Article | August 31, 2023
As the COVID-19 pandemic upended the healthcare system, hospitals and doctor’s offices doubled down on technology and implemented a host oftelemedicine services, from virtual visits to remote patient monitoring and customized treatment plans.
The results were unexpected. Not only did telemedicine help bridge the gap between physicians and patients during the health crisis, but arecent J.D. Power studyfound that telemedicine also delivered increased customer satisfaction, outpacing other healthcare services.
Patient-centered care played the largest role in this shift. Technologies that let staff reach patients anytime, anywhere enabled providers to shift their functional focus away from simply treating issues to building better relationships.
Article | November 29, 2023
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):
Clinical communication and collaboration
Next-generation nurse call
Alarms and notifications
Integrated patient room
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.
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.
Health Technology, Digital Healthcare
Article | September 7, 2023
Is your health technology company publishing content online? Not sure how to make your health tech content marketing effective?
Each step taken in the process of content marketing matters. Skipping essential steps may negatively affect the whole process. The following steps are critical in your content strategy:
• Defining your ideal buyer
• Gathering insights about how they make decisions
• Documenting how your content will address their needs
Lately, health tech companies are facing too much competition. Thus it would be wise to equip yourself with a good marketing strategy, including a clear content marketing plan. This blog focuses on making your health content marketing more effective, overcoming challenges, and eliminating the chances of failure.
Content Marketing challenges for Health Tech Marketers
Heath tech brands face multiple obstacles with health tech content marketing. They must deal with many challenges such as content proliferation, uncoordinated and inconsistent content creation, and difficulty in reaching out to customers and prospects with relevant and timely content.
A lot of hard work and time is required to create great content. Let us have a closer look at the challenges faced by health tech marketers related to content marketing.
Determining Content Marketing ROI
Many of the health tech content marketers are struggling to show ROI from their content marketing efforts. They cannot claim a specific conversions rate from a channel or a piece of content. Often digital conversion paths of the health tech industry cannot be analyzed or tacked.
Some other biggest challenges of health tech content marketing are tying content to conversions, defining appropriate and relevant metrics for measuring and evaluating the impact of the content marketing efforts on its bottom line.
Figuring Out How to Feed the Content Beast
Including health tech brands, many brands spend too much time thinking and worrying about creating compelling content. Or they worry about making content thattheir boss, salespeople, or other stakeholders need. The goal should be creating content consistently that is necessary for the client's journey. Your health tech content marketing should focus on your target audience searching online for your product and business.
Proving Credibility and Authority
Many health tech marketers struggle with defining a credible and authoritative voice for their brands. They fail in the process of cutting through the noise and grabbing the attention of their target audience for this very reason.
A health tech content marketing strategy should maintain the brand’s identity, improve its authority, and boost ROI. Here, thought leadership has a role to play. Use your people, their expertise, and their passion effectively to share what they know about your product with your target audience. Trust and credibility will follow.
Maintaining Volume, Quality, Speed
Another significant challenge of health tech content marketing is developing compelling, engaging content fast without compromising on volume and quantity. For many marketers, a big pain point is trying to stay agile and nimble within a large corporate structure.
The marketing landscape is ever-changing. It means health tech marketers have to dedicate themselves to learning throughout their life.. They also have to reinvent through innovation to avoid extinction. Primarily, health tech content marketing needs constant innovation and learning. Everyone is trying to navigate the learning curve. It is very challenging for marketers to train teams and update them with the latest marketing practices.
Another challenge faced by most marketers in their health tech content marketing efforts is identifying influencers to amplify content. Creating great content is just the first step. Having an effective promotion strategy to reach your prospects with your content helps.
Reasons for Health Tech Content Marketing Failure
A study by Healthcare Information and Management Systems Society (HIMSS) on health tech content marketing shows a vast gap between results and goals. Is there discontent in health tech content marketing? Eighty-five percent of the survey participants said they have a content marketing strategy, but only 4 percent only said their content marketing strategy was effective.
So, it's clear that something was not working for them. Here are some of the key reasons:
The Absence of a Content Strategy
Except for some large tech companies, such as IBM, Microsoft, and Salesforce, most vendors do not have a content marketing strategy. For many tech companies, content marketing is part of their overall business strategy. So consider building a health tech content marketing strategy to have a leg up on your competitors.
Lack of Sponsorship
Creating compelling content is a low-priority task for most B2B tech companies. A small marketing team usually takes care of health tech content marketing with limited resources and budget. It results in content that does not align with your market positioning and business strategy.
Content marketers need quality content for marketng. For this, subject matter experts (SME) should be involved in the process of content creation. SMEs, most often, are too busy to participate in the process. As a result, the content may turn out low in quality and might not be consistent
Lack of a Content Distribution Strategy
Unless you promote high value content on all the digital and social media channels, no one will hear about your health tech brand. Most B2B health tech companies fail to promote themselves through effective health tech content marketing on multiple digital and social media channels.
B2B health tech companies want instant results. The survey by in 2020 HIMSS points out the minimum time needed to fetch results from content marketing programs. A health tech content marketing program launched in this quarter will not bring you any developments in the same quarter.
Effective Content Marketing Plan for Health Tech Marketers
The global pandemic has accelerated the shift towards digital marketing. Many healthcare technology marketers focus on pure sales collateral and product-centric content instead of thought leadership and human-centric content.
Here are five areas that deserve more attention in your health tech content marketing plan. These should be top priorities in your content plan.
Planning the Process and Setting Reasonable Goals & Objectives
Data shows that most healthcare technology companies do not have an effective health tech content marketing plan. It is not surprising that these companies, accelerating ahead everyday, do not have the time to plan or enough resources to execute it. There are three reasons behind it:
• They never made planning a proper priority.
• They realize they don’t have the time.
• The team doesn’t know where to start.
Good planning with realistic goals and expectations solves this issue. Quality content marketing is a long-term investment, not a short-term performance vehicle.
Benchmark the Market & Your Competition
Investigating your competition may get you down. Analyzing every aspect of your competitors’ content, including approach and strategy, will help you learn many essentials things. You will get ideas to improve your health tech content marketing from your competitor analysis.
Do not get confused between your sales competitors and content competitors. Your sales competitor is the one who sells your exact product or service. Your content competitors are companies ranking on search engines for the same content that you want to ranked for on search engine result pages. In addition to pure sales competitors, these content competitors can include major publishers such as trade associations and newspapers.
Fine-Tune Your Messages, Themes & Topics
It is effortless to come up with a long list of content ideas. It is vital to understand the themes and topics, which will work better for your business goals. The topics should be worth the time and effort you put in. Your themes, topics, and other content ideas in your health tech content marketing plan should support your core health tech messaging.
People usually tend to create content randomly and wonder why their content marketing does not work in the end. Fine-tuning your themes, messages, and topics and making sure they are all unified in the process is a big part of ensuring your content succeeds in the end.
Address All Four Content Distribution Channels
Creating great content is just a first step to your health tech content marketing. You may have to think of the content distribution channels: owned media, internal channels, earned media, and paid media. All of these channels have multiple options for content distribution. Depending on your company’s business goals and particular situations, you can choose the best-suited application from these channels.
Get the Most Out of Your Content Marketing Efforts
Maximize your health tech content marketing investment with the three Rs: refresh, repurpose, and repromote.
Refreshing means updating old content that performed well in the past. It may include changing the published date and updating the internal links to timely and more current information. Repurposing means changing the format. You can make a blog post out of a webinar or create an infographic out of a case study. Promoting and redistributing older content that performed well in the past is repromoting.
With content marketing, it is tough to have long-term success without a documented strategy and commitment. With a detailed process, you will have clarity about your goals and the tactics you will use to achieve them. If you do not have this practice, it's better to develop your strategy and write it down to improve your health tech content marketing's better effectiveness.
Content marketing is a proven way to connect with the tech industry audience, especially in health tech. Creating great content about your business, which the audience finds reliable and helpful, will make your company reliable and a trusted source to solve problems.
Doing it alone may be a tiresome job. We, at Media 7, provide all the assistance in marketing technology products online. We have the right solution for all your demand generation, lead generation, sales, and marketing problems. Media 7 converts leads and turns them into your happy customers forever. To know more about Media 7, visit: https://media7.com
Frequently Asked Questions
Why is content marketing important in health tech?
Content marketing is crucial in health tech as unique content makes your target audience trust your brand and consider it a trustworthy source for solving their issues. Moreover, to build your brand, content marketing is a vital component.
How do you create a successful health tech content strategy?
When you create a successful health tech content strategy, an ideal buyer profile, buyer persona, customer journey, etc., should be considered. Along with that, you should have clear objectives and goals when you make a content strategy.
What is the essential step in creating a health tech content marketing plan?
The essential step in creating your health tech content marketing plan is defining your targeted audience and understanding the buyer persona. It will help you create relevant and audience-focused content.
Article | November 1, 2021
Throughout my professional carrier, I iused to visit many companies involved in drug discoveries and had seen the challenges they go through. Some are pleasant as the investigational molecules were moving forward in value chain whereas few faced bottlenecks at the end. The association with Pharma industry over the years had taught me about many new ideas and allowed me to see that how innovative ideas are impacting our social and scientific world to a great extent. The changes we see today, are the results of ideas came from various quarters globally and I feel digital innovation had shaped today’s world differently. The impact of digital platform in today’s Pharma world is a “Game-Changer".
Innovation is a continuous process which simplifies challenges into reality and plays a very important role in our society. Centuries ago, scientists used to spend years in laboratories to understand material science. The chemical science evolved around discovering elements, synthesis of compounds or even isolating products from natural resources. Today’s world is highly indebted to those discoveries and efforts and modern science has gradually moved towards digital platform. Last few decades, innovations based on new technology platforms has made huge impact in scientific discoveries and few such ideas and action I feel has brought significant changes. Our lifestyle and social environment have witnessed deep impact due to such innovation. The chemical science is evolved not around only chemists today but have huge influence of mathematicians and technologists for faster development.
Advancement of digital science, new algorithms to solve the problems has modified the way of drug discovery to a great extent. In the recent past, we were heavily depended on big machines, but innovation has brought the whole items in a small packet now. The technology platform is modified, speed has increased in identifying new drugs with artificial intelligence (AI) and machine learning is accelerating the drug discovery and development processes. Today’s Pharma industries for commercial supplies are now depended on automation, optimization of the manufacturing processes, as well as designing effective marketing and post-launch strategies. The process is aimed to have better control on the operation, improving safety and better predictability of quality. For conducting clinical trials, identifying patient’s profile, an eligibility criteria is crucial which has been made by the processes being faster and cost effective by introducing Artificial Intelligence (AI).
Earlier when focus was to identify the origin of life, finding new elements, compounds or building blocks, today’s world is heavily dependent on data or ‘Big Data’. The amount of information available throughout drug discovery and development process, analyzing, interpreting, and predicting right candidate require high-performance systems to analyze data properly and derive value from it. There is advancement of analytical techniques, which provides more accurate information about the clinical trial reports and the data across patient pool, zeroing down towards right candidate is a real challenge and there are several AI enabled tools available where the processing time is reduced significantly which might have taken several years. The exciting part is that innovation is not only limited to laboratory work but works in coordination of mathematical interpretations, data analysis and provide significant clues to develop new molecules and even provide approach towards therapeutic categories. Currently available advanced technologies enhance drug development process, making it less time-consuming and cost-effective process where AI can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design. Data scientists play a very significant role in all these activities.
Innovation focusing personalized medicine is now a reality and companies involved in such basic research have made breakthrough to understand how the human body responds to drug. Software solution is also available for simulating effects of drugs in patient body based on individual characteristics, scientific data for real time prediction of efficacy and drug interaction on individual. These predictive models are shortening drug discovery pathways to a great extent. Small molecule drugs or even large molecules development are heavily depended today on such modelling and predictive approach. The aim to reduce cost of drug development, shortening discovery path, focus on clinical trial mechanism is more productive with a higher success rate. During the pandemic period, in a shortest possible manner, several companies started working to develop new drugs or vaccines using drug-specific exposure models for drugs under investigation for the treatment of Covid-19.
Similarly, discovery platform is also working on cutting edge technology ‘Organ-on-a-chip’ that can emulate the physiological environment and functionality of human organs on a chip for disease modeling, mimicking the impact and could be a game changer in future. I will be happy to see when technology platform can accurately predict human mind and with the help of AI, can find a probable solution to avoid any such complex conflicts. It would be interesting to see that AI is analyzing and predicting the chemical change in the bodies impacting human mind and analysisng it quickly to predict psychological behavior of the patient and guide physician for right therapy. This may lead to predicting problems one may face in old ages where the decays may be prevented at early stage. This is a challenge but understanding and predicting psychological behavior may improve patients’ life. Depression and its remedy may be based on understanding changes, patterns of physicochemical behavior and its impact during mood swing and predicting such things in advance by using the advanced AI tools could be a game changer.
Another path breaking development where technology involving both engineers and scientists to help drug design to obtain maximal therapeutic benefits for patients including designing drug delivery systems and biomedical devices is 3D-printing technology. This involves high end computer simulations making analysis faster and predictive than before. Influence of 3D printing in designing variety of dosage forms has simplified its preparation. Though further study is under progress but the technology implementation at late has reduced cost of drug development to a significant extent and will add value in future drug development. It is interesting to see how this 3D printing technology works on human brain mapping and predicting a right path for treatment for betterment of large patient pool. Today with advanced technology, we are now more dependent on machines, limited close interaction with our near and dear ones, but created more friends on social platform. Though life looks easy, but over dependent on machines is creating another complex environment and this growing complexity may change the disease pattern. It will be interesting to see that how these technology platforms improve further to ease out such complexities for a healthy future.