Readers Write: Rural Hospitals Need Federal Assistance to Strengthen IT Security Posture

Rural Hospitals Need Federal Assistance to Strengthen IT Security Posture
By Kate Pierce

Kate Pierce, MSMIITA is senior virtual information security officer and executive director of the Subsidy program of Fortified Health Security of Franklin, TN.

The majority of my career in healthcare IT has been dedicated to working for a small and rural hospital, leveraging technology advancements to improve patient care and to keep those systems safe from cyberattacks. I spent 21 years with North Country Hospital in Vermont, starting as a systems analyst and working my way up to chief information officer and chief information security officer.

Growing up in a rural community in northeast Vermont, I have a deep understanding of the challenges faced by smaller hospitals, especially those in rural settings. I also understand how vital these organizations are to the communities they serve.

When I was asked to testify before the US Senate’s Homeland Security and Governmental Affairs Committee on the challenges that small and rural hospitals face in managing an effective cybersecurity program, as well as barriers to adequate funding and human capital constraints, it was an honor to do so, as this is a topic near and dear to my heart.

Even without the cybersecurity challenges, rural hospitals are experiencing unprecedented staffing and budget constraints. More than 40% operate in the red, and nearly one in three is at risk of closure. When it’s a daily challenge to deal with basic healthcare delivery while managing higher labor costs and shrinking margins, cybersecurity isn’t a top priority for most hospital executives.

Anyone who has ever worked in a hospital knows that change is constant. However, a cyberattack is among the most disruptive and devastating events that can occur within a healthcare environment. A 2021 study by the federal Cybersecurity and Infrastructure Security Agency (CISA) found that hospitals hit by ransomware often experience additional stressors that can be correlated with higher patient mortality rates.

This can happen at any facility, but criminals shifted their focus to attacking small and rural hospitals in 2022. Even though a successful attack against a smaller facility may yield less patient data or a lower ransom to release data, the reality is that they are often easier to breach and invariably connected to larger facilities.

When an urban or suburban hospital is hit with a cyberattack, it may inconvenience patients, but they often have other care options nearby. That’s not the case for rural hospitals. The nearest facility may be 40+ miles away, which doesn’t make it feasible to simply divert patients. Even if patients are diverted, nearby facilities can become overwhelmed, creating a cascading crisis throughout the community.

The stakes couldn’t be higher, as evidenced by a 2019 attack on an Alabama hospital that knocked out the hospital’s IT systems for three weeks and is believed to have resulted in the nation’s first fatality attributed to ransomware. According to the lawsuit, patient monitors were offline while the plaintiff was in labor, leading to insufficient monitoring of a fetus that was born unresponsive with the umbilical cord wrapped around the baby’s neck. Although the child was resuscitated, brain damage occurred, and the infant died nine months later.  In a recent 2022 attack, a rural Washington State hospital was so overwhelmed that an ER nurse called 911 for help.

The urgency of improving the security posture of these small and rural facilities continues to escalate every year.

As the sophistication of cyberattacks continues to grow, the federal government should be stepping in to help secure these hospitals and keep patient data safe. As I testified to the Senate committee, implementing these four measures could improve the state of cybersecurity for our small and rural hospitals.

First, we must move beyond guidance and recommendations and create minimum standards for cybersecurity that all healthcare organizations must follow. These standards must be reasonable, effective, achievable, and continually evolving as cybersecurity requirements change over time.

Based on the items outlined in the Health Industry Cybersecurity Practices (HICP) document, recommendations can be grouped into five basic categories:

• Email security and protection
• Access management
• Asset management
• Network management
• Incident response

Simply put, regulators must spend less time suggesting and more time providing concrete solutions.

Second, we cannot leave our small and rural hospitals behind. We must create funding opportunities to allow all hospitals to meet the standards. Options include:

• Subsidies, which have found success among rural hospitals in other initiatives
• Grants, which may prove more difficult as smaller hospitals often don’t have grant-writing resources
• Incentives for small and rural hospitals to enhance security, a “Meaningful Security” type program modeled on Meaningful Use
• Enhancements in Medicare and Medicaid payments for eligible facilities, with hospitals showing how additional funds were used to boost cybersecurity

Third, we need better coordination of government cyber efforts for healthcare. While the guidance and services from government are appreciated, there is often a knowledge gap regarding the unique healthcare challenges that must be considered when applying cyber best practices in this sector. Due to time and budget constraints, many rural hospitals find it challenging to access or use available resources, so coordination must be streamlined to be effective.

Fourth, the federal government should establish a cyber disaster relief program, much like the assistance provided by the Federal Emergency Management Agency (FEMA). Such a program would provide this vulnerable sector with valuable resources in the event of attack, assist organizations in their recovery process, and increase the likelihood that hospitals could keep their doors open following a cyber-attack.

Overall hospital operating margins have been in negative territory for the past 12 months, according to a February hospital report from Kaufman Hall, and margins have decreased year over year for the past eight months. Operating margins are often higher for larger facilities that have outpatient clinics and more ancillary services than a smaller hospital can offer.

Adding to the challenging complexities, nearly 700 healthcare data breaches of 500 or more records occurred in 2022, according to the Office for Civil Rights. While the number of breaches is basically flat, the number of breached records topped 51 million for the first time, apart from the anomalous 2015, when just two breaches exposed 90 million records. Cyber insurance rates also continue to increase, with insurers demanding more monitoring and detection technologies that smaller facilities may not have if facilities can obtain insurance at all.

Because healthcare records are so valuable, hackers aren’t going to stop. Small and rural hospitals need help to protect their systems and patients, and these simple measures are a sensible path forward.

Readers Write: Turning Data into Action to Address Social Determinants of Health

Turning Data into Action to Address Social Determinants of Health
By George Dealy

George Dealy, MS is VP of healthcare applications of Dimensional Insight of Burlington, MA.

Social determinants of health (SDOH) are widely recognized as critical factors that influence the health outcomes of individuals and communities. SDOH refers to the various environmental, economic, and social factors that impact a person’s health, including food and housing insecurity, social isolation, and lack of reliable transportation options.

While most people are aware of SDOH, they often struggle with how to derive meaningful insights from the available data. Therefore, it is essential to explore practical ways to turn data into action to address SDOH and reduce health disparities.

Failing to address social determinants of health can have dire consequences, particularly for underserved populations. CDC data shows that certain populations, such as minority groups, are disproportionately impacted by SDOH. The health outcomes of these groups are often comparable to those in third-world countries.

One of the most striking examples of this is the maternal and infant mortality rates among certain minority populations. For instance, maternal and infant mortality rates are significantly higher among African American women than among other racial and ethnic groups in the United States. Addressing social determinants is a critical step in reducing these disparities and improving overall population health. This highlights the need to use data related to SDOH for actionable change.

One practical way to address SDOH is to use data and analytics. It’s first important to know where to find data related to SDOH. The data can be obtained from various sources, including national surveys, government agencies, and community organizations. One such resource is the US Census Bureau American Community Survey, which provides valuable data on community demographics, including economic and social characteristics at the neighborhood level. As SDOH information is very geographically specific, data at this level can help identify specific needs and target interventions more precisely. Another important resource is the US Agriculture Department’s Food Desert Map, which helps to identify communities where residents lack access to healthy food options.

Additionally, many non-profit organizations aggregate data from various sources into information resources. These resources include the County Health Rankings Project, run by the University of Wisconsin and the Robert Wood Johnson Foundation, and Community Commons, which is a collection of tools and resources for democratizing data related to advancing equitable community health and well-being. These data-centric resources can empower healthcare providers and policymakers with the necessary insights into the needs of the community and identify potential solutions.

The next step is to analyze the data to identify trends to better understand the specific challenges that a community is facing in relation to SDOH. One practical approach is to use technology to analyze and visualize the data. This can help identify trends and patterns more efficiently and communicate findings in a clear and compelling way.

Leveraging data on SDOH can help in the development of targeted interventions tailored to address the specific needs of each community, such as expanding access to healthy food options or improving transportation services. For instance, data mapping tools can identify areas with high rates of poverty and food insecurity, with this information then used to target interventions in those areas. Predictive analytics can forecast potential health risks based on social and economic factors. The use of data and analytics can be a powerful way to identify trends related to SDOH, develop appropriate interventions, and measure their effectiveness.

Improving population health requires a comprehensive understanding and focus on social determinants of health. While healthcare plays a crucial role, it is only one piece of the puzzle. Addressing SDOH requires a practical and collaborative approach that involves analyzing data, leveraging available resources, and recognizing the dire consequences of inaction. By turning insights into action, we can make meaningful progress in improving the health outcomes of communities, particularly among minority populations, and ensure that every citizen has access to the care they need and deserve.

Readers Write: HIMSS23 Recap

HIMSS23 Recap
By Mike Silverstein

Mike Silverstein is managing partner of the healthcare IT and life sciences practice of Direct Recruiters, Inc. of Solon, OH.

The HIMSS Global Health Conference & Exhibition was held April 17-21 in Chicago, where over 40,000 professionals gathered for educational sessions, visited vendor booths, and networked. Our company was excited to get back to HIMSS as a larger group and share these observations.

Trends

AI and tools such as ChatGPT are getting adopted into healthcare quickly. There were a few innovative companies already showing off their new ChatGPT features, which was extremely cool. We anticipate this trend continuing, with ChatGPT having strong use cases in many areas of healthcare tech. We’re just at the tip of the iceberg.

We noticed a pivot towards partnerships. With hospitals struggling, tech companies are putting a big focus on partnerships. Fundraising remains in a slowdown. However, we continue to hear about investments being made for companies going from seed to Series A or to A to B, not in later stages. Series B/C companies are conducting more inside rounds to sustain cash needs.

Direct-to-provider meetings seemed to be relatively slow. We saw a continued trend of companies who sell direct to providers that were looking at ways to break into the health plan space. As hospitals are taking a long time to buy and are very ROI-driven, RCM services are staying strong.

Interoperability has been a theme for over a decade now in healthcare and remains today. Conversation has shifted from inside the four walls of the hospital to how technology receives data and information from what is available globally. A “Designated Record Set” is pushing for provider compliance to ensure their technology integrates with all systems (HIE extender).

Hiring seems to be ramping back up, especially looking into Q3 and Q4. The emphasis remains mostly in revenue-generating roles at the moment. There is also a need for senior finance and accounting. Product and operations roles are hardest to come by.

General Observations

From an overall size and attendee standpoint, the conference seemed to be back to pre-COVID levels. It was key to have pre-booked meetings, but even then, some companies were no-shows. Microsoft’s booth (and Nuance), and surrounding areas were always packed with people. It was great to see a renewed energy at HIMSS despite the ups and downs of the industry over the past few years.

Constructive Feedback

The main hall was split into two sides. The north side felt a bit forgotten, and some did not venture out much to the booths on the fringes. In addition, there seemed to be a lack of places to sit, grab a good coffee, and most importantly, charge your phone.

The last piece of feedback is to bring back the carpet. We noticed one person trip and fall (luckily, they were OK) because of the lack of carpet-to-carpet transition at one of the booths.

Overall, our team is excited about the connections made and the new technologies we saw at HIMSS. We are energized by the passion and innovation of the industry as a whole and look forward to what’s to come.

Readers Write: Labor Augmentation Technology Solutions Automating Manual Processes

Labor Augmentation Technology Solutions Automating Manual Processes
By Kelly Feist

Kelly Feist, MBA is managing director of Ascom Americas of Morrisville, NC.

General care floors represent one area of the hospital that has experienced the most change post-COVID, including increased clinical staff pressure resulting from caring for patients with higher acuity levels than in the past. This continues to be the area of care where continuous monitoring is the exception rather than the rule, and the ability for care givers to have patient contextual information at their fingertips is challenging.

As a result, the possibility of patient deterioration over time goes unrecognized until the patient becomes symptomatic, often resulting in unplanned ICU admissions, activation of rapid response teams, and sometimes other adverse events, such as codes.

By using vendor-agnostic medical device integration capabilities solutions, nursing staff can collect inputs from devices such as patient monitors, spot check monitors, laboratory information systems, EHRs, ventilators, CPAP devices, IV pumps, and more. This information is interpreted via pre-defined algorithms to determine a patient score that is regularly updated and trended. Automated alerts are generated and communicated to appropriate recipients — such as rapid response teams, charge nurses, and physicians — when a change in patient score indicates. The outcome is delivering the needed change in care prior to an adverse event occurring.

Automated, non-latent Early Warning Scoring is a vital tool in preventing unrecognized deterioration on the general patient care floors, ensuring improved clinical outcomes for the patient and financial outcomes for the hospital. There is a real and measurable ROI attached to well executed and automated early warning workflows.

Early Warning Scoring is one of several high-value workflow automations these kinds of MDI solutions can bring to bear to improve nursing efficiencies, create nursing practice safety nets, improve patient outcomes, and ultimately contribute to institutional financial goals.

COVID has had an impact on nursing capacity and on staff-to-patient ratios, resulting in the need for technology to become the force multiplier in the clinical space. There’s incredible opportunity through this technology to elevate the quality of care hospitals can provide today.

Readers Write: Healthcare Needs to Slow-Roll Fast-Moving ChatGPT

Healthcare Needs to Slow-Roll Fast-Moving ChatGPT
By Jay Anders, MD

Jay Anders, MD, MS is chief medical officer of Medicomp Systems of Chantilly, VA.

Now that the initial hype surrounding the chatbot ChatGPT has peaked or perhaps plateaued, its strengths, weaknesses, and applications are being scrutinized.

Perhaps one of the most visible applications revealed recently was the AI tool correctly answering 60% of the United States Medical License Exam (USMLE) medical board exam questions, a task that many top-tier students fail to achieve. This raised a number of concerns about how the technology could, and should, be used in healthcare.

Granted, as an AI language model, ChatGPT has a number of applications in healthcare today, including administrative tasks, triaging patient inquiries, and performing preliminary analysis of medical data. However, ChatGPT is not a trained, certified medical professional and should never be relied upon for clinical guidance or diagnosis. Just like a Google or Bing search, it can provide limited general health information, but it is certainly not a substitute for professional medical advice or treatment.

As a physician, my primary concern with ChatGPT and other large language AI models is that patients accessing the technology will begin to distrust the advice of medical professionals when a disagreement occurs.

Here’s an example of how such a disagreement can go awry. Years ago, a patient came to our practice and told me she wanted to feel like ‘that guy surfing in a wheat field’ in a popular ad for an allergy medication.

When I inquired about her allergy symptoms, she said she had none. She argued that the drug would help her anyway. So, when I would not write her prescription, she switched doctors to one of my practice colleagues. My colleague asked why she was making the change, and I told her. My colleague then revealed that this same patient argued with her as well and then switched to the clinic down the street.

I am a staunch advocate of transparent patient information that is accurate and science based. In this case, a little knowledge could be a dangerous thing. At the time of the dispute, the patient was taking a medication that would interact with this antihistamine and cause a severe reaction.

Although ChatGPT and AI weren’t available at the time of this encounter, the danger is clear. There is a genuine risk that some patients, particularly those without access to primary care or those trying to avoid the inconvenience or expense of an office visit, might rely on AI technology like ChatGPT for medical guidance. This could lead to incorrect self-diagnoses, misinterpretation of symptoms, and any number of potentially harmful consequences. It is essential for consumers and patients to understand the limitations of AI in healthcare and always seek professional medical advice for their health concerns.

AI and the role of the clinician

What is the clinician’s role in this learning curve? Healthcare providers (and naturally, developers of AI solutions) should emphasize the importance of using AI as a supplementary tool rather than as a knowledgeable substitute for professional medical care.

The real issue is the lack of reliable, trustworthy information for patients. Patients, especially those with a rare disease community or with complex conditions, can’t advocate for their own health and care if they don’t know anything about the condition they are battling. Reliable academic medical information isn’t as freely or easily available to them, so they often rely on what they find on the internet to supplement what their doctors tell them for peace of mind and, in some cases, survival. The patient advocacy community calls the patient administrative burden associated with this lack of reliable information “information toxicity.”

That said, patients are already using AI to self-triage, so it’s really up to the medical and technology communities to establish parameters to prevent people from using the technology in lieu of trained medical professionals, or educate them on how to do it safely. Ultimately, it would seem that both communities would work to make the AI better able to do it better.

In my experience as a physician, I’ve encountered many patients who consider themselves quasi-medical experts and excellent researchers. Still, some patients don’t particularly care if the information they unearth is accurate. They just don’t want to feel left in the dark about their symptoms. After all, a wrong answer is still an answer.

Overall, patients want and need to be collaborators in their own care, and with the availability of information being what it is, they are moving forward in the best way available (to them). Unfortunately, the burden is on the physician to correct the misinformation, and that will need to be included in the job description of physicians and nurses going forward. With technologies like this on the rise, with questionable, though increasing, accuracy, there is no choice.

The responsibility is on health systems to educate patients on how to use these technologies and other more reliable websites to research and also regularly share population health information with communities to combat disinformation. Additionally, efforts should be made to ensure equitable access to quality healthcare for all, reducing the reliance on AI technologies for primary medical guidance.

Harnessing AI to supplement clinical decision support

Looking back at those USMLE licensing exams, consider this. The exams are written very discreetly. “A patient presents with X, Y, and Z. What is the diagnosis?” It’s based on a set of facts, and is possibly multiple choice. Humans do not operate that way. Consider a 65-year-old with high blood pressure, elevated cholesterol, diabetes, osteoarthritis, and spinal stenosis. That is not a single question, it’s multiple conditions. Physicians are trained to mesh those conditions together because a treatment for any one condition may exacerbate another. An exam would not approach it this way.

Physicians need to learn how to use AI to augment their practice, knowledge, and skill, not the other way around. Harnessing AI as a supplement to clinical decision support is a promising option.

For now, ChatGPT is out there, and it will be used, sometimes for medical advice. That’s all well and good until it makes a mistake or doesn’t surface something of importance. Meanwhile, there are technologies in use that work with clinicians, in their workflow, and present clinically relevant information regarding conditions in a way that mirrors the way they think and work.

The human element is, by necessity, still very much at the center of healthcare. So, for now, let’s slow the roll on ChatGPT. Let it mature. Crosscheck it. See how it evolves as its models are further trained and deepened. The technology holds tremendous promise, but is still in its infancy.

Readers Write: HLTH, CHIME, ViVE, HIMSS — Choose and Invest Wisely

HLTH, CHIME, ViVE, HIMSS — Choose and Invest Wisely
By Steve Shihadeh

Steve Shihadeh is founder of Get-to-Market Health of Malvern, PA.

Many vendors in the health technology space have just attended ViVE and/or are preparing for HIMSS to showcase their offerings, meet with clients and prospects, and engage with their investors. For most of our clients and friends in the industry, these shows represent a significant investment of time, money, and education for their teams.

Execute these shows right and reap the rewards in new contracts, bought-in clients, and investors who get your plan. Do it not so well and you miss out on the year’s biggest potential market exposure for your company. What can you do to maximize this opportunity?

ViVE

If you just went to ViVE looking to find a vast field of potential customers on the exhibit floor, you probably came up wanting. One well respected colleague called it as “six vendors for every buyer.” The main live customer engagement seemed to come from the well-run Hosted Buyer Program in the ViVE Connect Lounge. This is a “pay for x meetings” gig, where the matched potential customer is trading their time to hear your pitch for reimbursement for ViVE fees and possibly T&L.

Based on our experience, about half of these meetings have potential value for you, but it is in your hands. It is really speed dating. You must be on message, be quick to read the buyer, and put your best foot forward.

ViVE is smaller (although just about every company in the space made sure to have someone there), more posh, and easier to get your head around than HIMSS. If I was mostly intent on getting time with investors and partners, I would have gone to ViVE. If I was mostly focused on potential buyer organizations …  let’s go to HIMSS, which comes up in a few weeks.

HIMSS

HIMSS is the grand old show of the health technology business. In its heyday, 40k+ attendees and healthcare IT companies sent everyone from the CEO to their junior sales reps. COVID and the way HIMSS handled the associated cancellations knocked the show for a bit of a loop. Their breakup with CHIME was the next issue. Now ViVE, in association with CHIME, is giving them competition that they did not have before, along with HLTH.

HIMSS has historically been gigantic and hard to navigate. However, it had most everyone in the business in one enormous convention center, along with a sea of potential buyers evaluating systems. HIMSS attendees slant heavily towards IT staff and leadership, some clinical and financial executives who are in the market for new systems, and an occasional CEO. Given its size, HIMSS takes extra work, extra people, and extra prep to get the most of your investment, but it is too tempting to pass up for most every company in the business.

Given the hip and more accessible vibe of ViVE (sorry, could not resist), I suspect HIMSS will make some adjustments. Good competition will do that for you. It will be great to be at HIMSS and get a real pulse of the industry post-COVID and in the new AI-everything world.

A Few Words on CHIME and HLTH

CHIME (College of Healthcare Information Management Executives) is the single most important event for CIOs and those who are on a CIO career track. They run a fabulous boot camp for people in the field and have an agenda aimed squarely at the CIO. Vendor participation is carefully managed and expensive, but several key luminary vendors in the field, such as Epic, have built their business around important relationships that it established with CIOs via CHIME. If CIOs are crucial to your business, CHIME is the place to be.

HLTH is a relatively new show hosted by the same folks who run ViVE. It is well run, quite upscale, targeted mainly at investors, and attracts enough potential buyers to make it interesting. For those who have been to the JP Morgan Healthcare conference, HLTH is a friendlier environment to tout your plans, show your wares, and get quality time with all the key investors in the space.

Where to Make Your Investments and Our Top 10 Tips to Maximize Your Return

Given how expensive any of these shows can be, we have some suggestions on how to get the most return.

1. Have a presence, even just one key person, at as many of the shows that you can. It is a great way to drive meetings and prospect engagements, e.g., “Are you going to HLTH?” A lot of startups that cannot justify the fees of the show still get mileage by being around the event and meeting potential partners at local hotels.
2. Based on your company’s priorities, pick your most important show for any exhibit booth investments. Securing, staffing, and running a booth at a show is a significant investment.
3. Send only your best, most committed people. Buyers will judge you by your staff.
4. Be fully committed to setting up meetings in advance. Know who in your target market is attending and schedule meetings and interactions. Do this whether you are exhibiting or not.
5. Train in advance on any new announcements and have your team arrive early for last minute retraining.
6. Trade shows are a fantastic selling opportunity and need to be treated as such. Booth personnel need to be focused on facing clients and prospects and not connecting with industry friends. The best-run booths are hardcore about this. Be best.
7. Booth hygiene matters. Have a dress code and stick to it. No food. No cell phones. No rep-to-rep chatting.
8. Get a modern lead tracking tool and train your booth staff on it.
9. Force (gently) all inquiries to your front desk so you can properly capture their info and direct them to the right staff in your booth.
10. Get your leads into your CRM system ASAP and send immediate follow-up notes to all visitors.

Trade shows are a big lift, so make sure that you and your team are aligned, prepared, and motivated to have a great show. Hone your messaging so that it works from a buyer’s perspective. Practice your demos until they are crisp. Worry about the details and have a fantastic event.

Readers Write: The Myth of the Golden Health Record

The Myth of the Golden Health Record
By Peter Bonis, MD

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

In 2009, President-elect Obama signaled his plan for the federal government to support the adoption of electronic medical records (EMRs). His expectation was that broad adoption would “…cut waste, eliminate red tape and reduce the need to repeat expensive medical tests,” adding that, “it just won’t save billions of dollars and thousands of jobs; it will save lives by reducing the deadly but preventable medical errors that pervade our healthcare system.”

The subsequent Health Information Technology for Economic and Clinical Health (HITECH) Act, part of the larger American Recovery and Reinvestment Act, achieved his directive, at least in part; most healthcare systems now use EMR systems. However, the strategic objectives of reducing costs and preventing medical errors have been elusive. Preventable medical errors remain common, growth in healthcare spending has not been reduced materially, and healthcare providers frequently cite EMR systems as being an important contributor to professional burnout. 

In this backdrop rests a common belief that the full promise of EMR systems has yet to be obtained. It will only be achieved once patient data can flow unimpeded from system to system, permitting healthcare providers (and other stakeholders involved in healthcare services) to have a comprehensive view into patient care wherever it is delivered, a concept referred to as interoperability. Over the years since the HITECH act was passed, many barriers posed challenges for achieving such a vision including concerns related to data privacy, deliberate blocking of information flow (especially when it interfered with business models), and approaches to gathering and making sense of intrinsically messy data.

Nevertheless, the journey has continued; key pieces of legislation and advances in technology have led to demonstrable improvements in interoperability.  Most recently the federal government gave the objective a boost by advancing standards and designating qualified health information networks intended to establish a universal floor for interoperability across the country. As a result, the healthcare system is marching toward a comprehensive, golden health record.

But once we have it, will the golden record enhance the quality, safety, and effectiveness of care? The answer is unsurprisingly no unless more is done to use the data effectively. Primary care providers would need almost 27 hours a day to deliver all the guideline-recommended care, according to one estimate. In this context, more information is not better.

Healthcare data must be delivered in ways that are useful for busy healthcare professionals working in varied settings. Information must be high value and organized into consumable payloads and workflows not only for time-pressed individual clinicians but for extended care teams. It should ideally support decision-making and subsequent actions while saving time, reducing cognitive burden, decreasing administrative overhead, measurably improving the quality and safety of care, and reducing costs. The golden health record is a welcome enabler, but will not in itself accomplish these objectives. 

So, what is needed? Foremost is recognition that the matter is critically important. The core of healthcare delivery is an interaction between providers and patients where decisions are made and care implemented. There is much to be gained by making it easier for healthcare professionals to take care of patients.

A greater sense of urgency is needed. Burnout and other challenges are leading to attrition of healthcare professionals. There will be a shortage of primary care physicians and hence a need for advanced practice providers (such as nurse practitioners and physician assistants) to take on greater responsibilities. They will need help. At the same time, healthcare services are becoming more distributed to new sites of care, such as retail pharmacies and to digital health technology companies, creating greater challenges for coordinating care and optimizing flow of useful information. The proliferation of devices that generate healthcare data adds further complexity.

EMR vendors need to expand capabilities, focusing on metrics that are directly relevant to the experience of various user types and ultimately to the quality of care delivered. Financial incentives and payment models must justify the investment for both EMR vendors and providers.

Healthcare professionals want to deliver exceptional care for their patients. Let’s make their needs a priority. The golden health record is a worthy goal, but the usability of the data should get equal attention.

Readers Write: Healthcare Delivery Must Evolve to Meet the Needs of a Generation in Crisis

Healthcare Delivery Must Evolve to Meet the Needs of a Generation in Crisis
By Bob Booth, MD

Bob Booth, MD, MS is chief care officer at TimelyCare of Fort Worth, TX.

A recent new report from the CDC shows startling trends about the never-before-seen levels of hopelessness and suicidal thoughts among teens.

The findings for teenage girls in the CDC’s 2021 Youth Risk Behavior Survey were particularly grim. Nearly three in five teen girls (57%) said they felt “persistently sad or hopeless,” the highest rate in a decade. And 30% said they have seriously considered suicide, a 60% increase over the past decade.

While boys generally fared better overall, more than 40% of boys and girls said that they had felt so sad or hopeless within the past year that they were unable to do regular activities, such as schoolwork or sports, for at least two weeks.

The members of Generation Z, born in 1997 or later, are experiencing unprecedented levels of stress and emotional turmoil. While some of this is likely to ease as they age out of adolescence, adulthood is certainly no cure for depression, anxiety, loneliness, and other stressors. Additionally, double the number of Generation Z members report feeling emotionally distressed compared to older Millennial and Generation X groups.

In order to meet the healthcare needs of Gen Z, particularly mental and behavioral health, the industry needs to become more proficient in its use of digital and virtual care tools. However, not all of these tools are equally effective or designed to meet these young patients where they are.

The digital-native generation that has never known a world without the Internet or smartphones expects that their preferred technology will deliver relevant information and an engaging experience as part of treatment. It’s something we can expect to see more of as part of the future of care for younger generations.

Artificial intelligence (AI) can play an important role in care delivery and engagement if the algorithms enable a highly personalized and patient-centric experience. For example, not all young adults are ready for, or want, 50-minute, one-on-one counseling sessions with a mental health professional. AI can accurately analyze and interpret intake screenings, so a patient’s selected care pathway is the most relevant and takes into account their unique health and personal needs. AI can even help guide digital-only care pathways through content and activity selection based on the young adult’s interactions with the solution.

The promise of AI is that it delivers an even more personalized experience as its algorithms learn more about young adults, which accelerates their growth and motivation to improve their mental health and well-being. These engagement-building concepts have been understood and applied in other consumer-facing technology for years. Healthcare is finally catching up, and that’s good for young adults and healthcare overall. It’s exciting to see where this will take us in the future.

Gen Z needs a solution that leverages personal technology to enable access to mental health and well-being at their fingertips. By seeking tech-enabled help from a healthcare platform that is designed for them and understands their unique challenges, Gen Z can develop the skills and resilience to help them prepare for college and beyond.

It’s time for healthcare to look beyond traditional models of healthcare delivery and meet a generation who so badly needs care where they are.

Readers Write: The Impact Intelligent Automation Can Have on Healthcare Costs

The Impact Intelligent Automation Can Have on Healthcare Costs
By Krishna Kurapati

Krishna Kurapati is founder and CEO of QliqSOFT of Dallas, TX.

RPA stands for robotic process automation. RPA uses technology to automate repetitive human interactions with a computing system. In other words, instead of a human clicking a button over and over to generate a desired outcome, the system automatically connects and completes the stipulated work process, eliminating significant amounts of manual steps and time for the care team.

A similar approach applies to robotic workflow automation, where a chatbot can automate manual and repetitive tasks between a care team member and a patient. For instance, in healthcare administrative and clinical support tasks, the end-to-end steps include reminding a patient of an appointment, sending them digital forms to complete before the visit, automating patient check-in, and reinforcing instructions after the visit. Each task’s workflow comprises a number of work processes to gather, upload the patient’s information to an electronic health record (EHR) system, and to communicate with and guide the patient.

To illustrate, let’s examine the case of patient intake: Today, the office staff creates a paper clipboard and shares it with the patient on arrival, who takes five to 15 minutes to complete the paperwork. Staff then looks up the patient record, scans and uploads the requisite forms to the EHR, and checks the patient in. Humans can be removed entirely from this sequence of steps with RPA and chatbots, which automate the workflow to capture and process the requisite patient data to meet clinical and billing purposes.

The benefits of intelligent automation in healthcare

Faced with a never-ending need for reporting and data entry, healthcare organizations must manage high volumes of administrative duties. A recent study found that the average employee spends 60 hours per month on easily automatable tasks, making healthcare an ideal use of RPA to digitize and scale manual, routine processes. The upshot is dramatically reduced labor costs while optimizing workforce usage for lower costs.

In a January 2023 paper by the National Bureau of Economic Research titled “The Potential Impact of Artificial Intelligence on Healthcare Spending,” the authors calculated that hospitals employing AI-enabled use cases could achieve total annual run-rate net savings of $60 billion to $120 billion (roughly 4% to 10% of total costs for hospitals) within the next five years using today’s technologies, without sacrificing quality or access. The Academy projected that 60% of clinical workflows can be automated through AI, including bots, signifying untapped potential in new revenue and cost reduction.

The role intelligent automation will play in transforming healthcare

Digital health is about delivering care and managing data electronically. Unfortunately, many patient experiences at healthcare systems and practices are handled through traditional communications, including paper transfer, phone calls, snail mail, and fax. This can lead to disconnected patient communication, misdiagnosis, medical errors, waste, and poor quality care. Digital capabilities help providers, innovators, payers, and other stakeholders come together collaborating in an agile, more communicative way to solve problems, overcome scalability limitations, empower patients, improve efficiencies, and speed up throughput.

Once digital infrastructure and capabilities are built, the robotic process automation sits on top to automate workflows. The conjoining of digital and RPA accelerates and scales processes and elevates innovation to create a new standard for the patient experience.

Current use of intelligent automation

Automation started in revenue cycle management processes and is relatively new to the clinical side of healthcare, where the initial focus is processing and management of large quantities of paper into the EMR or content management systems.

Although automation is now happening on the clinical side, it’s not yet well adopted. The most common focus areas are patient communication regarding appointment reminders, appointment scheduling, patient intake, billing, procedure readiness, documentation management, and evidenced-based content for patient education.

The future state of intelligent automation in healthcare

Automation’s ability to simplify healthcare is limited only by our imagination. The cost of labor has skyrocketed to 64% of total operating costs, creating new pressure to reexamine workflow and adopt automation. Healthcare has two broad categories where automation can be of service:

• Eliminating work by automating existing manual, repetitive administrative tasks staff are doing today.
• Supporting automated communication and monitoring needs not possible today because of staffing limitations, such as readmission prevention.

I expect intelligent automation to play a larger role in healthcare for years to come. The time is now to blend clinical and business efficiencies to improve operations and provide relief to overworked and understaffed healthcare professionals.

Readers Write: A Glimpse of Telehealth’s Future: Five Takeaways from ATA 2023

A Glimpse of Telehealth’s Future: Five Takeaways from ATA 2023
By Lyle Berkowitz, MD

Lyle Berkowitz, MD, is CEO of KeyCare of Chicago, IL.

My recent visit to the American Telemedicine Association (ATA)’s annual meeting offered an opportunity to briefly reflect on how far the industry has come, as well as provided a few glimpses of what the future of telehealth might hold.

When ATA was founded three decades ago, broadband internet was a rare commodity and telehealth visits were primarily via phone calls. Fast forward to today and it’s obvious that telehealth is leading healthcare transformation in multiple areas, from urgent care to women’s health needs to lifestyle medications – while pushing how we can use virtual care tools to simultaneously improve the patient experience, quality, and cost.

Here are five of my key takeaways from the ATA 2023 Annual Conference and Expo:

1. The rise of femtech and women’s health. Numerous startups are developing solutions that leverage telehealth to address women’s health issues. For example, Nest provides virtual same-day lactation support and has partnered with several hospitals to improve infant health outcomes. Separately, SimpliFed partners with caregivers before an infant is born to develop feeding plans and delivers support to patients through a virtual breastfeeding provider network.
2. Increased focus on hybrid care. In this context, hybrid models refer to those that offer patients access to telehealth visits which can coordinate with in-person care, based on a patient’s individual care needs. Corporate giants like Amazon, CVS, and Walmart are lurking around in this space, but health systems have the greatest potential to own it. That’s because it is far more straightforward, simple, and cost-effective to add a virtual care partner to a robust office-based health system than to bolt on office-based care to a virtual care company.
3. A new market for hearing aids. Over-the-counter hearing aids are now available to the public, thanks to a ruling by the US Food and Drug Administration last year. As a result, companies like Audicus have jumped into this market to serve customers via telehealth. In this easy and convenient process, a hearing test is performed online, a hearing aid is shipped out, and any adjustments are done via a video visit.
4. The rise of remote patient monitoring. Like telehealth, remote patient monitoring (RPM) technology has been around for decades, and while various startups have different approaches for obtaining data, they all have the same vision in mind. For example, some companies use a wearable patch for continuous monitoring, others use Bluetooth to connect to devices a patient may have, and “device-less” companies use a chatbot that allows a patient to self-enter data. Some may even combine these tools or add others. Then all of this data is sent to a dashboard for analysis and display so that a virtual team can appropriately monitor and engage with patients, and then identify outliers which need to be escalated to office-based providers. However, the real trick is knowing where to apply RPM and align incentives. The post-acute care area has been popular for years; the chronic care space has experienced slow growth but offers strong potential; and the new hot area is clearly hospital at home.
5. Niche products. It has become easier for companies to focus on specific use cases for virtual care monitoring and management. For example, I came across the super niche startup Staling Medical, which has created an at-home urine diagnostics tool that uses a patient’s smartphone microphone to listen to their urine stream, with a goal of improving outcomes for recurrent urinary tract infections, urinary obstructions, and chronic kidney disease.

It’s a fun time to be in telehealth. I’m looking forward to seeing what’s up next at ATA 2024!

Readers Write: Value-Based Care Arrangements: Four Ways Specialty Care Providers Can Prepare for Claims Data

Value-Based Care Arrangements: Four Ways Specialty Care Providers Can Prepare for Claims Data
By Tyler Johnson

Tyler Johnson is VP of strategic partnerships at Ursa Health of Nashville, TN.

Companies that are bringing new specialty care models to market face a big early hurdle when partnering with plans, full-risk provider groups, or self-insured employers: working with claims data. Although lacking some clinical context, clean, well-organized claims data is vital for creating longitudinal patient views and the main fuel for analytics (which of course become even more powerful when supplemented with clinical and other data sources). Trusted analytics, in turn, are the first step toward optimal operations and outcomes, as well as the financial reconciliation between partners that value-based contracts require.

With everything else involved in launching or expanding a new business, specialty care providers (SCPs) may be tempted to put data and analytics planning on the back burner. Those that delay too long, however, could find themselves scrambling to get new partnerships off the ground or to keep up with an ever-changing landscape. In the best-case scenario, late-night heroics save the day but inordinately stress the team. In the worst, lack of planning leads to lost sales, crumbling partnerships, and dwindling rather than growing healthcare impact.

SCPs must ready themselves to consume claims data from their partners in four key ways.

The first concerns the security review. Organizations are very particular about how and where their data gets shared. Convincing business or clinical leadership to try a novel intervention is tough, but convincing security and InfoSec folks that others can be trusted with their most prized possession is another obstacle altogether.

Before any data is shared, an organization will ask its potential SCP partner to submit to a comprehensive vetting process to ensure the SCP’s technical and administrative safeguards are strong enough to meet both internal and HIPAA requirements. To prepare for the review process, the SCP should:

• Create very tight and easy-to-understand documentation around its technical architecture, including where data is going to live and what people, tools, and processes are going to touch it.
• Create an overview document that summarizes its security posture.
• Organize employee business and security procedures for easy reference.
• Devise a system for retaining answers to assessment questions to expedite the next review.
• Consider being HITRUST and SOC2 certified, which can quickly ease the security team’s concerns. Because the level of effort isn’t trivial, working with a technology vendor that is already certified can help organizations that do not have the internal resources to pursue certification themselves.

Second, an SCP needs to prepare is its tech stack. The contracting and security assessment process can feel a lot like hurry up and wait, but the reality is that this is a task in a very long queue, and once the organization assigns resources to complete that task, it will expect a new partner to be ready to roll.

If the SCP can tap into and pull from the organization’s existing infrastructure for hosting data, great. If not, it needs its own secure cloud storage mechanism (e.g., Amazon S3, Azure blob storage) into which data can be dropped, as well as a pre-defined process for granting access to it. In addition to transmission mechanisms, a database/warehouse and any data modeling and transformation tools must be up and ready to use.

The potential partner is also going to expect the SCP to quickly provide feedback and ask questions about the data. If the environment is ready to go in advance, the SCP can spend more time on loading and investigation instead of provisioning cloud resources. It is also extremely helpful to get answers to data questions while the company’s technical resources are still engaged and informed.

As a final note, SCPs should think in advance about how they will assess the quality of the incoming data, both in a general sense (e.g., data completeness) and regarding specific data points or lineage that is important to their analyses.

The third way to prepare is to ensure a scalable approach exists for organizing and analyzing data. Without a proactive approach to a data model, an SCP can very easily stack up technical debt — in the form of silos of logic and code that are custom to analyzing data from a single source — that becomes a nightmare to untangle down the road and will prohibit efficient scaling of its business.

Once it has defined the data model, the SCP should apply transformation logic to all incoming raw data sources to map the data to that standardized structure. Rules and algorithms to interpret data for specific use case(s) should only be authored on top of that standardized data model, an approach called hierarchical data modeling. This approach facilitates scalability while making it easier to marry up claims data with other sources of information: for example, clinical data from the EHR, patient engagement data, and internal product data.

The final way to prepare is to identify how the claims data will be used to provide insight into their operations and business. This planning should occur before any value-based contract is signed, let alone claims data is shared, to help determine whether other sources of data will be needed — for example, other patient data or industry-available supplemental data such as value sets and provider rosters. Armed with a clear understanding of what insights need to be derived, SCPs can more intelligently articulate their needs and the anticipated value to partnering organizations.

Effective partnership in the co-management of patient populations demands a strong data foundation paired with trusted, useful analytics. Bad data in results in bad data out. SCPs embarking on new value-based partnerships can increase their chances of success and make life easier for both parties with some basic preparation. With a solid and scalable data foundation in place, technical resources can shift their attention away from non-strategic data wrangling work and focus on building the special sauce that differentiates it from competitors and adds the most value to its customers.

Readers Write: The Cost of Doing Nothing: Five Learnings from the Build versus Buy Debate

The Cost of Doing Nothing: Five Learnings from the Build versus Buy Debate
By Kimberly Hartsfield

Kimberly Hartsfield, MPA is EVP of growth enablement at VisiQuate of Santa Rosa, CA.

It’s a conundrum that health system executives regularly face. Build a much-needed software solution in-house or buy it from a vendor?

Once hospital leaders identify the need for a solution that requires new functionality, the debate is on. Revenue cycle management (RCM) solutions are no different.  While many hospital IT departments are no doubt capable of designing, constructing, and implementing new RCM solutions, leadership must decide whether taking this route is likely to yield the best business results.

Often, it starts with hospital leaders surveying vendors, seeing the price tag, and deciding to embark on the journey to complete the project internally, with the premise that it will be at a much lower cost. The decision frequently backfires. Rather than making the investment and having the technology that hospitals need to support their RCM operations efficiently, do-it-yourself health IT projects often end up taking years to fail and costing hospitals far more than if they would have signed with a vendor in the first place.

Health IT leaders see a pretty Tableau or Qlik dashboard and think, “We can do this ourselves.” When it comes to data visualization, they probably can. What they don’t consider is that the data aggregation, normalization, and transformation work that happens under the hood is actually the challenging part of RCM transformation.

The following are factors to consider when considering whether to build or buy a new RCM solution.

Complex health IT projects require more than health IT

IT departments sometimes believe that because they have their own developers and analysts, they can design, build, and implement complex health IT systems on their own. However, complex health IT projects require far more than technical skills. There must be business knowledge and experience married to that technical skill. Frequently that is where the projects break down because the people with the business knowledge already have full time jobs in the organization that are not related to building a platform.

Indeed, the reality of large IT projects is that they frequently exceed timelines, go over budget, or sacrifice important functionality. For example, one in six large IT projects have an average cost overrun of 200% and a schedule overrun of almost 70%, according to Harvard Business Review. Similarly, 56% of IT projects fall short of the original vision, according to a study by McKinsey.

It’s all about speed to value

Leading RCM vendors have been waking up every day for years thinking about how they can work to evolve revenue cycle analytics and deliver value and ROI to clients. Vendors have the benefit of having seen and evaluated RCM systems from healthcare organizations of many different shapes and sizes across the country. They understand best practices, having implemented RCM solutions alongside numerous electronic health records systems. This experience enables the ability to identify idiosyncrasies that hide within data and frequently uncover gaps that clients didn’t know existed.

While hospital do-it-yourself RCM projects may take years to complete, leading vendors can perform an installation in 90 days, delivering immediate insights and ROI.

RCM processes are broken and technology is the fix

It’s an unprecedented time for healthcare. There is no model for the circumstances the industry is undergoing, given labor shortages, supply chain constraints, and the financial after-effects of the COVID-19 pandemic. Across the nation, hospitals are pushing for more automation to augment staffing issues, letting their staff focus on tasks that require decision making, not repetition.

In many cases, RCM processes are broken, and technology is the only route hospitals can take to do more with less. Hospitals must lean into technology and automation, leveraging data to build predictive models and using artificial intelligence and machine learning to boost efficiency.

Unless hospitals are large, mature, and complex, they typically don’t have the resources to handle a large RCM project internally. Smaller hospitals often lack resources like a database administrator, a data warehouse, and data scientists who can build predictive analytics models, for example.

RCM processes continually evolve

It’s easy to forget that RCM projects typically are not “build it and you’re done” solutions. In addition to building RCM solutions, hospital IT departments must provide ongoing support and maintenance. These projects continually evolve, with new requests for additional reports or functionality upgrades. This often requires analysts, engineers, and other highly paid technical resources that are difficult to find and are only growing more expensive.

Further, it’s an open question as to whether build-it-yourself solutions deliver enough value and differentiation to be worth the time, expense, and effort. For example, if all an organization’s competitors can simply build their own systems to accomplish a certain objective, then that system is hardly a source of competitive advantage.

Move from descriptive to predictive

RCM employees cannot manage by spreadsheets. The industry is moving beyond rows and columns. RCM employees need to be able to visualize data to detect patterns to quickly identify outliers and manage by exception. Additionally, hospitals must move their RCM processes beyond descriptive analytics to predictive and prescriptive analytics.

It is no longer acceptable for hospital leadership to simply understand what happened yesterday. Hospital leaders must look to the future with the ability to anticipate and predict what will happen tomorrow, next month, or even in six months. Through automation and advanced data analytics, leading RCM solutions drive those insights.

Readers Write: Making a Case for Digitizing HICS Protocols and Emergency Notification Processes

Making a Case for Digitizing  HICS Protocols and Emergency Notification Processes
By Dave Sinkinson

Dave Sinkinson, MBA is VP of mobile at Rave Mobile Safety of Framingham, MA.

Patient safety is and always will be at the forefront for healthcare systems. While planned events such as a move or community open house warrant and receive pre-planning by hospital officials, emergency situations can and do arise in the healthcare setting at a moment’s notice. Whether it is a global pandemic, natural disaster, system malfunction, or a violent incident, hospitals must plan for and respond quickly to adverse events, not only for the sake of their patients, but also because they have a duty of care to safeguard employees and visitors to their facilities.

That’s where the Hospital Incident Command system (HICS) comes in. HICS offers hospitals and other healthcare organizations a standardized framework for managing complex emergencies and helps health systems prioritize safety. But meeting the various HICS requirements in the midst of an already busy role can be arduous for health system emergency managers and those they rely on during crisis events. Healthcare safety practitioners are ditching outdated tools and technologies — such as printed manuals, paper phone tree lists, or legacy communication systems — in favor of digital solutions that streamline work, automate compliance, meet quorum requirements, and improve notification capabilities.

Digital transformation is certainly not new in the healthcare sector. Healthcare safety leaders have been using technology to improve patient care processes and outcomes for years, and to help with emergency notification. However, healthcare safety practitioners are realizing the benefits of using these same tools to digitize incident command protocols and to enhance operational efficiencies.

The Mayo Clinic is using technology to tackle the manual, time-consuming tasks on their HICS to-do list. Their automated approach to industry compliance is not only ensuring that all HICS team members are on the same page, in real time, it is ticking the box on staff accountability, notification, and reporting. Emergency management professionals at the world’s top hospital recognized how unrealistic it was for key personnel to access hard copies of crisis plans with detailed responsibilities or to search for materials in times when seconds matter. They digitized safety protocols, resources, and benchmarks in a handy, one-stop app that not only helps them to accomplish necessary HICS steps, but allows them to do even more, for example, further leveraging the Common Alerting Protocol (CAP) for a more integrated approach to safety.

By automating HICS activations, healthcare safety professionals can lay out emergency response plans in the order they need to be carried out, manage permissions, reassign responsibilities if they are not undertaken swiftly, and notify certain audiences about an event unfolding and steps that need to be taken. Technology also captures important data including when an emergency alert was sent, who received important messages, and which HICS team members performed emergency response actions.

Health systems are also tapping into these tools for non-emergencies. They are being used to communicate about staffing shortages and to share severe weather updates that may impact employees coming to work or leaving their shift. They are also being used more often as digital resource centers. In the past, it may have been sensible to house emergency preparedness and response materials on a hospital website or intranet portal, but when you consider how many of us are tethered to our phones these days, it just makes sense to prioritize safety apps.

As with anything worthwhile, it is not simply a matter of building it and they will come. Hospitals must consistently communicate about safety tech solutions via signage, during meetings, and as part of staff onboarding to raise awareness and encourage usage during crisis situations and as part of the health system’s engagement culture. They must commit to training staff at different intervals throughout the year so that personnel can take an active role in their own personal safety by using anonymous tip-to-text technology and two-way communication components, or simply just so they know where to go for important hospital updates. Reviewing page visits and other digital data can also help hospitals to better understand what is resonating with employees and what may need tweaking or highlighting.

Communication and collaboration are the foundation for any HICS plan. With the push of a button, safety apps can effectively connect hospital leaders with people in the trenches while simultaneously informing first responders of an emergency situation.

Readers Write: Social Determinants of Health and Interoperability

Social Determinants of Health and Interoperability
By Jada Parker

Jada Parker is a public health graduate student at George Washington University.

Social determinants of health (SDOH) have a huge impact on population health. SDOH can be defined as the conditions and environments where individuals are born, live, learn, work, play, worship, and age. Political determinants of health, such as voting patterns, government makeup, and policies, have led to SDOH and the resulting population health inequities.

SDOH can be divided into five domains: economic stability, education access and quality, healthcare access and quality, neighborhood and built environment, and social community context. These factors heavily influence health, functioning, and overall quality of life. Care management and community partnerships allow healthcare organizations to address patients’ social needs in areas such as housing, food security, financial assistance, and intimate partner violence.

Health IT can help physicians and clinicians address and understand how SDOH impact their patients’ overall health. Investments in health IT can also support care management in better addressing SDOH to improve patient health.

Interoperability across IT systems plays a pivotal role in addressing SDOH. When organizations can share patient healthcare data, community partnerships are strengthened and providers are able to provide more streamlined referrals to and better coordination with social service resource providers. Resource providers and care management teams are better able to help patients manage chronic conditions as well through care coordination with healthcare providers allowed by interoperability.

Patients who are experiencing homelessness provide a prime use case of how interoperability facilitates care coordination to address SDOH. Homelessness heavily influences overall health, as it may interfere with a patient’s ability to take their medication as prescribed. Homelessness can also result in multiple hospital readmissions for a number of reasons, including poor health management and that a night at the hospital may provide better conditions than a night at a shelter or outside.

Care coordination, improved by interoperability, allows physicians to make social care referrals and share information with necessary outside resource providers. Without interoperability between health IT systems, much of the burden of obtaining and keeping up with paper referrals and records falls on the patient.

Organizations like Administration for Community Living (ACL) provide IT solutions to support healthcare and community-based organizations partnering in order to provide social and whole-health care for the elderly and individuals with disabilities. ACL incorporates open application programming interfaces to provide resource directories through their Open Referral Initiative. These types of IT solutions streamline the referral process, improve care coordination, and strengthen community partnerships .

SDOH data gaps pose limitations to interoperability. However, there are emerging standards for using and sharing SDOH. The Gravity Project is working to define SDOH information so that it may be documented and shared across digital health and human service platforms. ONC Health IT Certification Program and ONC Interoperability Standards Advisory provide many of the current interoperability standards.

Readers Write: How Healthcare is Using AI to Address the Staffing Shortage

How Healthcare is Using AI to Address the Staffing Shortage
By Ed Ricks

Ed Ricks, MHA is managing director of healthcare consulting for Baker Tilly US of Chicago, IL.

One of the greatest challenges facing healthcare providers in 2023 is staffing shortages, particularly among medical doctors and nursing professionals. This stems from a variety of factors, including shifting demographics with an aging Baby Boomer population that is increasing the demand for medical care, a generation of nurses and doctors retiring, the education system not being equipped to create enough new medical professionals, and the demands of the profession that was exacerbated from the coronavirus pandemic.

With staffing shortages not going away any time soon, patients are continuing to experience adverse effecting, including a greater chance for errors, higher morbidity and mortality, employee burnout and dissatisfaction, and patient dissatisfaction, which create a spiraling expense to provider organizations.

Nonetheless, many experts predict that the influence of AI can help address the shortage, so I thought I would ask an AI engine to tell me a story of how AI could help. Here is the response from ChatGPT:

Once upon a time, in a world filled with endless technology, healthcare was facing a major problem. The nursing shortage was plaguing hospitals and nursing homes, leaving patients without proper care.

But then, a group of tech geniuses decided to put their minds together and come up with a solution. They created AI robots designed specifically for the healthcare industry. These robots were programmed with all the knowledge and skills necessary to be a nurse, and they could work 24/7 without taking breaks or getting tired.

At first, people were skeptical about the use of robots in the healthcare field. But as soon as the AI robots started working, everyone was blown away by their efficiency and care. They could do everything a human nurse could do, and they did it better! They never made a mistake, and they never complained about working overtime.

The nursing shortage was soon a thing of the past, and hospitals and nursing homes were able to provide the best possible care to their patients. Everyone was happy, except for the nurses who had been replaced by the robots. But they eventually found other jobs, and they were happy too.

And that, my friends, is the story of how AI robots fixed the nursing shortage in healthcare and made the world a better place. The end.

While this sounds cool, the reality is that proven methods already blend AI technology using computer vision and other input feeds with a repeatable change management framework. By doing so, providers can apply advanced analytics to determine ROI and the best impact opportunities that will lead to dramatic results for their patients and their margins.

Using this type of technology can help harmonize workflows, automate documentation tasks, and simplify communications to give nurses time back to spend with their patients. This leads to reducing friction from the act of providing care. When this type of automation is coupled with a remote nursing and care strategy across an organization with appropriate change management, the results can be far greater.

When combining the use of a proven technology platform with proven outcome driven change management strategies, health systems can see a dramatic impact on reductions in staffing costs, nursing turnover and associated costs, and patient falls. They can also see improved patient outcomes and increased staff and patient satisfaction.

We don’t have to wait for the day that we really have the sentient robots running around delivering full care in health systems. Let’s use the available processes and AI tools now to help providers make a difference on their transformational journey.

Readers Write: I Know You’re Not Thinking About Blocking Patient Data

I Know You’re Not Thinking About Blocking Patient Data
By Troy Bannister

Troy Bannister, MS is co-founder and CEO of Particle Health of New York, NY.

If you’ve worked in healthcare for 10+ years, this is probably the one hundredth time you’ll read an article about patients being unable to access their own medical data. Here’s why I think that trend might be coming to an end soon.

During healthcare’s slow digital transformation, care providers moved from fax machines to 1:1 digital pipelines to manual portal scraping tools, just to get the charts of their patients. In many instances, these processes are occurring in what are negatively referred to as data silos.

Technology solutions for improving healthcare data accessibility have evolved over the years. The legal requirements for complying with data accessibility have followed closely behind. Most clinicians are connected to nationwide interoperability networks through an EHR, theoretically giving them at least some ability to exchange patient records. Now, the US government has made it clear that their patients have the right to access their medical records in those same technically feasible ways.

Enter the Anti-Information Blocking Rule, the culmination of government efforts to legislate clinical data accessibility. Just like it sounds, its goal is to eliminate practices that interfere with the access, exchange, and use of electronic health information.

Whereas HIPAA has long made it clear that individuals can request their own records in paper format, information blocking rules theoretically give patients access to the same advanced Health Information Networks that care providers use to digitally exchange data.

The Office of the National Coordinator for Health IT (ONC), which sets the rules for information blocking, is working particularly hard to prevent cases where an individual is arbitrarily blocked from access to their own personal health information.

Despite ONC’s intentions, repercussions for information blocking were nonexistent. For that reason, healthcare organizations have continued to drag their feet when it comes to allowing patient data exports.

I’m here to spread the news that information blocking is coming to an end. As of October 2022, the HHS Office of the Inspector General began collecting reports of information blocking on its new online portal, with a mandate to issue fines of up to $1 million per violation. It is no longer in question that healthcare organizations will be held accountable unless they improve patients’ access to their own data.

Information blocking regulations don’t have any health IT loopholes. Providers can’t implement patient-only release delays, turn information into an unreadable format, or seek other ways to water down the rule’s provisions. Generally speaking, EHR vendors, Health Information Networks, and provider organizations must release the entire HIPAA-established designated records set of electronic health information (EHI) to a patient upon request.

In other words, if information exchange is doable for doctors, it’s doable for patients too. Information must be in the manner a patient initially requested. That could entail release over the same efficient digital channels, including APIs, that clinicians have access to.

The lack of access to clinical data at scale remains a core problem for US healthcare that can be linked to many of our system’s inefficiencies. These issues have a fundamental impact on healthcare costs, care quality, and ultimately on patient outcomes. Stakeholders cannot continue to wait for healthcare organizations to make their data-sharing preparations. Information blocking is no longer an acceptable policy, and yet we see it every day.

Ahead of the October 2022 information sharing deadline, there were concerted efforts from several healthcare organizations urging HHS to postpone enforcement, and not for the first time. However, regulators felt there was too much at stake to keep patients and digital health pioneers waiting any longer. Information blocking rules have survived multiple presidential administrations, and there’s bipartisan agreement that they are here to stay.

In reality, not every health system is at the same level of readiness when it comes to anti-information blocking compliance. Patient access to EHI has been a complicated task for many healthcare institutions, especially those that are not prepared to share patient data electronically. Many organizations are scrambling to find their own tools to support such efforts.

Fortunately, the technical challenges of anti-information blocking compliance are addressed by the 21st Century Cures Act, the law which led to information blocking rules in the first place.

The Cures Act aims to increase innovation by fostering an ecosystem that supports the development of data-driven applications. The rule also calls on the healthcare industry to adopt standardized APIs, which will help allow individuals access structured EHI using smartphone applications securely and easily. From new health tools to transferring records between providers, information blocking rules will help patients to take greater ownership of their care.

“In 2023, the vast majority of docs and hospitals will have FHIR APIs live,” Steven Posnack, the Deputy National Coordinator of ONC, predicted last month. With the ONC incentivizing this change, digital health organizations should urgently consider a single, comprehensive API that enables a seamless data sharing experience for institutions of all shapes and sizes. Integrating a pre-built API lets providers retrieve medical records in just a few clicks, eliminating reliance on faxes, portals, and other tired forms of data exchange.

For large organizations that support value-based care models, being able to work with patient health records at scale lets providers proactively address patient needs. This is especially critical when it comes to evaluating patients with chronic disease and comorbidities, leading to reduced readmissions. For example, on an ongoing basis, an API can help kidney care organizations that track glomerular filtration rate (GFR), endocrinologists who chart A1c values, and cancer care institutions that research case histories. In these instances, the ability to easily query patient health records allows healthcare providers to more efficiently and effectively care for their patient populations.

Moving ahead, digital organizations can look forward to obtaining data quickly and easily, powering tools that deliver more effective care. But it’s not just the data itself that matters; it’s how you get it. Leveraging advanced technology like a single API helps healthcare organizations and their patients tap into their health history when they need it.

Readers Write: For Safety’s Sake, Healthcare Must Address Its Patient Matching Problem

For Safety’s Sake, Healthcare Must Address Its Patient Matching Problem
By Gregg Church

Gregg Church is president of 4medica of Marina del Rey, CA.

Imagine if you log into your bank’s website to conduct a transaction, only to find that the bank can’t match your identity to your account. Not only are you unable to complete your transaction, you can’t even access your own information.

You rightfully would complain, and the bank almost certainly would move quickly to resolve the issue because, (a) it’s clearly unacceptable, and (b) the bank wants to continue benefiting from providing you with financial services.

Yet patient identification matching problems still proliferate in healthcare and are a major contributor to data integrity issues within electronic health records (EHRs). Average duplication rates among health organizations in America still range between 10% and 20%, with some duplication rates reaching as high as 30%.

At the 20% rate, this means one in five patients are at risk of having their medical records either duplicated or overlaid (when one patient’s data is placed in another patient’s medical file). It’s hard to imagine a bank or its customers tolerating a 20% error rate in customer records.

Wrong medical records accessed by providers and care team members could easily result in a wrong patient diagnosis, wrong medication prescribed, wrong lab test performed, wrong procedure or surgery conducted, and perhaps death due to a misdiagnosed condition. The problem is real, and the number of duplicate patient records is expanding as more clinical data is being moved digitally and shared across networks by hospitals, health systems, health information exchanges, labs and clinics.

What can healthcare organizations do to reduce patient identification matching problems and improve overall health data quality? Here are three things that could help healthcare organizations improve patient matching.

Standardize how data is collected at the point of care

Patient identification problems most commonly originate at registration. Busy staffers may make mistakes when entering data manually, or a lack of identifying information prompts the intake worker to create a new patient record, which can become a duplicate. Bad data can remain in an EHR for years, unbeknownst to clinicians or patients.

However, even if a healthcare organization is able to standardize data collection internally, other organizations with which they exchange data may have different processes, coding and data collection standards. The inevitable result for all stakeholders is low-quality data. Intelligent technologies such as artificial intelligence (AI), machine learning (ML) and referential matching can be used to identify and correct errors in patient data.

Patient ID to verify correct patient to medical record

Provider organizations lack a simple way to accurately identify patients. They are forced to rely on a combination of driver licenses, home addresses, Social Security numbers, phone numbers, and other non-medical identifiers. But what happens when a patient changes addresses or phone numbers? That’s when you see frustrated frontline staffers defaulting to creating a new (and duplicate) record for the patient.

A unified, single patient identifier would help resolve this problem. Unfortunately, a HIPAA proposal calling for the creation of a unique patient identifier (UPI), has been stalled by lack of funding. Until there is national patient ID, healthcare organizations must rely on technology to improve patient matching and mitigate related safety issues.

Data governance standards

Data governance is a framework for healthcare organizations to capture, process, normalize, use, store, and dispose of patient data. By consistently applying best practices to data, healthcare organizations can help ensure the accuracy of records in the EHRs and clinical systems is never comprised.

Effective data governance benefits healthcare organizations and patients in several ways. It improves the patient experience, leads to better clinical outcomes, and reduces healthcare costs through increased efficiency and better resource utilization. Finally, data governance can increase the value of a healthcare organization’s data because the governance process has improved its quality. This makes the data more attractive to prospective buyers such as pharmaceuticals and health policy researchers.

Accurately matching patients to their medical records is a daunting challenge to healthcare organizations as the volume of patient data – and the number of sources – continues to explode. Collecting and organizing patient data in a more standardized way will enable providers, labs, and other stakeholders to better serve patients while lowering costs and increasing the value of their data.