Don’t Underestimate Patients

Health Technology, Digital Healthcare

Article | August 21, 2023

Mental illness is a fatal illness. Surprisingly, mental health is grossly underfunded all over the world. Despite countries' economic strength, there are still no long-term and solution-driven results for addressing mental health. According to WHO reports, only one in every fifty people receives medical treatment for severe mental illnesses. The Sustainable Development Goals (SDGs) are a global initiative to create a more equitable and sustainable world. According to the World Health Organization, "there can be no health or sustainable development without proper mental health." As a result, mental health is one of the most critical pillars in creating a sustainable world in the future. In short, if mental health is good, sustainability will follow naturally. Positive Mental Health = Thriving Sustainability According to the WHO’s decision-making body, ‘The World Health Assembly (WHA),’ mental health deserves to be at the top of the sustainability agenda because it affects people of all ages. It means anybody can be diagnosed with a mental illness, which is directly proportionate to sustainable development. So, a similar amount of treatments, diagnoses, and awareness should help people overcome mental illness. However, on the other hand, according to the WHO's 2020 Mental Health Atlas, only 23% of patients with mental illnesses have been integrated into healthcare systems in developing countries. This highlights the undeniable fact that the world's 280 million people suffering from depression have been kept away from receiving a proper diagnosis, treatment, and care. Moreover, up to 85% of people with mental illnesses are untreated. The numbers are shocking! Such statistics are enough evidence to create a supportive culture free of the stigma that mental illness is incurable and encourage patients to seek help when they need it. It includes geography-specific mental health resources, proper diagnosis, care, medication, availability, accessibility, other requirements, and adequate support systems. Global Action is Key to Both Providers of mental health services cannot do it alone. Instead, it requires a strong global response. In this case, leading companies and legislative bodies should exert influence to promote cost-effective, widely accessible, and evidence-based treatments for mental health disorders. Some low-cost solutions to this global problem will eradicate it and bring about long-term development to support this point. As a result, the solutions are as follows Improving social and economic environments as part of sustainable development Integrating mental health into general primary health care Providing appropriate care and treatment through trained and supervised community members Using technology to introduce the most up-to-date solutions for mental health disorders Wrapping Up Transformation is essential today, both technically and in terms of humanizing. Otherwise, sustainable development will be impossible to achieve unless the enormous challenge of mental health is addressed. Therefore, healthcare leaders will need to develop transitional plans to increase coverage in real-time to accomplish this. This should include proper diagnosis and progressive tracking of mental health treatments.

Health Technology, Digital Healthcare

Article | August 16, 2023

Introduction Top Upcoming Healthcare Supply Chain Trends to Know About Immediate Access to Medical Supplies Emphasis on Visibility and Tracking Investment in Technologies Artificial Intelligence Cloud Computing Data Analytics Healthcare Supply Chain Management Solutions: Features and Significance Why Is Supply Chain Management Software Gaining Prominence in Healthcare? Closing Thoughts Introduction With the ever-evolving and changing nature of the healthcare industry, organizations are discovering new methods to eliminate waste, reduce costs, and prioritize patient concerns. Despite the fact that the COVID-19 pandemic has shown the vulnerabilities of healthcare supply chains and caused shortages of personal protective equipment (PPE) and other essential medical supplies, it has served as a catalyst for innovation and digital transformation in healthcare supply chain management solutions. Healthcare and medical organizations have learned the hard way to let go of the traditional ways of thinking, such as a mindset that prioritizes just-in-time supply chains and logistics optimization over everything else. Tight, fragile supply networks and lousy inventory management have caused severe shortages of pharmaceutical and medical supplies in the past. However, with the emergence of innovative technologies and trends, now is the time to adopt a new mindset that emphasizes supply chain resilience, flexibility, and agility, making supply chains stronger and adaptable. This will enable healthcare spaces to handle unexpected spikes in demand and survive unpredictable disruptions brought on by pandemics, natural disasters, or cyberattacks in the future. Top Upcoming Healthcare Supply Chain Trends to Know About In recent years, healthcare providers have been concentrating on supply chain management solutions not only to increase the effectiveness of supply chain networks but also to gain more visibility into the entire supply chain. With the goal of streamlining the ordering process, enhancing demand planning & inventory management, and informing purchasing contract decisions, healthcare providers have started to understand the potential of advanced healthcare supply chain solutions. Since then, these organizations have recognized the value of an efficient and dependable supply chain as a critical competitive differentiator. A substantial rise in medical and healthcare spending is being witnessed across the globe due to the rapidly increasing number of various diseases and conditions, such as chronic ailments, infectious diseases, and genetic disorders. According to a study, the national health expenditure in the U.S. reached US$ 4.1 trillion in 2020, including the spending of US$ 829 billion on Medicare and US$ 671 billion on medical aid. Hence, healthcare organizations, manufacturers of medical devices, and other entities participating in the medical supply chain are investing in innovative supply chain and logistic solutions to provide optimal and timely treatments to patients. Innovation is not only crucial for lowering operating expenses and simplifying business processes, but it is also necessary for providing superior care to patients and enhancing clinical outcomes. Let's see some of the top healthcare supply chain trends that are revolutionizing the healthcare sector. Immediate Access to Medical Supplies Medical equipment and component shortages are not new challenges for the healthcare industry. Hospitals and other medical establishments have faced continual disruptions in the supply chain over the last decade, starting from personal protective equipment and medical device shortages to improper management of medical inventory. This was especially witnessed in the past two years, during the COVID-19 pandemic. Healthcare producers previously adopted a strategy to either pay higher premiums for medical equipment or stockpile them on their balance sheets. However, the issue with this strategy was that during the acute and unprecedented shortage, fewer devices and parts were left to acquire and keep in inventory, which has compelled hospitals to adopt a more cautious supply chain strategy. Medical establishments need a more diverse pool of suppliers and cannot only rely on just-in-time inventory ordering. Hence, a swift rise in inclination toward adopting sophisticated supply chain solutions is being witnessed across the industry to monitor certain suppliers' run rates. This enables modern healthcare organizations to create more resilient and robust hospital supply chains and provide immediate access to medical inventory. Emphasis on Visibility and Tracking One of the most prevalent healthcare supply chain trends witnessed across the industry is the growing focus on visibility and tracking. Obtaining medical supplies, equipment, drugs and others safely, timely, and accurately from the factory floor to patients is the responsibility of the healthcare supply chain. As the industry shifts to value-based care models, the healthcare supply chain is experiencing enormous pressure to increase visibility, decline costs, and improve outcomes. With the advent of online shopping and the introduction of novel medical e-commerce platforms, manufacturers and suppliers of healthcare products are focusing on real-time tracking during transit and logistics operations. As a result, they are increasingly adopting advanced healthcare supply chain management software to enhance visibility, stream operations, and decrease delivery time. Investment in Technologies The efficient management of the healthcare supply chain is paramount for proper patient care and inventory control. The implementation of digital healthcare technology is an essential step toward the achievement of a higher level of efficiency in supply chain management. Digital healthcare logistics and supply chain solutions have the potential to generate long-term value for healthcare providers, which is one of the main aspects driving the transformation away from the conventional healthcare supply chain. It is improving patients' access to care while making it more efficient, cost-effective, and secure. In addition to this, the integration of advanced technologies is assisting hospitals and healthcare providers in reducing operational costs and identifying appropriate growth opportunities. The following are the technologies healthcare organizations are investing in to strengthen their supply chain management Artificial Intelligence The healthcare supply chain trend that is paving the way in the industry is artificial intelligence (AI). It is the future of the healthcare supply chain. The integration of the technology assists in improving logistics efficiencies via quicker data processing, continuous process improvement, and accurate demand-supply forecasting. By connecting historical data with external patterns that affect production, AI presents a chance to greatly increase the speed and precision of healthcare logistics and supply chain activities. In addition, it enables medical equipment manufacturers and suppliers to constantly record crucial operational data and preserve institutional expertise for future use. Cloud Computing Cloud computing technologies have played a significant role in transforming the healthcare supply chain management that is used today. It reduces expenses, shortens logistics operations, and increases data security by centralizing data and providing access to that data to numerous associated entities. With technological advancements, cloud computing is anticipated to gain huge popularity in creating a resilient supply chain with a well-knitted network of healthcare providers, manufacturers, and suppliers. And also provides next-generation features, such as transport route optimization and automatic inventory management. Data Analytics Data analytics provides a broader range of in-depth information that can be used to make operational processes more effective, inventory management more strategic, and decisions more accurate. It also assists in transforming the healthcare supply chain by providing access to cutting-edge methods like data mining, predictive forecasting, and predictive analytics to foresee future events with precision, assisting healthcare providers in forecasting further demand and supply interruptions. In addition to the increasing need for declining costs and transit time, drug manufacturers and healthcare aid suppliers are investing in advanced data analytics to assist them in visualizing the whole supply chain, including its pain points, inefficiencies, and strengths. Healthcare Supply Chain Management Solutions: Features and Significance The healthcare industry as a whole is thriving, and this growth is reflected in the more sophisticated and digital nature of healthcare supply chain management software. The solution helps medical facilities see the big picture by coordinating and integrating procedures that manage and control the flow of money, data, and items as a product or service, all the way from the point of production to healthcare spaces, allowing for more efficient care. The primary goals of these supply chain solutions, such as healthcare logistics software, are to improve visibility and efficiency throughout the distribution network. In recent years, these goals have expanded to include the strategic objective of improving supply chain agility and resilience— to cope with times of uncertainty, shortages, and volatility in demand and supply conditions. Getting supply chain management right in healthcare means that participating players will be able to effectively identify and resolve bottlenecks, possible interruptions, and other issues that arise anywhere along the end-to-end supply chain. As one of the features, these supply chain solutions have the potential to improve patient care and safety while reducing waste and wasteful expenses. Let's see a few more features of healthcare supply chain management software Procurement Management Logistics Management Inventory Management Order Management Warehouse Management Supplier Relationship Management Why Is Supply Chain Management Software Gaining Prominence in Healthcare? Due to increasing instances of medication and healthcare aid shortages, a strong need for modernizing the healthcare supply chain is being experienced by numerous hospitals, drug manufacturers, and suppliers. In addition, the growing use of medical e-commerce is further increasing the complexities in the inventory and logistics operations, making it difficult for the players mentioned above to provide timely delivery, reduce wastage, and ultimately save patients. To counter these challenges, medical establishments are emphasizing adopting more advanced and resilient supply chain management solutions, which assist them in streamlining and automating routine manual processes such as logistics operations, planning, forecasting, and others. The following are more reasons to encourage healthcare establishments to adopt resilient supply chain management software Logistics and Shipping Optimization Reduce Cost Effects Enhance Quality Control Increase Flexibility Better Collaboration with Suppliers Reduce Inventory and Overhead Costs Increase Output Enhance Transparency Decrease Delivery Time Closing Thoughts In today’s constantly evolving healthcare sector, medication manufacturers and hospitals are looking to adopt innovative solutions, such as healthcare logistics software, to eliminate waste, reduce costs, and improve patient care. The growing integration of AI, predictive analysis, blockchain, and other technologies is playing a major role in modernizing the healthcare supply chain and streamlining operations by automating and improving decision-making abilities. As the healthcare supply chain trends discussed above keep on creating new roads in the industry, supply chain management solutions, such as healthcare logistics software, are anticipated to become a necessity in the future. Thus, ahead-of-the-curve healthcare providers are seizing this instance as an opportunity to invest in cutting-edge technologies and shift towards using digital solutions to make their supply chains more robust and improve the flexibility of their logistics operations. FAQ What is Healthcare Supply Chain Management Software? Ans: A healthcare supply chain management software assists medication manufacturers and hospitals in sourcing, tracking, quality control, and logistics by automating operations, enabling them to manage costs, time, and inventory by planning and forecasting trends. What Are the Types of Supply Chain Management Software Used in Healthcare? Ans: The most common types of supply chain management software used across the healthcare sector are Logistics Sourcing and Procurement Inventory Management Warehouse Order Processing Shipping What Are the Key Performance Indicators of Supply Chain Management Software in the Healthcare Industry? Ans: Key performance indicators of supply chain management software in healthcare are Reduction in Cost Enhancement in Customer Service Time to Customer Forecasting Accuracy Flow of Logistics Process

Health Technology

Article | September 12, 2023

While many United States healthcare providers are returning to their in-person offices, many others are digging in to offer hybrid telehealth or exclusive telehealth practices moving forward. Understanding the future of telehealth reimbursement is then a pivotal issue. As a hub for telehealth consultants and trainers, the Telebehavioral Health Institute (TBHI) is receiving daily requests for assistance from behavioral health groups and independent practitioners seeking to position themselves for telehealth expansion optimally.

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.