The rising out-of-pocket costs from health insurance is one of the most common barriers to health care for patients. According to a recent study, 46 million people cannot afford needed care. With significant increases in job loss due to COVID-19, many people have become uninsured and are deferring care, which consequently places financial burdens on healthcare systems.
Further, many patients who are uninsured or underinsured don’t know there are financial resources available that could help lower their out-of-pocket costs. Providers are in the unique position to adopt strategies to help remove barriers to treatment using technology, particularly for those struggling to afford care. These can lead to better financial outcomes for both the patient and provider.
Here are four ways technology is helping providers remove financial barriers to care:
Predictive analytics. Healthcare organizations can leverage predictive analytics to proactively identify patients at risk of not affording treatment – and mitigate the financial and personal stress that comes with receiving a costly medical bill post treatment. Providers can analyze patient data including income, propensity to pay, health insurance out-of-pocket cost, and treatment plan to assess financial risk. It can also help prioritize which patients have the highest probability of not affording high-cost care. This level of visibility can help providers identify more patients upstream needing financial care and take the next steps toward reducing the financial burden.
By Kim Huynh and Esther Ketelaars, health and life sciences experts, PA Consulting.
Studies unanimously show the negative effects medication non-adherence has on clinical outcomes and healthcare costs. Adherence is critical to ensure that medications work properly and important for pharmaceutical companies because it helps keep their drugs covered under health plans. Healthcare insurers want to ensure their covered drugs are treating their members effectively and are seeking to prevent more costly health care.
So, while traditionally medical adherence solutions have been paid for and promoted by pharma and payors, a new player has a chance to enter the scene and challenge the status quo. Now is the time for providers to proactively address medication adherence as they start to bear more financial risk through value-based care models.
How can providers play a larger role?
Provider organizations who focus on medication nonadherence have a great opportunity to improve patient outcomes and support their value-based care models. According to the Center for Disease Control medication nonadherence results in 10% of hospitalizations and 125,000 preventable deaths in the U.S. each year. Likewise, low medication adherence leads to treatment failures between 30% and 50% of the time.1 These negative impacts become even more relevant to providers as patient outcomes continue to be more closely tied to reimbursement and payment incentives.
Pharmaceutical companies, payors, some providers, other consumer-focused companies in the healthcare value chain, and even governments have tried to address nonadherence. With the recent growth of digital health solutions especially, many have focused on leveraging novel technologies. However, there are a myriad of reasons for patients not compliant with their medication and adopting a single tool or technology has rarely been effective in reducing nonadherence.
A one-size-fits-all approach will often only address a single issue for a limited number of patients. With their direct access to patients, providers have a better chance of addressing the complex mix of reasons for nonadherence and design medication adherence programs customized to each patient. Personalized intervention plans invite a direct solution to each patient’s reasons for nonadherence with the appropriate tools that address the underlying cause for that individual.
Developing such a program requires factoring in root causes and reasons for nonadherence, using predictive analytics to identify high-risk patients, and gathering a diverse set of interventions to address those root causes. The position and shared decision-making power between providers and consumers indicate that providers can address medication adherence for more patients, improve patient outcomes, and reinforce value-based care.
Designing a patient-centered medication adherence program
Many of the tried and tested programs have been designed based on an assumption of the underlying reason for nonadherence. However, for these programs to be truly effective provider organizations need to understand and diagnose the patient’s reasons for nonadherence and tailor their intervention with the right tools.
Understand the reasons for medication nonadherence
Exploring the factors contributing to nonadherence allows organizations to understand the inherent complexity of nonadherence. Most interventions fail to produce the desired results because they don’t consider the many contributing factors. The reasons for nonadherence go beyond simple forgetfulness. Only 30% of patients cite forgetfulness as the cause of their nonadherence.
Now more than ever, it is critical for information across the care continuum to be accurate. Data plays a crucial role in patient care, from electronic health/medical records (EHR/EMR) to predictive analytics. Digitization has been widely adopted and successful in improving workflow efficiency, enhancing the accuracy of communications, mitigating risk and improving safety for the benefit of patients.
There has been one communication tool that’s remained a final, manual holdout from a bygone era: patient room information displays. Communicating key information such as a patient’s diagnosis, allergies and DNR info, these signs are often printed on paper or handwritten on whiteboards.
What is the solution? Hospitals need to bring digital signage into the 21st century.
Why digitize information room displays?
• Handwritten = prone to human error. According to The Joint Commission, communication errors with patients or administrators are cited as one of the top three core factors underlying hospital sentinel events. In addition, anyone in a rush is more likely to input the wrong information, or scribble something that can easily be mistaken by another caregiver.
• Double the work. The information nurses are inputting onto these signs is exactly what’s already in their database. Having to access the data and re-input it manually amounts to grunt work that distracts nurses from what they do best, which is caring for patients. Over any given twenty-four hour period, manually re-inputting information can amount to forty-five minutes to an hour of precious time — potentially more if the patient’s condition is complex.
• Instant Translation. Another advantage of digital signage is that it’s multilingual at the touch of a button. The latest census indicates that twenty percent of Americans speak a language other than English at home. With digital signage, patient information can cross the language barrier at the push of a button.
At Landmark Hospitals, we looked to digital paper, a new Hospital of the Future solution allowing hospitals 21st century digital signage. Known by most people as the screen in their eReader, digital paper mimics the look of printed paper and provides the advantages of digital media. Made by E Ink, digital paper has particles within microcapsules coated onto a thin film layer, which act as a form of ink that can be digitally updated.
Why hospital of the future digital paper?
• Versatility. E Ink screens are now available in a range of formats, from medical admission forms, to patient door signs, to bedhead and bedside patient care signs, to large-format patient communication boards.
• Connected. E Ink-enabled signage is linked to hospital databases, allowing for automatic information updates without the need for manual updates by time-pressed nursing staff. Each individual sign integrates with the system as a whole, so information on the patient care signs, door signs, and patient communication boards is always up-to-date and consistent.
• Sustainable. Digital paper screens require 99% less power to operate than LCD screens. Most hospital signs made with digital paper require only small batteries to operate, making them easy to deploy. The low battery usage allows for continuous use in patient environments, all day and night, with the comforting look and feel of paper and no light pollution.
• Non-light-emitting. Some companies have introduced digitized whiteboards, with EHR/EMR data automatically updated onto a TV screen in patient rooms. The light pollution from these screens, however, is a significant concern. The bright lights of LCD screens can disrupt a person’s circadian rhythm, essentially resetting their personal clock to disrupt sleep patterns.
There is tremendous pressure on health systems to serve more patients, particularly as the need for services rises and insurance disbursements decrease. Adding new facilities to accommodate demand is often not a feasible option due to budgetary/capital constraints nor is adding more doctors with nowhere to put them. As a result, healthcare leaders consistently ask whether they are getting all that they can out of existing resources.
Operating rooms are a prime example. They serve as the economic backbone for health systems, and organizations need to maximize the use of OR capacity if they want to achieve their fiscal and patient access goals. Yet effectively managing OR blocks and scheduling in the face of volatile weekly demand patterns can feel like trying to squeeze blood from a stone.
If a significant portion of OR time has been reserved as dedicated blocks for surgeons or service lines, unless time not needed is released efficiently, ORs end up both not being used during business hours and yet working late into the night. Patients wait longer than necessary for scheduling procedures and organizations lose revenue.
Additionally, poor OR utilization makes it extremely difficult to accommodate the new surgeons that health systems want to attract. Current providers have a lock on ORs far in advance, regardless of whether they may need the time or not and/or later release these blocks. Such practices leave few opportunities for new surgeons to secure time when needed.
To remedy the situation, health systems are turning to data to look for insights into what can be done to improve OR utilization. After all, small changes in utilization translate to big differences in patient access, revenue and profitability.
The Role of EHRs
As data insights move to the forefront in organizational decision-making, there is some confusion about what EHR systems do and the role that they play. In a nutshell, EHRs tell you what’s already happened. They are vital systems, absolutely necessary for describing problems within organizations and supplying the data to back up assessments.
Breaking this down further, the purpose of implementing an EHR is threefold:
Reservation system: EHRs provide health systems a way to reserve resources, whether it be rooms, infusion chairs, or clinic time. EHRs do this as well as enable scheduling of specific equipment or providers.
Single source of truth for patient encounters: Organizations need a way to maintain records for every patient encounter — a single instance for each enterprise deployment — and EHRs provide this.
Descriptive reporting: Health systems require reporting. EHRs generate reports based on what was done, sort of like how Charles Schwab tells you how your portfolio performed.
While these functions are all essential to running a successful health system, there are several things EHRs are NOT designed to do no matter how much teams may wish they could. For example:
By Brooke Faulkner, freelance writer; @faulknercreek.
Up to a fifth of patients with serious conditions are first misdiagnosed, and that leaves tremendous consequences. With the help of healthcare technology, doctors are able to diagnosis patients more effectively and easier. For example, migrating patient data from paper to online, known as electronic health records (EHRs), has greatly aided the medical world. Technology, especially using artificial intelligence and predictive analytics, has enabled doctors to make faster, more accurate diagnoses, and thus provide better care.
The volume of big data
Duquesne University estimated there to be 150 exabytes of healthcare data collected in 2011. Four years later, they reported about 83 percent of doctors had transitioned from using paper to electronic records. By now, with the ubiquity of the cloud, these numbers have assuredly gone up.
Massive amounts of data make predictive analytics possible, as trends can be spotted and analyzed. By spotting patterns, diagnosis of a disease becomes easier even for doctors unfamiliar with a specific disease or symptom. Uploading symptoms allows a computer to compare records and identify symptoms comorbid of other problems. This allows even specialized doctors to recognize issues outside of their field. Medical mistakes lead to the death of some 440,000 people each year; while misdiagnosis is only a part of this number, correct diagnosis and treatment will reduce it.
Big data can even be collected in the form of PDFs as part of telemedicine. A doctor can send PDFs to patients as part of a poll or survey or simply to collect symptom information from the patient. From there, data entered in the PDF can be collected and analyzed, generating patient data or feedback for the doctor.
Google flu trends
Google ran what can best be called an experiment from 2008 to 2014. Using artificial intelligence, the search engine recorded flu-related searches in an attempt to predict the severity of an outbreak, as well as the affected geographical area.
It was a flawed model, and tried to use big data as a replacement, rather than a supplement, for traditional data collection and analysis. It completely missed a flu outbreak in 2013, the data off by a massive 140 percent, and Google Flu Trends ended its public version in 2014. The algorithm monitoring flu-related search terms was simply not sophisticated enough to provide accurate results. While new data is no longer available to the public, historical data remains available to the Centers for Disease Control and other research groups. It’s possible that once the algorithm and predictive analysis is capable, the program will continue.
With digital information flowing from countless sources, including electronic health records (EHRs), wearable devices and digital maps that monitor global disease outbreaks, the healthcare industry is taking a big data approach to improving patient outcomes and enhancing the daily lives of countless others.
Yet one large part of the healthcare ecosystem isn’t efficiently capitalizing on the vast amount of data that is right at their fingertips: hospital operations. Many leaders and line managers are unable to take advantage of the readily available data sets from the billions of dollars invested in IT systems that would allow them to improve operational efficiency and provide an exceptional level of care. Instead, they rely largely on spreadsheets and back-of-the-envelope math along with first-hand experience to make critical daily operational decisions, such as scheduling operating rooms and reducing emergency department boarding.
Some hospitals have recognized that driving growth starts with superior planning and optimization. These forward-thinking facilities are leveraging their data using a hospital operations management software platform to revolutionize the operational and financial performance of various parts of their organization including ED, inpatient, perioperative and clinics.
The result? Higher resource utilization, better quality of care, satisfied employees and increased revenue.
Tap existing data resources to create growth
The OR suite’s multifaceted nature makes it extremely difficult to optimize for overall efficiency. As a result, millions of dollars are wasted each year. Still, it’s a primary financial driver for most hospitals and presents one of the largest opportunities for increasing profit margins through operational improvements.
In efforts to improve services while reducing expenses, perioperative leaders have been dependent on consultants, manual spreadsheets, and trial-and-error experimentation, leading to results that are often inaccurate, time-consuming, and have significant lag time to understand impact. These traditional methods should be abandoned.
Hospitals must embrace new analytics software platforms to deliver a practical application of analytics to the business of healthcare.
Predictive analytics utilizes the data gathered from existing EHRs, bed management, case management systems and external sources (such as weather) to quickly and easily see the potential impact of scheduling, staffing, and case mix changes. It empowers hospital leaders to develop optimized block and OR schedules that are easily managed and automates the staff planning and assignment process, all the while complementing traditional time and attendance systems.
At a time when hospitals cannot afford to mismanage valuable resources, analytics prevent costly trial and error and help hospitals overcome operational shortcomings in their perioperative suite. By testing “what if” scenarios, facilities can predict and manage the impact of operational changes for little expense or exposure.
Analytics software programs demonstrate measureable impact in OR operations. Impressively, analytics enabled a nonprofit medical center in Boston to increase its annual OR volume by three percent and improve utilization by more than five percent, resulting in an annual revenue increase of more than $3 million. And in New Jersey, a regional medical center used analytics to improve its OR utilization to 71 percent and improve labor productivity by 10 percent.
It’s impossible to see the future with certainty, but one branch of technology is playing a leading role in helping institutions and industries predict, on the basis of empirical research, the future behavior of participants and the outcomes of their decisions.
This relatively new branch of tech – predictive analytics (or PA) – has made inroads at a steady clip in the marketing, manufacturing and financial services industries. It is now gaining traction in healthcare as well.
Although debates around its ethical applicability to healthcare persist – the debate around data privacy, for one – the consensus emerging across the board is that with the right skills and in the right hands, PA has the power to effectively address challenges in the healthcare ecosystem in ways that human intelligence alone cannot.
Let us examine a few recent examples.
The power of PA
The Gold Coast Health Hospital in Southport, Queensland, Australia, dramatically improved patient outcomes and hospital staff productivity by applying a predictive model that was able to project with 93 percent accuracy emergency admissions before they happened. By analyzing admission records and details of sundry circumstances that led to patient admission to the ER, hospital staff were able to know how many patients would be coming in, on any day of the year, what they would be coming in for and methodically plan procedures that were now for all purposes elective rather than urgent.
Similarly, the El Camino hospital in California was able to drive a dramatic turn-around in its high rate of patient falls by collaborating with a tech company. The company, Qventus, linked patient EHR to bed alarm and nurse call light usage to derive an algorithm that was able to alert nurses in real time about the high-risk patients under their care and the exact times when they were most likely to be vulnerable. The result was a whopping 39 percent reduction in falls, improvement in patient health outcomes and a dramatically improved reputation for the hospital.
In fact, it isn’t only hospitals that are alive to the potential of analytics. Tech companies too are cognizant of how some of the newest technologies being developed under their roofs have immediate relevance to healthcare outcomes. In a paper published earlier this year, researchers associated with Google demonstrated how deep learning algorithms were able to correctly identify metastasized cancer tissue with nearly 90 percent accuracy as compared to just 73 percent when done by a human pathologist.
Predictive analytics and advanced labor management are the most important – and underutilized – methods to assure that provider organizations have the right caregivers in the right places at the right times.
A recent survey of nurse managers by AMN Healthcare and Avantas, Predictive Analytics in Healthcare 2016: Optimizing Nurse Staffing in an Era of Workforce Shortages, (available on the AMN website) brought the need for more awareness to light in just a few stats related to staffing and scheduling:
94 percent of nurse managers say that understaffing hurts staff morale
Nearly 70 percent say they are very concerned about the impact on patient satisfaction
More than 50 percent say they are very concerned about the effect on quality of care
The survey also revealed a lack of sophisticated scheduling tools being utilized:
24 percent use paper-based scheduling and staffing tools
19 percent use simple digital spreadsheets
23 percent don’t use any scheduling tools at all
Further, the survey found that while nearly 90 percent of nurse managers said that a technology that can accurately forecast patient demand and staffing needs would be helpful, 80 percent were unaware that such a solution exists.
Strategies to Fulfill the Potential Predictive Analytics
This process to predict future patient demand and strategically plan clinician scheduling and staffing is scalable, cost effective and accurate. First, staffing data are processed with advanced algorithms, then forecasting models are created and validated, customized for each unit or service area within the organization, allowing workforce projections up to 120 days prior to the shift. The forecast is updated weekly, and by 30 days in advance of the shift, the forecast of staffing need is 97 percent accurate.
Compared to how scheduling and staffing is conducted at most healthcare organizations today, predictive analytics may seem like something out of a sci-fi movie. The truth is, this sophisticated forecasting of labor needs has been leveraged in other industries with great success. And, in healthcare, it can lay the foundation for significant advancement in utilization of staff, leading to improvements in morale, quality, and financial results. The advanced labor management strategies and tools layered on an accurate projection of staffing needs – months and weeks in advance of the shift – will turn an accurate forecast into an effective resource management strategy.
Adopting Workforce Analytics Every organization’s staffing mix should be unique to the fluctuations in its patient volume. Once an organization understands its demand, it can then determine its supply – scheduling and staffing to meet patient demand in the most productive manner possible. The organization can analyze and solve the problems that reduce its available supply of core staff, such as leaves of absence, continuing education, training and other issues. This precision understanding of workforce availability is then layered with patient volume predictions, and the result is accurate insight into the core and contingency staffing levels needed to meet patient demand.