How to Avoid Keto Flu & Keto Flu Remedy Tips

Healthtech Security

Article | August 31, 2023

With their speed and maneuverability, drones have long captured attention for their potential to deliver shopping orders and photograph the world from above. But the machines can also serve a medical purpose by shuttling drugs, defibrillators and other equipment to emergency sites or care facilities. Improvements in battery life, GPS navigation and artificial intelligence functions are making drones a more practical option in healthcare, says Dr. Daniel Kraft, faculty chair for medicine and neuroscience at Singularity University, an executive education program that also serves as a business incubator and consultancy service. Theft is also a concern, says Will Stavanja, chief technical officer at AirBox Technologies, which is testing its DroneX fleet for medical deliveries in the Caribbean. “Right now, most drones land, drop the package and leave,” says Stavanja, whose team has developed smart mailboxes for secure delivery. Addressing that risk will be key to determining whether drones can handle sensitive cargo. “You want a very high degree of certainty,” Kraft says. “It’s one thing if you lose a urine sample or a blood sample; you can get another one. It’s another to lose an organ.”

Healthtech Security

Article | November 29, 2023

Since ChatGPT’s launch in November 2022, artificial intelligence (AI) tools have become disruptive to nearly every industry. While there's been controversy about whether AI would benefit the healthcare industry, it has proven to be just as capable in healthcare as in other sectors. In the medical field, there is reason to believe AI tools may be an even more reliable and useful resource than other sectors. Medical students have been panicking over AI's threat to their career prospects. But as these systems mature, the experts increasingly believe that AI may serve as a counterpart to human medical expertise rather than a threat. How AI Tools Are Expected to Aid Medical Professionals? Again and again, as the debate over modern AI tools rages on, we encounter the analogy of the calculator. No one feels threatened by calculators, not even professional mathematicians. Instead of throwing up their hands, math experts embrace the power of these now archaic computerized devices. If the experts are correct, this may be similar to the future of the alliance between AI and humans. According to the designers and programmers who understand how these systems work as well as how information technology tends to progress, AI can be expected to help the medical profession in the following ways: Cosmetic Surgery Consultations One of the farthest-reaching applications we see develop is in consultations for plastic surgery and similar applications. Perhaps one of the easiest aspects to understand is hair-loss consultations. In our practice, we use a device known as HairMetrix, which uses an AI-driven analytical system to help determine what is causing a patient to lose their hair and which treatment options would be the most effective. Because it is AI-driven, it is fully based on visual scans and is completely non-invasive. Just like this, AI can be used in an abundance of other ways to minimize the use of exploratory surgery and improve healthcare outcomes. Improved Diagnostics Artificial intelligence is already helping medical providers deliver diagnoses more quickly. These tools can identify anomalies that might otherwise take human hours or even weeks to identify. This has improved the rate of cancer detection, among other things, which will predictably improve survival rates. Developing New Pharmaceuticals The development of new medicines is notoriously slow. Not only is testing a painstaking process, but even seeking FDA approval can take years. AI is expected to help the development of pharmaceuticals through simulation on the molecular level, allowing researchers to see how the active mechanisms in a drug will work in the body. Improved Administrative Efficiency In the medical field, administrative tasks are notoriously slow. It is believed that generative AI will be able to automate many administrative functions and innumerable office chores. It could streamline sorting patient files, accelerate the interpretation of data, and much more. Patient Access In an area where information technology is already improving patients' lives, access to medical advice is still a bottleneck in the system. AI tools have the potential to slowly bridge the gap in health disparities. Combined with the power to diagnose, this could dramatically increase the capability of online patient portals. Of course, this list of anticipated AI capabilities is far from exhaustive. Researchers and medical professionals have high hopes for these tools, and some are already proving to be more than mere speculation. In a world where AI is reshaping industries at an unprecedented pace, the healthcare sector stands poised to benefit significantly from this technological revolution. From streamlining administrative tasks to revolutionizing diagnostics, the potential of AI in medicine is vast and diverse. As we witness AI-enabled tools like HairMetrix, enhancing the cosmetic surgery consultations and AI algorithms expediting diagnostic accuracy, it's clear that we are only at the beginning of a healthcare transformation that is set to improve patient care, increase survival rates, and revolutionize medical practices.

Health Technology, AI

Article | July 18, 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.

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.