Hospital Cybersecurity 2019: Seven Essential Leadership-Level Strategies

Health Technology, Digital Healthcare

Article | September 7, 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.

Digital Healthcare

Article | November 29, 2023

Introduction Healthcare supply chains are highly regulated, high-margin domains with stringent quality criteria, such as the demand for cold-chain storage and shipping. In addition, due to a lack of adequate understanding of the complicated rules governing healthcare products and services, medical aid providers are encountering inefficiencies while outsourcing capabilities to logistics service providers (LSPs). In today's value-based care paradigm, healthcare leaders are tasked with improving patient outcomes while decreasing costs. With surging globalization, the growing cross-border nature of several medical products, and rising complexities in healthcare logistics, the need for building a robust supply chain is rapidly increasing across the healthcare industry. Key Ways to Enhance Healthcare Supply Chain A resilient supply chain plays a vital role in managing supplies, procuring resources, and shipping medical goods, among others, which assists in improving patient outcomes, enhancing service efficiency, and declining costs. Healthcare organizations are aiming to strengthen their supply chain to enjoy these benefits such as increased reimbursement, improved quality control, reduced inventory and overhead costs, and better collaboration with suppliers. Here are some of the key ways to assist medical aid providers in building a strong healthcare supply chain Develop effective inventory management Manufacturers frequently encounter difficulties with timely inventory movement through their supply chains. Revenue is lost, and profit margins are harmed when products expire because they are not used adequately. This is why it is crucial to effectively manage the inventory process. Hence, healthcare executives should deploy a high-quality equipment management system and increase collaboration between organizations, suppliers, and manufacturers to assist them in controlling inventory levels and effective inventory management. Use technology and analytics to make operations run smoother When it comes to supply chain management automation, the healthcare sector continues to lag behind other sectors. This is true for both the enterprise resource planning (ERP) functions and data analytics use for making decisions about forecasting and product choice. Healthcare executives should make use of efficient technologies, such as solid analytics, in order to successfully improve a supply chain. Implementing data analytics and automation tools can assist in making healthcare supply chain management more efficient and effortless. Widen and strengthen the supplier base One of the most effective ways to build a robust healthcare supply chain is by increasing the number of suppliers and enhancing supplier base management. Manufacturers and purchasers should abandon using single-or limited-source supply chains for raw materials and finished goods. This will assist healthcare companies in expanding their network of raw material suppliers and helps in careful consideration of which suppliers to use for procuring which resources. Improve order accuracy and order cycle times to lower costs Healthcare providers sometimes encounter a large number of errors during the ordering process. When incorrect products are ordered, the facilities face losses in revenue and are sometimes unable to deliver specific services due to the unavailability of products they need. Errors in placing orders can also result in medication errors. This can lead to a decrease in patient outcomes and negatively impact the company’s revenue growth. To reduce the frequency of errors, these providers should deploy a computerized provider entry system. This will help improve order accuracy, increase supply chain efficiencies, and shorten turnaround times in healthcare ordering processes. Consistently track and calibrate performance Regardless of the size of the healthcare facility, it is of great importance for medical aid providers to carefully track and calibrate their performance frequently or on a specified timeline. This enables clinically integrated supply chains in healthcare to use key performance indicators (KPIs) to compare outcomes in terms of cost and quality and make decisions about products. What’s Next? The rising integration of novel disruptive technologies such as data analytics, the internet of things, artificial intelligence, and blockchain into healthcare supply chain management software is facilitating smooth operations, transparency, growth, and security across the life-science industry. With a growing need for enhancing supply chains, healthcare providers are focusing on deploying advanced supply chain management solutions, such as healthcare logistics software, to strengthen their supply chains. As the trend continues to grow, the sales of healthcare supply chain management software and services are anticipated to rise from the US$2.2 billion registered in 2020 to US$3.3 billion by 2025.

Healthtech Security

Article | August 31, 2023

Do you know a friend or loved one who suffers from fear, anxiety, and depression and do not know what to do to help them? It can be frustrating to watch someone you know struggle with their mental health and not be able to do anything to relieve their suffering. With this in mind, here are seven ways to help the person cope in these kinds of situations. 1. Learn as much as you can in managing anxiety and depression: There are many books and information that will educate you on how to successfully overcome fear and anxiety. Share this information with the individual who is struggling with their fears. The key is to get your friend to understand how important it is to seek some guidance when it comes to their mental health. 2. Be understanding and patient with the person struggling with their fears: Maintaining depression and anxiety can be difficult for the individual so do not add more problems than what is already there. Do not get into arguments with your friend who may be having a difficult time with their anxieties. Make an effort to listen to the person rather than making judgements. 3. Talk to the person instead of talking at them: It is important not to lecture the individual who’s having a hard time with anxiety and depression. Talk to the person about their issues without being rude. Most people will listen if you approach them in a proper manner. Remember to treat others the way you would want to be treated if you were the one who was struggling. 4. Ask for some ideas: Seek advice from a professional who can assist the person you know with their mental health issues. A counsellor can give you some ideas on how to overcome anxiety, fear, and depression. Getting help from a therapist is the number one priority in getting the individual to do something about their problems. 5. Find out why the person won’t get assistance: Address the issues on why he or she will not seek treatment. Many people who are struggling are fearful and frustrated. Try to find out the reasons why your friend won’t get the help they need and then try to find the ways that will overcome their resistance of seeking some guidance. 6. Remind the person on the consequences of not getting help: Another way to convince the individual who is struggling with fear and depression is to tell them what may happen if they don’t get some counselling. Anxiety and depression can make things worse and usually won’t go away by themselves. 7. You can’t manage your mental health all by yourself: A person’s fears and anxieties can be difficult to manage and more than likely he or she will need some help. Many people think that they can overcome their mental health problems on their own. This is a mistake. The individual should admit they have a problem and then seek treatment to get their life back on track.

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.