Health Technology
Article | September 12, 2023
With consumers’ share of healthcare costs expanding, we need to do a better job making charges more transparent and more predictable
My husband recently stubbed his toe. Badly. Badly enough that I encouraged him to go see a doctor. He was reluctant. While I suspected he’d rather just garner sympathy by complaining to me while limping around the house (just kidding, honey), his stated reason was all too familiar: “I have no idea what we’ll have to pay. They’ll want to do anX-ray,it might need surgery, and I have no idea what it’ll cost.” All true. We have good health insurance; but like most Americans, when we go to the doctor or have a procedure, what we will actuallyhave topay out of pocket remains a mystery.This is something that we can and should change.
As consumers we are shouldering more and more of the cost of healthcare. And the biggest increases are for those of us with employer-sponsored plans. According to an analysis of federal data by the Commonwealth Fund,deductibles in employer plans more than doubled between 2008 and 2017, from $869 to $1,808. Especially troubling, an accompanyingCommonwealth Fund survey revealed that only 62% of adults were very or somewhat confident in their ability to afford healthcare.
This increasing burden is also evident when you look at the crushing levels of medical debt in the United States. According to a new studyby JAMA, medical debt is now the largest contributor to personal debt. And the data for this study was collectedbeforethe COVID-19 pandemic.
Some of this debt is driven by unpredictability—if the heart procedure you needed costs several thousand dollars more out of pocketthan you thought it would, you may not be prepared, emotionally or financially, to pay it. This is a bad outcome, obviously. The risk of nonpayment rises for the provider; and a recuperating patient is burdened with the stress of a large,unexpected bill.
More skin in the game
Soas consumers are paying more out of pocket, some may become reluctant to seek care (like my husband) or seek more information about what they willhaveto pay for the care they receive. Consumers are also armed with incredible levels of price transparency with other products—everything from hotel rooms to clothing to household items. With so much skin in the game, and the internet providing so much information, consumers’ expectations are changing when it comes to healthcare.
State and federal regulators are also beginning to take action, a trend that will likely accelerate. Most hospitals are now required to publicly disclose the prices they charge. This does not, however, solve the issue for consumers. While it provides a measure of visibility into pricing for insurance companies, Medicare, and Medicaid, it doesn’t show what share the patient will ultimately pay.
Making the complicated simple
The complexity of pricing in healthcare is well documented. Niall Brennan, CEO of the Health Care Cost Institute, a nonprofit that analyzes medical costs, suggests that healthcare costs are too high.As a recent Wall Street Journal article reported, a price of a C-section varied from $6,241 to$60,584 at one hospital. This all has to do with the vagaries of the agreements that hospitals sign with multiple insurance companies and government payers. In turn, each insurance company will have its own deductible and out-of-pocket schedules, which providers don’t have access to.
We are seeking to change this at Change Healthcare. We are piloting our Care Cost Estimator with a few innovative providers. With the Care Cost Estimator, weleverage our unique dataset, and the largest eligibility network in the industry, to make the unpredictable, predictable.Because we’ve managed 15 billion healthcare transactions—and our network covers 1 million physicians, 6,000 hospitals, and 2,400 payers—we have an unmatched ability to analyze what real-world patients are paying for practically any procedure, performed at almost any hospital or clinic.
With a cloud-based transaction engine, providers will now be able to tell their patients how much they will have to pay out of pocket for a given procedure.And this analysis takes place in real time.
Removing unpredictability in pricing
This gives providers the opportunity to offer added value for their patients, taking some unpredictability out of whatis often a stressful transaction. In addition, it accelerates patient payment cycles which, as the patient’s share of the cost burden increases, is becoming more and more important. We’re not talking $50 co-pays anymore; it’s thousands of dollars per transaction. If necessary, providers can also help the patient plan for the expense, offering financing options, thus reducing unpaid bills. For the patient, it allows more informed decision-making and peace of mind.
Testing the beta version of our Care Cost Estimator with our partners will allow us to receive real-world feedback and collaborate with customers on how to continuously improve the product as we scale it. We expect the ROI for providers, in addition to the payment-cycle improvements, will include greater patient satisfaction and loyalty. For the patient, it provides information necessary to help make proper decisions and plan emotionally and financially; in other words, giving the consumer the same information for vital healthcare transactions that’s available to them for practically any other purchase.
This kind of win-win solution is at the core of Change Healthcare’s mission to improve the healthcare experience for everyone—including my husband and his broken toe!
Read More
Healthtech Security
Article | November 29, 2023
The healthcare industry has become a prime target for cybercriminals in recent times.
According to The State of Ransomware in Healthcare 2023 report from Sophos, six in 10 healthcare organizations have been hit by ransomware in the last 12 months, up from 34% in 2021.
Among this uptick have been several headline-grabbing attacks. For example, Shields Health Care Group became the subject of the single-largest breach affecting any organization globally in April 2023, when 2.3 million patients of the Massachusetts-based medical services provider had their personal data stolen after a cybercriminal gained unauthorized access to the organization’s systems.
Meanwhile, in the UK, a ransomware attack on the University of Manchester occurred in June, affecting an NHS patient data set holding information on 1.1 million patients across 200 hospitals.
Critically, the wealth of data housed in healthcare networks, and the potential impact of data unavailability in healthcare, make the industry both attractive and lucrative to threat actors.
It’s no coincidence that the Sophos report shows the rate of encryption in the healthcare sector is at its highest level in recent years. Of those healthcare organizations which suffered a ransomware attack in 2023, 73% had their data encrypted – up from 61% in 2022. When cybercriminals can successfully take down hospital systems and/or encrypt patient data so it can’t be used, they can blackmail health service providers, demanding significant sums before reinstating systems and/or data availability.
Considering healthcare's critical role as the highest-stake industry in our society, where people's lives depend on its success, the likelihood of attackers achieving their goals is greater than in other sectors, as confirmed by the Sophos report. Indeed, of the 73% of healthcare organizations that had their data encrypted, 42% reported that they paid the requested ransom to recover data.
DSPT and the compliance burden
Without question, the security-related challenges in healthcare are mounting. Right now, industry organizations are operating against a backdrop of unprecedented operational and workforce pressures, spiralling demand for care and industrial action.
Moreover, there is a growing regulatory burden, with organizations continually asked to comply with evolving cybersecurity rules, battling with multiple compliance mandates at any given time.
Take the NHS as an example. According to the 2023 NHS Providers’ Regulation Survey, just over half (52%) of respondents said the regulatory burden on their trust had increased. And this is expected to ramp up further in the future, with the UK government setting out a new 2030 strategy aimed at bolstering cyber resilience in healthcare.
Among the compliance burdens that the NHS faces is the challenge of meeting the requirements of the newly updated Data Security and Protection Toolkit (DSPT).
Mandated to minimize cyber risks and enable healthcare providers to maintain a robust information security posture, the DSPT is not a simple checklist of security controls, but a comprehensive toolkit to evaluate current security maturity and establish a risk management programme.
Indeed, in more recent times, DSPT has moved away from being a guide for achieving certain levels of assurance, and toward a mandatory evidence-based system which demands NHS organizations align with 10 precise National Data Guardian (NDG) standards: 1. The organization assures good management and maintenance of identity and access control for its networks and information systems. 2. The organization closely manages privileged user access to networks and information systems supporting essential services. 3. The organization ensures passwords are suitable for the information being protected. 4. Process reviews are held at least once a year where data security is put at risk and following security incidents. 5. Action is taken to address problems as a result of feedback at meetings. 6. All user devices are subject to anti-virus protections, while email services benefit from spam filtering and protection deployed at the corporate gateway. 7. Action is taken on known vulnerabilities based on advice from NHS Digital, and lessons are learned from previous incidents and near misses. 8. The organization has a defined, planned and communicated response to data security incidents impacting sensitive information or key operational services. 9. The organization has demonstrable confidence in the effectiveness of the security of technology, people, and processes relevant to essential services. 10. The organization securely configures the network and information systems that support the delivery of essential services.
Reducing Compliance Challenges with the Right Solutions
Taken individually, these standards may not seem too strenuous to adhere to. However, to be compliant with DSPT, all 10 items need to be completed and deemed ‘satisfactory’.
To tick all 10 key boxes in the most effective and efficient manner simultaneously, organizations should consider their strategy carefully. This could involve embracing supportive tools to accelerate and enhance their compliance journey.
Boiled down, DSPT demands several key things, including unincumbered visibility of the entire ecosystem, as well as the ability to demonstrate secure access, logs and storage, and essential auditing processes to maintain data security.
Achieving these things might appear complicated, even daunting. However, there are solutions known as Security Information and Event Management (SIEM) systems on the market that can make achieving these capabilities, and in turn DSPT compliance, easy.
Here, we outline some of the key features to look out for to meet compliance: • Log retention: A modern SIEM should be able to provide a centralized log storage and big data platform that scales to any organization’s size. Platforms should be able to provide role-based access to log data, including ‘data privacy’ functionality that can mask sensitive data until approved. Log data should not be modified or removed by users once ingested into the platform, while all data held should also be indexed and fully searchable. • Identifying and disabling unnecessary accounts: A good SIEM will also provide account auditing facilities for Active Directory that allow administrators to quickly identify dormant accounts. They should also be able to remove privileged user access when no longer required or appropriate. More sophisticated platforms will be able to do this in an automated manner. • Easy identification of issues: Clear and easily readable dashboards, alerts and reports for user logging activity should be provided, including failed login, apparent brute-force attempts, and bad password management practices. Further, those using machine learning will be able to identify unusual behavior patterns based on a baseline of activities of users and their peer group. • Integrate with third-party threat feeds: It will also be able to integrate with a wide variety of third-party threat feeds that provide information about specific known threat payloads/hashes and destination domains/addresses.
Meeting the mandate
Of course, having the right features in place is only part of the puzzle. For organizations to be truly successful in embracing tools that enable them to meet DSPT compliance more effectively, they should work to ensure that solutions providers offer them ongoing support – both in terms of ease of deployment and to ensure that they are using key systems in an optimal manner.
Scalability is another important aspect to consider.
Systems should be able to scale and continue to support the organization as data volumes increase and become more complex over time.
In respect of scalability, organizations should take time to think about pricing models, ensuring that these are based on the number of devices (nodes). In doing so, it will become easier to accurately budget future costs, as well as provide greater budgeting certainty over the short, medium and longer term.
A converged SIEM allows organizations to prioritize the big picture over individual tools, enabling them to develop a seamless and easy to use security operations setup. Not only does this approach boost cost transparency and eliminate potential complexities with managing a variety of siloed products – equally, it reduces the burdens on security teams, eliminating complexities over system integration and enhancing performance.
A converged SIEM combines key technologies easily to offer improved security outcomes. In doing so, organizations can easily home in on specific standards and adopt security best practices while reducing the burden on security teams tasked with meeting DSPT compliance.
Read More
Health Technology, Digital Healthcare
Article | September 8, 2023
Dialysis providers face many of the same financial and operational pressures that affect other provider organizations, including flat or reduced reimbursements, chronic staffing shortages, and increasingly complex insurance requirements. Dialysis centers, nephrologists, and renal pharmacies also grapple with the impact of a growing shift in dialysis care to the home setting.
End-to-End Automation Can Reduce Denials, Improve Cash Flow
The good news is that despite these challenges, dialysis providers can sustain strong cash flow, reduce costs, and mitigate denials by applying advanced technology to the revenue cycle.
Here are six ways technology can help strengthen the dialysis center revenue cycle in the today’s difficult operating environment:
Identify undisclosed insurance coverage
Because patients often present as self-pay even though coverage exists, determining their true insurance status can be challenging. Yet failure to identify existing insurance can result in significant write-offs.
That’s why renal providers need technology solutions that can uncover patient coverage information before care is provided. Change Healthcare’s Coverage InsightTMsolution provides an expansive network and search-and-matching capabilities necessary to identify and confirm patient coverages at the outset of care.
The solution uses machine learning algorithms—coupled with access to vast stores of available third-party-data—to develop robust patient profiles, which can then be linked to potential funding sources. Notably, it identifies a variety of indicators, including high probability of disability, income levels and financial status, insurance sources, and other actionable information to help you verify coverage and recover revenue.
We can help identify undisclosed coverage for end-stage renal disease (ESRD) patients through Medicare/Medicaid, Disability/SSI, third-party liability, commercial insurance, state and county programs, social programs, and charity.
Expedite seamless prior authorizations
Streamlining the prior authorization process is essential to help ensure optimal reimbursement for renal care rendered, particularly with commercial insurance and Medicare. But traditional prior authorization processes are frequently time-consuming and labor-intensive and can delay necessary care.
Our Clearance Authorization software addresses the chronic problem of prior authorizations with automated functionality that can determine if prior authorization is required and on file with the payer. The solution also will automatically check medical necessity requirements at the time of registration and electronically submit requests to integrated payers.
Change Healthcare’s Connected Authorization Services go a step further by deploying pre-authorization experts to handle routine authorizations quickly using intelligent technology while working complex cases by exception to improve authorization efficiency and accuracy.
Speed adjudication with electronic attachments
As claims management processes have grown more numerous and complex, providers have struggled to ensure that the correct information is provided to the payer at the appropriate time. The result can be delayed, denied, or rejected claims.
Assurance Attach AssistTMcontributes to faster reimbursement and reductions in denials, organizational expense, and administrative burden by automating the attachments process to meet payers’ increased demands for additional documentation. Attachments are automatically delivered and matched to the appropriate claim, and once the claim is released, claim and attachment status can be easily tracked.
Expedite claims workflow for recurring services
Creating claims for ongoing ESRD care requires repeatedly documenting the same details on each claim. Revenue Performance Advisor, an end-to-end medical billing platform, provides automation that allows dialysis staff to save time by quickly replicating unchanged data from prior visits while updating date-of-service and other information to expedite claims processing.
Revenue Performance Advisor also includes eligibility and benefits verification and automated claims scrubbing that flags incomplete or incorrect claims prior to submission, resulting in a first-pass clean claim rate of 98%.
Accelerate your Medicare claim cash flow
Medicare is one of the largest payers of dialysis services, so ensuring a problem-free and expedited Medicare claims submission process is essential to strong cash flow.
Our Assurance Medicare Direct EntryTMsolution provides a single system for the real-time submission and processing of Medicare claims. It can help expedite reimbursement, reduce AR days, and speed your Medicare primary claim cash flow by at least one full business day.
Assurance Medicare Direct Entry also checks your Medicare claims for eligibility errors using the CMS eligibility transaction system (HETS). Claims needing attention are flagged and posted in Assurance Reimbursement Management for editing. You can quickly correct errors within the system before transmitting the claim directly to Medicare for validation and payment processing.
Optimize patient liability
Making it easy for patients to receive, understand, and pay their portion of the medical bill is key to ensuring a healthy revenue cycle, mitigating the need for collection services, and improving patient goodwill.
With our Patient Billing and Statements solution, Change Healthcare serves as your strategic communications partner, delivering multi-channel, personalized print and digital statements to help expedite patient payment collection.
The solution is designed to provide fast, effective statement and invoice processing, printing, and mailing—cutting your costs and getting you paid sooner. Our advanced statement printing allows you to bypass conventional and time-consuming folding, stuffing, and stamping.
SmartPayTMconsolidates each step of the billing and payment process into one place, enabling you to collect more patient payments, get paid faster, reduce your collection costs, and lower patient write-offs. With multiple payment channels, including online, mobile, telephone and via mail, SmartPay helps expedite patient payments before, during, and after the encounter.
A single, trusted partner
Change Healthcare’s deep knowledge of the renal care landscape and our development of disruptive technologies to overcome traditional revenue cycle barriers can help dialysis centers achieve unprecedented revenue cycle excellence.
And unlike many point solutions that only address a specific revenue cycle issue, Change Healthcare’s technologies are part of a comprehensive approach delivered through a single, trusted vendor. That translates into improved process integration and continuity, as well as simpler overall accountability.
Read More
Article | September 4, 2020
A digital twin is a digital representation of a real-world entity or system. The implementation of a digital twin is a model that mirrors a unique physical object, process, organization, person or other abstraction. For healthcare providers, digital twins provide an abstraction of the healthcare ecosystem’s component characteristics and behaviors. These are used in combination with other real-time health system (RTHS) capabilities to provide real-time monitoring, process simulation for efficiency improvements, population health and long-term, cross-functional statistical analyses.
Digital twins have the potential to transform and accelerate decision making, reduce clinical risk, improve operational efficiencies and lower cost of care, resulting in better competitive advantage for HDOs. However, digital twins will only be as valuable as the quality of the data utilized to create them. The digital twin of a real-world entity is a method to create relevance for descriptive data about its modeled entity. How that digital twin is built and used can lead to better-informed care pathways and organizational decisions, but it can also lead clinicians and executives down a path of frustration if they get the source data wrong. The underlying systems that gather and process data are key to the success for digital twin creation. Get those systems right and digital twins can accelerate care delivery and operational efficiencies.
Twins in Healthcare Delivery
The fact is that HDOs have been using digital twins for years. Although rudimentary in function, digital representations of patients, workflow processes and hospital operations have already been applied by caregivers and administrators across the HDO. For example, a physician uses a digital medical record to develop a treatment plan for a patient. The information in the medical record (a rudimentary digital twin) along with the physician’s experience, training and education combine to provide a diagnostic or treatment plan. Any gaps in information must be compensated through additional data gathering, trial-and-error treatments, intuitive leaps informed through experience or simply guessing. The CIO’s task now is to remove as many of those gaps as possible using available technology to give the physician the greatest opportunity to return their patients to wellness in the most efficient possible manner.
Today, one way to close those gaps is to create the technology-based mechanisms to collect accurate data for the various decision contexts within the HDO. These contexts are numerous and include decisioning perspectives for every functional unit within the enterprise. The more accurate the data collected on a specific topic, the higher the value of the downstream digital twin to each decision maker (see Figure 1).
Figure 1: Digital Twins Are Only as Good as Their Data Source
HDO CIOs and other leaders that base decisions on poor-quality digital twins increase organizational risk and potential patient care risk. Alternatively, high-quality digital twins will accelerate digital business and patient care effectiveness by providing decision makers the best information in the correct context, in the right moment and at the right place — hallmarks of the RTHS.
Benefits and Uses
Digital Twin Types in Healthcare Delivery
Current practices for digital twins take two basic forms: discrete digital twins and composite digital twins. Discrete digital twins are the type that most people think about when approaching the topic. These digital twins are one-dimensional, created from a single set or source of data. An MRI study of a lung, for example, is used to create a digital representation of a patient that can be used by trained analytics processes to detect the subtle image variations that indicate a cancerous tumor. The model of the patient’s lung is a discrete digital twin. There are numerous other examples of discrete digital twins across healthcare delivery, each example tied to data collection technologies for specific clinical diagnostic purposes. Some of these data sources include vitals monitors, imaging technologies for specific conditions, sensors for electroencephalography (EEG) and electrocardiogram (ECG). All these technologies deliver discrete data describing one (or very few) aspects of a patient’s condition.
Situational awareness is at the heart of HDO digital twins. They are the culmination of information gathered from IoT and other sources to create an informed, accurate digital model of the real-world healthcare organization. Situational awareness is the engine behind various “hospital of the future,” “digital hospital” and “smart patient room” initiatives. It is at the core of the RTHS.
Digital twins, when applied through the RTHS, positively impact these organizational areas (with associated technology examples — the technologies all use one or more types of digital twins to fulfill their capability):
Care delivery:
Clinical communication and collaboration
Next-generation nurse call
Alarms and notifications
Crisis/emergency management
Patient engagement:
Experiential wayfinding
Integrated patient room
Risks
Digital Twin Usability
Digital twin risk is tied directly to usability. Digital twin usability is another way of looking at the issue created by poor data quality or low data point counts used to create the twins. Decision making is a process that is reliant on inputs from relevant information sources combined with education, experience, risk assessment, defined requirements, criteria and opportunities to reach a plausible conclusion. There is a boundary or threshold that must be reached for each of these inputs before a person or system can derive a decision. When digital twins are used for one or many of these sources, the ability to cross these decision thresholds to create reasonable and actionable conclusions is tied to the accuracy of the twins (see Figure 2).
Figure 2: Digital Twin Usability Thresholds
For example, the amount of information about a patient room required to decide if the space is too hot or cold is low (due to a single temperature reading from a wall-mounted thermostat). In addition, the accuracy or quality of that data can be low (that is, a few degrees off) and still be effective for deciding to raise or lower the room temperature. To decide if the chiller on the roof of that patient wing needs to be replaced, the decision maker needs much more information. That data may represent all thermostat readings in the wing over a long period of time with some level of verification on temperature accuracy. The data may also include energy load information over the same period consumed by the associated chiller.
If viewed in terms of a digital twin, the complexity level and accuracy level of the source data must pass an accuracy threshold that allows users to form accurate decisions. There are multiple thresholds for each digital twin — based on twin quality — whether that twin is a patient, a revenue cycle workflow or hospital wing. These thresholds create a limit of decision impact; the lower the twin quality the less important the available decision for the real-world entity the twin represents.
Trusting Digital Twins for HDOs
The concept of a limit of detail required to make certain decisions raises certain questions. First, “how does a decision maker know they have enough detail in their digital twin to take action based on what the model is describing about its real-world counterpart?” The answer lies in measurement and monitoring of specific aspects of a digital twin, whether it be a discrete twin, composite twin or organization twin.
Users must understand the inputs required for decisions and where twins will provide one or more of the components of that input. They need to examine the required decision criteria in order to reach the appropriate level of expected outcome from the decision itself. These feed into the measurements that users will have to monitor for each twin. These criteria will be unique to each twin. Composite twins will have unique measurements that may be independent from the underlying discrete twin measurement.
The monitoring of these key twin characteristics must be as current as the target twin’s data flow or update process. Digital twins that are updated once can have a single measurement to gauge its appropriateness for decisioning. A twin that is updated every second based on event stream data must be measured continuously.
This trap is the same for all digital twins regardless of context. The difference is in the potential impact. A facilities decision that leads to cooler-than-desired temperatures in the hallways pales in comparison to a faulty clinical diagnosis that leads to unnecessary testing or negative patient outcomes.
All it takes is a single instance of a digital twin used beyond its means with negative results for trust to disappear — erasing the significant investments in time and effort it took to create the twin. That is why it is imperative that twins be considered a technology product that requires constant process improvement. From the IoT edge where data is collected to the data ingestion and analytics processes that consume and mold the data to the digital twin creation routines, all must be under continuous pressure for improvement.
Recommendations
Include a Concise Digital Twin Vision Within the HDO Digital Transformation Strategy
Digital twins are one of the foundational constructs supporting digital transformation efforts by HDO CIOs. They are digital representations of the real-world entities targeted by organizations that benefit from the advances and efficiencies technologies bring to healthcare delivery. Those technology advances and efficiencies will only be delivered successfully if the underlying data and associated digital twins have the appropriate level of precision to sustain the transformation initiatives.
To ensure this attention to digital twin worthiness, it is imperative that HDO CIOs include a digital twin vision as part of their organization’s digital transformation strategy. Binding the two within the strategy will reinforce the important role digital twins play in achieving the desired outcomes with all participating stakeholders.
Building new capabilities — APIs, artificial intelligence (AI) and other new technologies enable the connections and automation that the platform provides.
Leveraging existing systems — Legacy systems that an HDO already owns can be adapted and connected to form part of its digital platform.
Applying the platform to the industry — Digital platforms must support specific use cases, and those use cases will reflect the needs of patients, employees and other consumers.
Create a Digital Twin Pilot Program
Like other advanced technology ideas, a digital twin program is best started as a simple project that can act as a starting point for maturity over time. Begin this by selecting a simple model of a patient, a department or other entity tied to a specific desired business or clinical outcome. The goal is to understand the challenges your organization will face when implementing digital twins.
The target for the digital twin should be discrete and easily managed. For example, a digital twin of a blood bank storage facility is a contained entity with a limited number of measurement points, such as temperature, humidity and door activity. The digital twin could be used to simulate the impact of door open time on temperature and humidity within the storage facility. The idea is to pick a project that allows your team to concentrate on data collection and twin creation processes rather than get tied up in specific details of the modeled object.
Begin by analyzing the underlying source data required to compose the digital twin, with the understanding that the usability of the twins is directly correlated to its data’s quality. Understand the full data pathway from the IoT devices through to where that data is stored. Think through the data collection type needed for the twin, is discrete data or real-time data required? How much data is needed to form the twin accurately? How accurate is the data generated by the IoT devices?
Create a simulation environment to exercise the digital twin through its paces against known operational variables. The twin’s value is tied to how the underlying data represents the response of the modeled entity against external input. Keep this simple to start with — concentrate on the IT mechanisms that create and execute the twin and the simulation environment.
Monitor and measure the performance of the digital twin. Use the virtuous cycle to create a constant improvement process for the sample twin. Experience gained through this simple project will create many lessons learned and best practices to follow for complex digital twins that will follow.
Read More