball as less time on tedious administrative work means less burnout and turnover, and greater employee satisfaction and productivity. And all these organizational benefits gained from digital assistants in turn improve the quality of care and the patient experience.

Readers Write: Clinical Trials and the Data Diversity Problem

Clinical Trials and the Data Diversity Problem
By Liz Beatty

Liz Beatty is chief strategy officer for Inato of Guilford, CT.

Minority and marginalized communities have historically been underrepresented in dozens of private sectors globally. This includes pharma, where there exists a steep chasm that drug makers have yet to bridge concerning research and trials – a chasm that directly impacts the quality, quantity, and diversity of data that determine the efficacy of a drug and its applicability to broad patient populations.

One needs to look no further than a retrospective review of 302 drug submissions to the FDA to find evidence of data problems. That review found that nearly 16% of submissions had insufficient data to determine safe dosages, more than 11% had inconsistent results between study sites, and about 13% failed to demonstrate statistically significant benefits. These findings suggest data limitations, including diversity, influence the FDA’s rejection of five out of six submissions.

The longer the trend of incomplete data exists, the harder it becomes to address. The feelings of mistrust, resentment, and disenfranchisement only get more complex to overcome with time. While there has been a concerted push by the pharmaceutical industry to improve the situation over the past decade, it’s not moving fast enough toward a solution.

The time has come for technologies to step in and innovate solutions for this situation once and for all.

Significant progress has been made in the public sector, particularly among studies funded by the National Institutes of Health (NIH) and the National Cancer Institute (NCI). However, the same cannot be said for industry-funded studies. For example, NIH requires grant applicants to include plans for recruiting women and members of minority groups, while the FDA released guidance focused on expanding eligibility criteria for such trials and discouraging unnecessary patient exclusions, as well as boosting the recruitment process in order to attract diverse patients. NCI also reported a 14% increase in minority participation among clinical trials it has funded over the past decade.

Among private-sector trials, however, little progress has been made with regards to inclusivity and diversity. The FDA reports that 75% of enrollees in trials for drugs it had recently approved were white, while just 8% were black and 6% were Asian. An NCI-funded study found that 9% of those participating in its SWOG Cancer Research Network trials were black, compared to fewer than 3% in trials sponsored by pharmaceutical companies. 

Nor is the diversity problem limited to race. Under-representation also extends to gender and even disease. For example, just 8% of cancer patients enroll in cancer trials, and less than 2% of cardiovascular disease trials reported any female sex-specific cardiovascular risk factors.

Historically, one barrier has been a lack of medical facilities with the capacity to host clinical trials in underserved areas. One NCI study found that 75% of patients don’t participate in trials simply because there are none in their area. Additional barriers for underserved patient populations included distrust of clinical trials, insufficient information about the participation process, limited time and/or resources, and lack of awareness.

The resulting lack of diversity impacts sponsors and clinical trials in three key areas:

• Accurate, robust data. The scientific method is null and void when data sets are incomplete. Yet a multitude of clinical trials continue to enroll smaller, homogeneous groups of patients who predominantly reside within a short travel distance of major trial sites. This should ring alarm bells for any drug maker seeking the efficacy and side effects of the medications they intend to bring to market. Incomplete data is a risk to the patient and treating physician, and it’s a financial and reputational risk to the business.
• Trial efficiency. Including broader demographic and geographic groups can accelerate trial speed and boost efficacy, while testing on a narrow participant group can result in unanticipated results after approval. Additionally, pharma companies that run fully representative trials are likely to experience greater success in reaching FDA approval for their medications.
• Financial incentives. Greater patient participation enables speedier trials and reduces costs, while a wider prospective candidate pool can reduce recruiting time, which drives down opportunity costs associated with delays that can run from $600,000 to $8 million per day. Further, better detection of side effects that might otherwise be missed when enrollment is limited — resulting in an incomplete picture of the treatment’s actual impact on the broader population – can prevent post-approval FDA black box designations and millions in lost revenues.

Increasing diversity and minority recruitment requires more than simply making a greater number of trials available in underserved areas; it also involves increasing engagement by partnering with community sites, using digital tools to enhance accessibility, and employing a diverse staff to better translate trial information to broader patient populations, all of which can be accomplished by embracing community-based research centers.

Powered by advances in remote patient monitoring and telemedicine technologies, decentralized trials and distributed testing eliminate physical and geographic boundaries. When these technologies are combined with cloud-based marketplaces to connect trial sponsors with underutilized community sites and provide those smaller sites with additional enrollment support, the patient pool expands significantly. A larger patient pool allows investigators to recruit a more diverse and representative patient population, improving data quality, avoiding delays, lowering costs, and accelerating FDA submission and approval.

The benefits of trial diversity are apparent. And while steps have been taken in the right direction, there is always more to be done. The benefits of doing so, however, are clear. Prioritizing community sites and recruiting for and implementing equitable, inclusive clinical trials can have a significant impact from both a scientific and ameliorative standpoint.

Readers Write: Are HCC Codes and RAF Scores Enough?

Are HCC Codes and RAF Scores Enough?
By Sara Pastoor, MD

Sara Pastoor, MD, MHA is director of primary care advancement for Elation Health of San Francisco, CA.

The introduction of electronic health records (EHRs) has ushered in an age of data-driven capabilities that hold great potential to alter healthcare, both as an industry and at the front lines of care delivery. EHRs have apparently disrupted everything, for better or for worse. Well known are the complaints that the EHR has inserted a wall of hardware and electrons between doctor and patient, not to mention the documentation burden that has often decreased revenue by slowing the pace of care and adding hours to a physician’s work week. Yet EHRs capture, organize, and store large volumes of health information that can now be leveraged in unprecedented ways to help payers, providers, and patients all win.

One of the most transformational results of this data and information boom in healthcare is the ability to analyze the medical complexity of a patient population and use that analysis to inform resourcing and care. Sicker patients need more and different things than healthier patients do. Historically, a primary care doctor was paid relatively the same amount of money whether caring for a patient with one chronic condition or five. Today, the data encoded into EHRs can be leveraged in specific payment arrangements to justify higher reimbursement rates for sicker patient populations, with bonuses for delivering better care through reporting on defined quality metrics.

The most common example of this involves Hierarchical Condition Categories, or HCCs, which are part of a model for risk stratification originally designed by CMS in 2004 to predict future healthcare costs for patients. Each condition category, which is based on ICD-10 codes, is combined with a set of demographics (gender/age) to assign patients a Risk Adjustment Factor or RAF score. RAF scores are based on demographics and disease burden (determined by ICD-10 codes), and are used to adjust quality and cost metrics by accounting for differences in patient complexity. Using this scoring system, payers in capitated payment arrangements can provide higher payments to primary care practices with more complex patient populations. Payers can also use these scores to determine performance-based metrics and bonuses, by identifying patients with specific expected care needs based on gender, age, and chronic condition.

In value-based payment models, RAF scores work pretty well for getting paid. However, the concept of risk adjustment and stratification carries much greater potential beyond cost estimation and reimbursement structures. It carries tremendous power to improve outcomes and decrease total cost of care. While it is critical to compensate care teams for the resources required to properly manage complex patients, more enhanced risk adjustment models based on predictive analytics enable clinical interventions that change lives.

Consider my patient, who we’ll call Albert. Albert is a 72-year-old with diabetes, hypertension, obesity, obstructive sleep apnea, and chronic venous insufficiency. His wife died after a sudden and short battle with cancer. His diabetes and other conditions were previously well controlled, but he had one prior episode of venous leg ulcer complicated by cellulitis requiring a hospital admission in the past year. Following the death of his wife, Albert started to forget to take his medications, use his CPAP device for his sleep apnea, and wear his compression stockings for his venous insufficiency. His bereavement made it difficult for him to cope, and he began to neglect his care.

Additionally, his wife had been the one to check his feet for calluses, wounds, or infections since he could not reach them,  an important daily ritual for diabetics. Without his wife to cook for him, he began dining out more often, frequently defaulting to fast food. His weight increased, his chronic conditions spiraled out of control, and he developed a diabetic foot ulcer that went unnoticed until infection had invaded the bone, eventually requiring a partial foot amputation.

Albert’s diagnoses of diabetes, hypertension, obesity, sleep apnea, and chronic venous insufficiency make up a common constellation of conditions. Every family physician in America manages many patients like Albert. Yet Albert’s ICD-10 codes, age, and gender do not alert us to his quite predictable and extremely high risk of at least one bad outcome in the very near future. Albert’s RAF score is equal to that of every other patient with his same demographics and ICD-10 profile, but Albert is a ticking time bomb. With extra support and appropriate interventions, Albert’s diabetic foot ulcer, osteomyelitis, and subsequent partial foot amputation were entirely preventable, if only we had known.

HCC codes and RAF scores are a blunt instrument for managing a population. Patient complexity and the corresponding patient needs are far more nuanced than ICD-10 codes and demographics would suggest. Determining which patients need what interventions is a delicate and sophisticated science. Furthermore, the CMS HCC/RAF model does not generalize well beyond the Medicare population, and there is a need to manage clinical risk across all types of patients and ages.

To achieve the level of insight needed in a risk adjustment model for targeted population management, the model needs to factor in a number of additional determinants. My suggestions include functional status, severity of illness, the interplay between diagnoses and treatments, historical utilization patterns, pharmaceutical costs and risks, number of subspecialists involved, and social determinants of health. This more nuanced risk stratification serves to better inform the true risk of each patient, producing actionable information clinicians can use to intervene and make a difference for those who need it most.

In Albert’s case, his combined conditions of diabetes, obesity, and chronic venous insufficiency dramatically increased his risk of chronic limb ulceration and corresponding complications due to the interplay between these three conditions. According to scientific evidence, his prior history of venous leg ulcer with infection placed him at even higher risk of repeat hospitalization for a similar event. His bereavement, a pivotal life event, predictably increased his risk of worsening severity of illness for his baseline conditions. Exacerbation of his sleep apnea due to poor CPAP compliance predictably worsened his diabetes, hypertension, and obesity, even if he didn’t struggle with medication compliance and worsening of his diet. This complex interplay of factors had a dramatic effect on Albert’s health status, not reflected in a RAF score.

Sophisticated risk adjustment models are very effective at plucking patients like Albert out of the crowd and identifying him as high risk/high need. Evidence-based clinical interventions could very likely have spared Albert both his foot and significant mental anguish, also saving his health plan a chunk of change. If we apply this illustration to thousands or even millions of patients, the potential impact to the healthcare system and more importantly to society is staggering.

I envision a world in which the EHR has an integrated advanced risk adjustment model that alerts care teams to patients like Albert. Running in the background of an EHR platform, these analytic models can identify patients who are at highest risk of a health crisis and drive actionable information into the primary care workflow where care teams can not only intervene, but also capture their work for measuring, reporting, and follow up. This is a powerful intersection between technology and the physician-patient relationship for which rudimentary HCC/RAF coding falls short.

Any patient would be grateful to avoid hospitalization or a trip to the emergency room, but the benefits of such analytic tools go much further. This is the Quadruple Aim in action, with meaningful impacts to patient experience, provider experience, outcomes, and cost of care. In a payment arrangement involving shared risk, primary care is positioned to drastically reduce total costs of care with such technology while reaping significant financial benefits for doing this work. Often, the extra clicks and associated tasks related to EHR alerts for HCC reporting feel like administrivia, lacking direct clinical impact to the patient. Alerts that directly result in meaningful clinical intervention feel like time well spent. Payers win, providers win, and patients win.

Albert had interacted with the healthcare system both while his wife was dying and after his bereavement. His history of hospitalization for venous leg ulceration sat right there in his claims data. His poor CPAP compliance was transmitted wirelessly to the DME company managing the settings on his device. The information necessary to predict Albert’s escalating risk was known, but the systems and processes were not in place to identify his risk and notify someone who could do something about it. I learned about his unfortunate health debacle when he came to me with an advanced foot ulcer, well past the window of opportunity for meaningful intervention.

I have so many memories of patients over the decades for whom the trigger(s) leading to the trip down disaster lane toward catastrophic health outcomes only became obvious in retrospect, because we didn’t know what we should have known, so we couldn’t do what we should have done. We have the technology to do better. When we start putting that technology in the hands of primary care, lives will change.

Readers Write: Public Health Agencies Share the Blame for COVID-19 Misinformation

Public Health Agencies Share the Blame for COVID-19 Misinformation
By Peter Bonis, MD

Peter Bonis, MD is chief medical officer of Wolters Kluwer Health.

Surgeon General Vivek Murthy, MD, MPH asked big tech companies to provide data related to COVID-19 misinformation and issued an advisory on confronting health misinformation, reflecting the vital importance trustworthy health information plays in public health. The consequences of misinformation can be deadly to individuals and, tragically, to entire populations, as we have witnessed during the pandemic.

The surgeon general’s approach is, however, unlikely to achieve a meaningful impact on online health misinformation even if big tech companies comply with his request. The impact of misinformation is rooted in the trust that people place in it over alternatives.

The public has good reason to be mistrustful of official sources of information, making our nation’s health agencies partially culpable for the misinformation problem we face today. During the pandemic, we received conflicting guidance that changed frequently, didn’t satisfy our information needs, and was politicized. No “official” source of information has earned unalloyed trust, a role the CDC should own.

Well-intentioned spokespeople delivered inconsistent messages and disagreed in public forums, sometimes acrimoniously, leaving us with serious doubts about what to believe. At the same time, we have been terrified by the uncertainty and bewildered that the agencies charged with protecting us did not have the equipment, distribution systems, regulatory processes, and other operational mechanisms that we’d expect.

These factors contributed to making us less than confident in official sources of information and hence receptive to misinformation. Thus, the issue is bigger than addressing misinformation, it is a matter of restoring trust in our public health system and the policies and recommendations it delivers.

Fortunately, the White House just appointed Ashish Jha, MD, MPH as the new face of the federal coronavirus response. He will be instrumental in coordinating the response across federal agencies. It’s critical that Dr. Jha and Dr. Murthy collaborate, as misinformation and the coordinated federal response are intertwined. 

The Senate Health Education Labor and Pensions (HELP) committee is also addressing the topic. It is working on the PREVENT Pandemics Act, bipartisan legislation aimed at improving coordination between public health agencies. One component of the proposed legislation will require a senate-confirmed CDC director, a recognition that the public has lost faith in the CDC.

Now to a possible solution that Drs. Jha, Murthy and the HELP committee might consider. We can help tackle misinformation, fortify our public health system, restore the CDC’s reputation, and be better prepared for the next pandemic, all with the same set of actions. The best way to reduce the impact of misinformation is to create a preferred and trusted alternative.

The creation, maintenance, and dissemination of reliable health information are complex. I have spent the last 20 years helping to create and oversee UpToDate, one of the most rigorously vetted sources of medical information that millions of healthcare professionals worldwide rely on every day. From my vantage point, it’s clear where and how public health agencies are falling short and what solutions are needed. The CDC needs support to better tackle the curation and dissemination of information for healthcare professionals, policymakers, and the public. 

Curation involves identification of relevant clinical and policy questions, use of relevant data, and expert peer-review with stakeholders. Questions must be addressed directly, even when information is incomplete or evolving. It should include relevant perspectives, incorporate feedback, and be updated continuously. Controversies should be addressed, the evidence should be transparent, and recommendations that reflect the strength of convictions should be explicit.

Dissemination involves having clear communication approaches across multiple reading abilities, languages, and user types; intuitive user experiences tailored for healthcare professionals, policymakers, and the public; and a content platform that is easy for search engines to index. Major public health announcements should be published and disseminated with coordinated efforts across public health agencies, media, and social media. Officials speaking on behalf of public health agencies should confidently refer to the guidance, distinguishing extemporaneous comments and reflections from consensus opinion.

Applying these principles to develop a trustworthy clinical information service will reduce the impact of misinformation. Search engine and social media algorithms (and policies) will point to and prioritize such guidance. The public would still be free to pursue alternative points of view, but they could be compared against a trusted reference standard while fringe, conspiracy and unscientific information could be more easily de-prioritized—or dismissed.

Readers Write: The Scale of Interoperability: Healthcare Data is at Zettabyte Level and Growing

The Scale of Interoperability: Healthcare Data is at Zettabyte Level and Growing
By Jason Brantley

Jason Brantley is president and general manager, provider solutions at Datavant of San Francisco, CA.

We are swimming in an ocean of healthcare data. It is everywhere, yet it is incredibly hard to get complete health data for an individual.

Data on the health of anyone individual is being collected everywhere we turn, including when visiting our doctors all the way to the wearables we have on our wrists. All of this health data combined amounts to approximately 30% of the world’s data, and that number is steadily increasing year over year. If we were to consolidate all the healthcare data in the world, we would have an estimated 2 zettabytes, which means 2 trillion gigabytes, of data .

The amount of healthcare data generated has reached the zettabyte level and shows no signs of slowing. And that’s just the digitized healthcare data – there is still a lot on paper and on film.

With over 2 zettabytes of data, we should be able to do some really high-powered research studies to understand rare and complex diseases, personalized treatment for each person, preempt onset of debilitating diseases, among many other ways to ensure that every health decision is based on data.

The current reality is starkly different. Although there are many examples of health data being used to understand diseases, the efficacy of treatment, or how we can detect illness earlier, it is estimated that 97% of the data produced in a hospital goes unused.

How do we ensure that more of the data that is already being generated in the healthcare industry can be used to benefit patients? This is not a new problem, and neither is the answer, which is interoperability.

Interoperability in healthcare has been talked about for years, and has not been achieved yet for a number of reasons. Some of these reasons include lack of communication standards between different systems, integration costs that reduce motivation to become interoperable, and apprehension of organizations to sharing data due to security and safety concerns.

Although there are barriers, improving the ease of exchanging and using data in healthcare will mean complete access to patient information at the time of care, improved care coordination, and the ability to study complex diseases in real time. The zettabyte of healthcare data that is already being generated could actually be used to improve patient outcomes, and more importantly, save lives.

The first step to this vision of interoperability is making sure that health data can be connected and can also be exchanged easily while maintaining patient privacy and security. Data in the healthcare ecosystem will remain fragmented across many different systems until we have efficient and easy ways to exchange health data. Once we have solutions to solve the fragmentation of healthcare data, the right data will be in the right hands at the right time.

Digitizing health data exchange is essential to solving fragmentation. It means that the owners of health data, typically healthcare providers, enable digital retrieval and distribution of the data. This is not a trivial problem, but it is solvable with current technologies. The systems to enable digital exchange must offer easy and intuitive controls such that the data privacy, security, and any other protocol set by the providers are enforced for each exchange of data. A digital network with adequate control mechanisms will ease providers’ concerns on data privacy and security, while dramatically improving speed and cost of health data exchange. It is a giant step towards enabling interoperability.

Readers Write: Chief Nursing Officer Checklist for Healthcare Technology Implementations

Chief Nursing Officer Checklist for Healthcare Technology Implementations
By Robert Wittwer

Robert Wittwer is SVP of professional services at Ascom Americas of Morrisville, NC.

CNOs and CIOs know that patient-centered technology projects perform their best when clinical workflows drive the selection, integration, and adoption of solutions. However, there are several key considerations they should keep in mind before investing in their next technology-driven patient care improvement project:

1. Bring the right people to the table early. Gather the right set of stakeholders across IT, nursing, finance, etc. to define your needs and be part of the selection team for a technology vendor.
2. View technology-driven solutions as implementations that require a more complex set of adoption principles than an installation. Begin with the end in mind and not the technologies available.
3. Define the objectives and strategy the technology should achieve. A CNO can look across the overall landscape and consider bigger patient care questions. Instead of asking, “Can it be done?” ask, “Should it be done?” Avoid the temptation to use all the capabilities or features of a technology if they don’t benefit your objectives. For example, an alert may not need to be sent if it doesn’t require a nurse to respond to it. Alert fatigue is a leading reason for unanswered alerts.
4. Think long term. Whether it’s future-proofing your investment or ensuring it’s agile enough to respond to unanticipated events like COVID-19, think about your technology solution’s shelf life. Ensure you’re updating software frequently and having regular conversations about using the technology to adjust your workflows so your technology can support how you do nursing today.
5. Prepare for organizational adoption. While adopting new technologies and workflows requires nurses to change habits, by having clearly defined objectives for its impact and involving stakeholders in the process, you are better prepared to shorten the time it takes to adopt new ways of working.

Readers Write: Why Data Quality Matters in Price Transparency Workflows

Why Data Quality Matters in Price Transparency Workflows
By Cory Deagle

Cory Deagle, is chief product officer of RxRevu of Denver, CO.

As a healthcare technology vendor, we often hear that data quality is critical. It’s true that without access to reliable data, providers will question or even ignore key notifications, patient information, and clinical data. Now more than ever, vendors have a responsibility to both identify and improve the data flowing through their pipes.

Real-time prescription benefit (RTPB) – one example of an integrated tool that brings patient-specific coverage and cost data to EHR workflows – has been adopted by thousands of health systems, hospitals, and clinics across the country. This technology allows patients to understand the cost of their medications, including if lower-cost alternatives are available, while they are sitting with their care provider. I’m sure many of us have experienced the unpleasant surprise of arriving at a pharmacy only to find out the medication prescribed had an outrageous price tag. As more and more providers adopt RTPB, this should become a thing of the past.

While RTPB has incredible power to transform the patient experience, unless the vendor is providing a heavy dose of quality checks against the data, providers will notice inaccuracies or incompleteness, rendering the tool useless when making prescribing decisions. In order to resolve this, RTPB vendors must work closely with PBMs and EHRs to translate indistinguishable codes, ensure clinical relevance, and filter unnecessary noise, all with the goal of providing meaningful information so that providers can have better cost conversations with patients.

Here are a few examples of data quality steps that can be taken to improve provider trust in RTPB:

1. Quantity translations. Providers often enter medication quantities in familiar “clinical” terms (4,500 units of a diabetes injectable, for example) instead of entering quantities in “billing” units. Without a correction of the quantity from insulin units to milliliters (the billing unit for this medication), the cost information displayed could be an astounding $101,000. This is due to the fact that the PBM is pricing based on the quantity of insulin units submitted, which can be 100 to 300 times the billing unit. Vendors must be able to translate intended input quantities to ensure an appropriate covered price of $25 is displayed and communicated to the patient.
   Code Mapping: In many cases, drug costs cannot be determined because the National Drug Codes (NDC) used for pricing are obsolete or not recognized by the PBM. In order to receive an accurate price, solutions must automatically find comparable codes to display relevant pricing information.
2. Clinical logic for improved outcomes. In many cases, solutions cannot display pricing information because of user input error. For example, providers often mis-select the days supply, which can lead to errors such as “maximum dose per day.” Best-in-class vendors are able to leverage intelligence to alter days supply issues and enable transaction success. Clinical expertise and medical literature can also be used to hide erroneous results and prioritize meaningful medication alternatives in the workflow.
3. Message normalization. Providers want to focus on patient experience, but unhelpful error messages in the EHR (e.g. drug not found), slow down the ordering processes and drive mistrust in integrated tools. Standardizing errors codes from payer and PBM partners allows for actionable messaging (e.g. this medication is not covered at the selected pharmacy, please select a different pharmacy) and can improve the care experience.

It is the combination of these quality-focused tactics that can create a truly exceptional  and reliable healthcare experience. Technology vendors can no longer meet the bare minimum when delivering data. If they do, providers will ignore data presented to them, and patients will no longer trust the healthcare system they rely on. However, superior data and technology enable better decisions and drive real value in healthcare.

Readers Write: Unleash Human Capacity – And Leave Time for More Breakfast Burritos – With Better OR Scheduling

Unleash Human Capacity – And Leave Time for More Breakfast Burritos – With Better OR Scheduling
By Michael Bronson, MD

Mike Bronson, MD is an anesthesiologist with Providence Mission Hospital of Mission Viejo, CA; CEO of the Ketamine Wellness Clinic of Orange County of Laguna Beach, CA; and founder and CEO of AnesthesiaGo, which was acquired by PerfectServe in January 2022.

My path to becoming an anesthesiologist was, by all accounts, pretty normal. I went to undergrad, moved on to medical school, completed my residency, then joined a private practice. That was always the goal, and checking each of those boxes was fulfilling.

After I joined the practice, though, I started to wonder what was next. My whole life had been structured around working hard and staying focused on the future, and it felt a bit like I had reached the final phase.

Boy, was I wrong.

Like many other physicians, I was eventually thrust into a position that I never expected to inherit. There was some dissatisfaction in our group with the daily case scheduling process, and before I knew it, I was holding the hot potato in my hands.

As I leaned into this new responsibility, I most often found myself wrapping up daily cases around 5 p.m., only to be handed a stack of papers—representing the next day’s cases—and a pencil that was always topped with one of those old-fashioned detachable erasers. Why, you ask? Well, let’s just say changes, mistakes, and oversights were an inevitable part of the process.

This probably sounds relatable if you’ve ever doled out OR case assignments, but I’ll explain for everyone else. Fundamentally, it doesn’t sound too challenging—just put a name next to every case, make sure they’re not in two places at once, make sure they’re qualified to do every case, and make sure they’re credentialed at all locations.

But then come the other considerations. First, the person on call should get the most complicated cases. Second, you’ll find that surgeons often have preferences about who they want—and do not want—in the OR because of prior experiences. And finally, the patient may have a strong preference for a particular anesthesiologist because they were assigned to them previously. We always try to accommodate those requests when possible.

When you put all of this together, things can get messy. Like clockwork, every time I sent out a proposed schedule, calls and texts from colleagues would begin. Maybe somebody was double booked, or maybe there wasn’t enough travel time to get from one location to another. The reasons varied, but changes were always necessary.

In the end, this almost always took an hour or more, and I’ve seen anesthesiology groups where scheduling—which is typically done by a senior anesthesiologist—can take up to two hours. And remember, this is adding time to the end of the scheduler’s day, preventing them from going home or doing other important non-work activities.

If you’re reading this wondering whether technology can be used to improve this process, you’re asking the same question that popped into my mind several years ago. The short answer is yes, there’s a better way.

Scheduling technology isn’t necessarily new, but for a long time, the only vendors that existed were the ones that could take care of monthly scheduling needs: who’s on call, who’s working every day, who’s on vacation, and so forth. That’s a different animal than building a daily OR case schedule.

With intelligent OR case scheduling technology, you can use automation to quickly generate and distribute schedules that are free of the common mistakes people like yours truly would make when building them by hand. We’re all smart and well intentioned, but in this instance, technology is definitely the answer.

Going a step further, we can even champion provider wellness in a meaningful way. If a surgeon works best with a specific anesthesiologist, why not pair them together as much as possible to create an ideal working environment? As it turns out, technology can do that too.

I want to reinforce that none of this means we need to remove the human touch from healthcare. The best technology will make clinicians’ lives easier every day, but it should also give them a chance to provide meaningful oversight. No system is perfect, after all.

But in the end, shouldn’t we all be hyper focused on identifying and improving dated processes like the one I described? For me, getting home later every day because of scheduling duties meant less time with my family, and I had fewer chances to enjoy a favorite pastime with my son: grabbing a breakfast burrito.

Let’s all continue to think of better ways to unleash more human capacity by freeing ourselves from age-old processes that require too much time and effort. Less time being frustrated, more time caring for patients, and more time getting breakfast burritos with my son.

Readers Write: The Life and Times of Dave Garets, Healthcare IT Evangelist

The Life and Times of Dave Garets, Healthcare IT Evangelist
By Ivo Nelson

Ivo Nelson is an entrepreneur, author, and speaker of Huntsville, TX. Helping with this tribute were Mike Davis, Steve Lieber, and Phil Pead. 

Dave Garets passed Monday morning at the age of 73, having battled Parkinson’s disease for several years. Dave made a huge impact on the healthcare IT industry.

It is hard to separate the man from his mission. For in Dave, he had the goodness of the human being coupled with the mission of improving healthcare. Dave believed that if technology was uniformly adopted in healthcare, then caring for patients would be greatly enhanced and outcomes would improve and become more predictable.

Two ideas formed from his healthcare IT experience. One was that the technology had to meet certain standards, because after all, healthcare IT was affecting people’s lives. The second idea was that healthcare IT had to be universally adopted to obtain the maximum benefit to society.

As a result, Dave left his mark on an industry that has now become almost entirely dependent on technology, the capture of healthcare data that is used in research to improve care outcomes and care safety, and delivering standardized care protocols to support lower cost and higher quality of care.

The early days of Dave’s exploits are told about his nightly guitar playing at local Idaho bars. He used this musical skill at several HIMSS venues over the years to create a unique identity for his presentations. Dave’s introduction to IT was developed by AT&T, where he would tell people that he used to code in assembler. Gartner analysts gave Dave the benefit of the doubt about his programming skills. Dave was an accomplished CIO for Magic Valley Hospital in Twin Falls, Idaho for several years, where he became a thorn in the side of Meditech.

He then moved on to management consulting with Arthur D. Little before joining Gartner as a VP for healthcare research and analytics. At Gartner, Dave demonstrated his executive management skills by successfully managing a group of research analysts who were highly intelligent, accomplished, and opinionated. The research and advisory team he built at Gartner is considered one of the best in healthcare.

Gartner provided the platform for Dave to truly shine. Dave loved being at the front and center of emerging healthcare technologies and regulations. He relished being on stage, presenting well thought out and defensible positions for using healthcare IT to improve healthcare. He promoted healthcare IT across the US and at international venues. Dave’s involvement with driving healthcare research provided him the platform to establish relationships with other powerful healthcare IT executives in provider, payer, and vendor organizations. Many vendor executives considered him a nuisance, especially when then did not deliver what they promised to the market.

I recruited Dave to my Healthlink consulting company to help drive consulting services for providers. Once again, Dave recruited the best and the brightest to join Healthlink during a pivotal point in the company’s growth. Under his leadership, Healthlink built one of the best strategy practices in the healthcare IT industry.

It was during his time at Healthlink that Dave was asked to be the chairman of the largest association in the healthcare industry, HIMSS, a byproduct of a merger between CHIM (healthcare IT vendors) and HIMSS (healthcare IT provider members).

Two major components of the deal struck from the CHIM-HIMSS negotiations were to change the formal membership structure to include a corporate member category (vendor companies) and to change the volunteer leadership succession in such a fashion that guaranteed that a CHIM (vendor representative) board member would become the next HIMSS chair. While this would not be the first time an employee of a vendor served as the volunteer chair of HIMSS, it would be the first time under the new membership structure.

The planned CHIM succession, which would determine who that new HIMSS chair would be, wasn’t the best approach for the organizations’ strategic objectives for the merged association. To solve this, Dave was instead elevated within CHIM leadership rotation and thus setting the stage for him to serve as HIMSS chairperson.

Dave was instrumental during his HIMSS leadership tenure in gaining widespread acceptance of the corporate community as full-fledged members of HIMSS. As both a former CIO and vendor, Dave was able to speak to both audiences and helped reinforce the strategic concept of HIMSS as a big tent, a place that was open and welcome to all points of view to get to the right answers for the American health systems and the patients they served.

It was during his term as HIMSS chair that HIMSS acquired survey research and data assets from The Dorenfest Group and set into motion a series of events that took Dave, HIMSS, and the entire health information technology sector in new directions that shaped HIT adoption trends and federal HIT policy for more than a decade.

Upon the acquisition of Dorenfest by HIMSS, a national search was conducted for the management head of the new initiative, which became HIMSS Analytics. Dave resigned as HIMSS chair and was hired to lead this group. Dave again demonstrated his executive management skills by converting a demoralized and toxic employee base into an empowered and progressive culture that generated an accurate and highly respected provider IT market database solution.

It was at HIMSS Analytics that Dave helped develop the EMR Adoption Model (EMRAM) that was used to objectively identify acute care EMR capabilities in hospitals. This model provided a simple and accurate assessment of provider EMR capabilities for supporting healthcare delivery.

In the early days, the model was challenged, and at times, maligned. Dave’s relentless promotion of the EMRAM in the US and internationally was the key factor in its market adoption, success, and impact on the EMR market and federal health policy. Much of the early dissatisfaction was how the model showed the lack of not only coherent HIT adoption, but also how the healthcare delivery system significantly lagged other business sectors in its technology adoption. The model’s enduring success proved that the thinking behind the model was right, and it ultimately became deeply connected to the US government’s efforts to spur the adoption of electronic medical records.

After achieving success with HIMSS Analytics, Dave was recruited by The Advisory Board Company to create a research and advisory service. Dave recruited several of his old Gartner team members to help create and launch it, the first at the Advisory Board to be completely electronic in format, replacing an outdated and expensive paper publishing research service. Dave also helped promote new consulting services for the company related to Meaningful Use regulations and the emerging ICD-10 coding system.

Dave retired from the Advisory Board, almost. He continued to take on consulting projects that kept him busy working with his wife Claire with their company ChangeGang that kept him connected to the healthcare IT market.

Dave helped drive healthcare IT advancements that resulted in considerable improvements for the market. He is irreplaceable in his zest for driving healthcare IT to enable higher levels of high-quality healthcare services. He created and developed strong corporate teams, he played the politics necessary to keep his team from experiencing corporate disruptions, he was the chair of HIMSS and participated in CHIME’s CIO boot camp training curriculum for several years, and he mentored his teams that generated several successful IT professionals.

Dave lived life large. He once owned three yachts at the same time (not on purpose). He traveled globally and immersed himself in the local cultures. He always had a well-stocked wine cellar that he gladly shared with friends. He married Claire, who was his intellectual match and had the character to keep him on his toes.

Dave slid into the home plate of life with a torn uniform, dirt on his face, bleeding, and missing a few teeth on March 28, 2022. But what a ride he had.

May God bless him and welcome him into heaven.

Readers Write: Thirty Years in Healthcare IT, An Accidental Pilgrimage

Thirty Years in Healthcare IT, An Accidental Pilgrimage
By Jim Fitzgerald

Jim Fitzgerald, MBA is founder and EVP/chief strategy officer of CloudWave of Marlborough, MA.

Friday is my last day at CloudWave, my latest and likely last team in which I labor full time in the healthcare IT space.

Whether you work at a healthcare provider, an industry software vendor, or a managed cloud services company like ours, healthcare IT is by nature a team sport. It is also often as much a vocation as a career. There are darned few deep thinkers, deeply technical people, or talented managers in HCIT who could not make more money outside of it. But on the flip side, could probably not muster the directed passion for the work outside of HCIT.

That has been a recurring theme from the time I entered this business in 1993 by joining a firm weirdly and appropriately called JJWild. Everything along the way needed to be designed, built, and managed so that to the greatest extent possible it could ease and empower the safe delivery of healthcare,while being where possible, “minimally invasive.” You would have to be a heartless megalomaniac (not that we notice many on the world stage these days) not to be able to buy into that mission. After all, short of a handful of blessed protected natives sequestered deep in the Amazon who have never had to read an Explanation of Benefits, we are all healthcare consumers. Some combination of spiritual awareness, concern for our neighbors, and enlightened self-interest continues to drive the space as powerfully as financial motives. At least I hope so.

What was the road like? In 1983 (yeah, I’m that old), I was working in a non-healthcare oriented technical and marketing support role at a modem company called Microcom. Our modems were unique in that the analog / digital conversion and signaling engine was overlaid on a Z8 breadboard with a whopping 64K of RAM that booted its own device OS and loaded code from EPROM that allowed the serial interface to be programmable and also allowed the modems to run their own in-band data communications protocol to protect the data stream.

This caught the attention of a rapidly growing HCIS vendor called Meditech, whose founder, Neil Pappalardo had invented a proprietary color terminal for their Magic OS that would deeply impact the industry. The appealing interface could do block and character color graphics at about 20% of the cost of a PC and almost no maintenance. The catch was that for remote data access, it needed a connection between the terminal and the remote terminal server that had no data communication errors, as the terminal server and the terminal were in constant “chatter,” both to transmit and receive HCIS data and to manage screen formatting and behavior.

That’s how I got to know Meditech, and it changed my path. Nine years later, I joined the team at JJWild at the urging of one of Meditech’s system gurus, Chris Anschuetz, whose simple explanation was, “We are moving from Magic to TCP/IP. Our customers are going to need open networks and we need partners who can build them.”

My personal education on TCP/IP had come from a product manager at Microcom, Eugene Chang, an MIT engineer with a gift for making the complex simple. He had helped build DARPANET while at the semi-legendary consulting firm Bolt, Beranek, and Newman. I was excited. Shortly thereafter I found myself counting wires in hospital closets, ceilings, repurposed laundry chutes, and ceiling chases. Lab visits were always the frightening highlight of those network walkthroughs.

One thing led to another. JJWild helped Digital Equipment / Compaq introduce the Alpha to the Meditech community. Data General, Meditech’s larger systems partner, got sold to EMC. JJWild started offering applications, tech consulting, and managed disaster recovery services to hospitals.

Oddly, this tech support guy turned sales engineer turned sales guy (also known by “pure” engineers as the path to the dark side) was kicked into a CTO role at JJ to cap my cost to the organization. It was insane in scope, but could be a lot of fun. I got to work with a large cross section of the company – sales, consulting, engineering, support, and partner management — while still being able to work daily with our hospital customers. A group of us from inside and outside the company constantly debated and schemed to figure out how to build unbreakable systems to support healthcare apps. We got support to launch a private cloud-based disaster recovery service, JSite, at JJWild.

Perot Systems gobbled JJWild up in 2007 and put us to work before the ink was dry on harnessing emerging cloud tech to host legacy healthcare apps. A hosting solution called MSite was introduced by Perot in 2008. Dell bought Perot in 2009 with the intent of becoming more services-oriented, but the Meditech team at Perot barely showed up on their financial radar at the time.

When it became clear we were not a core strategy for Dell at the time (they sold Perot to NTT Data in 2013), 27 of us quietly left Dell from October 2011 to May of 2012 and joined with Park Place International. Its founders agreed to fund a new hybrid cloud managed services venture that would evolve into CloudWave and a suite of secure, highly available managed services called OpSus that today hosts over 125 diverse applications from EHR to enterprise imaging for more than 200 hospitals, securely backing up petabytes of data to both public and private cloud, and disaster recovery protecting over 175 hospitals.

Our services, with a cross-cloud platform sourced from our own secure private cloud data centers as well as AWS and GCP, began to transcend the Meditech realm and are gaining new customers from hospitals running Epic and Cerner, as well as smaller ISVs who need somebody to provide an ops center that can “take them to cloud.”

What do I see coming? The 20-year cycle in IT that goes from everything centralized to everything decentralized will continue and perhaps compress. The ongoing migration to cloud is driven by economic, operational, and security forces and will continue, but the cloud edge will also get built thoughtfully to support advances in genomics, analytics, and machine learning. Either PHRs will become real and the consumer will be their own best health data steward, or the vaguely and mostly unintentionally evil government / medical / pharmacy / insurance megaplex that wants no one to really have a private life will win and someone other than you will own your EHR.

Consumers will reassume financial responsibility for their own healthcare with some kind of underlying insurance for big bills or will surrender to a central system that doles out equal misery and lack of excellence for all. Black hat hackers will be heavily prosecuted instead of modestly slapped and sent to abandoned monasteries to do something useful for the rest of their days, like crush wine grapes with their feet. All but the largest integrated healthcare systems will get out of the IT business in a similar fashion to how they got out of the laundry and food service businesses and buy IT services modularly, the way individuals mix apps on their tablets. No matter where you sit in the space, it’s still going to be a wild ride.

What have I learned? Most hospital IT teams I have worked with over the years are understaffed, underpaid, and hugely dedicated to their work. They have capacity for X projects per year, demand for 3x projects, and funding for X/2 projects. They adapt like ADHD chameleons traversing a mosaic. Intended and unintended poop is flung at them by regulators, vendors, colleagues, and customers.

You are collectively some of the best people I could have hoped to serve. Thank you for the privilege.