Category: IT in Healthcare

AI Helps Clinicians to Assess and Treat Leg Fractures

Photo by Tima Miroshnichenko on Pexels

By using artificial intelligence (AI) techniques to process gait analyses and medical records data of patients with leg fractures, researchers have uncovered insights on patients and aspects of their recovery.

The study, which is published in the Journal of Orthopaedic Research, uncovered a significant association between the rates of hospital readmission after fracture surgery and the presence of underlying medical conditions. Correlations were also found between underlying medical conditions and orthopaedic complications, although these links were not significant.

It was also apparent that gait analyses in the early postinjury phase offer valuable insights into the injury’s impact on locomotion and recovery. For clinical professionals, these patterns were key to optimising rehabilitation strategies.

“Our findings demonstrate the profound impact that integrating machine learning and gait analysis into orthopaedic practice can have, not only in improving the accuracy of post-injury complication predictions but also in tailoring rehabilitation strategies to individual patient needs,” said corresponding author Mostafa Rezapour, PhD, of Wake Forest University School of Medicine. “This approach represents a pivotal shift towards more personalised, predictive, and ultimately more effective orthopaedic care.”

Dr. Rezapour added that the study underscores the critical importance of adopting a holistic view that encompasses not just the mechanical aspects of injury recovery but also the broader spectrum of patient health. “This is a step forward in our quest to optimize rehabilitation strategies, reduce recovery times, and improve overall quality of life for patients with lower extremity fractures,” he said.

Source: Wiley

Admin and Ethics should be the Basis of Your Healthcare AI Stratetgy

Technology continues to play a strong role in shaping healthcare. In 2023, the focus was on how Artificial Intelligence (AI),  became significantly entrenched in patient records, diagnosis and care. Now in 2024 the focus is on the ethical aspects of AI.  Many organisations including practitioner groups, hospitals and medical associations are putting together AI Codes of Conduct, with new legislation planning to be passed in countries such as the USA.

The entire patient journey has benefited from the use of AI, in tangible ways that we can understand. From online bookings, the sharing of information with electronic health records, keyword diagnosis, sharing of visual scans, e-scripts, easy claims, SMS’s and billing, are all examples of how software systems are incorporated into practices to facilitate a streamlined experience for both the patient and doctor. *But although 75% of medical professionals agree on the transformation abilities of AI, only 6% have implemented an AI strategy.

Strategies need to include ethical considerations

CompuGroup Medical South Africa, (CGM SA), a leading international MedTech company that has spent over 20 years designing software solutions for the healthcare industry, has identified one main area that seems to constantly be the topic for ethical consideration.

This is the sharing of patient electronic health records or EHR’s. On one hand the wealth of information provided in each EHR – from a patient’s medical history, demographics, their laboratory test results over time, medicine prescribed, a history of medical procedures, X-rays to any medical allergies – offers endless opportunities for real time patient care. On the other hand, there seems to be a basic mistrust of how these records will be shared and stored, no one wants their personal medical information to end up on the internet.

But there’s also the philosophical view that although you might not want your info to be public record, it still has the ability to benefit the care of thousands of people. If we want a learning AI system that adapts as we do, if we want a decision making support system that is informed by past experiences, then the sharing of data should be viewed as a tool and no longer a privacy barrier.

Admin can cause burnout

Based on their interactions with professionals, CGM has informally noted that healthcare practices spend 73% of their time dealing with administrative tasks. This can be broken down into 38% focusing on EHR documentation and review, 19% related to insurance and billing, 11% on tests, medications and other orders and the final 6% on clinical planning and logistics.

Even during the consultation doctors can spend up to 40% of their time taking clinical notes. Besides the extra burden that this places on health care practices, this also leads to less attention being paid to the patient and still requires 1-2 hours of admin in the evenings. (Admin being the number one cause of burnout in clinicians and too much screen time during interactions being the number one complaint by patients.)

The solution

The ability for medical practitioners to implement valuable and effective advanced technical software, such as Autoscriber, will assist with time saving, data quality and overall job satisfaction. Autoscriber is an AI engine designed to ease the effort required when creating clinical notes by turning the consultation between patient and doctor into a structured summary that includes ICD-10 codes which is the standard method of classification of diseases used by South African medical professionals    

It identifies clinical facts in real time, including medications and symptoms. It then orders and summarises the data in a format ready for import into the EHR, creating a more detailed and standardised report on each patient encounter, allowing for a more holistic patient outcome. In essence, with the introduction of Autoscriber into the South African market, CGM seeks to aid practitioners in swiftly creating precise and efficient clinical records, saving them from extensive after-hours commitments.

Dilip Naran, VP of Product Architecture at CGM SA explains: “It is clear that AI will not replace healthcare professionals, but it will augment their capabilities to provide superior patient care. Ethical considerations are important but should not override patient care or safety. The Autoscriber solution provides full control to the HCP to use, edit or discard the transcribed note ensuring that these notes are comprehensive, attributable and contemporaneous.”

Virtual Reality Sessions can Lessen Cancer Pain, Trial Shows

Photo by Bradley Hook on Pexels

Hospitalised cancer patients who engaged in a 10-minute virtual reality (VR) session experienced significantly lessened pain in a trial published in CANCER, a peer-reviewed journal of the American Cancer Society. Participants still experienced sustained benefits a day later.

Most cancer patients experience pain, and treatment usually involves medications including opioids. VR sessions that immerse the user in new environments have been shown to be a noninvasive and nonpharmacologic way to lessen pain in different patient populations, but data are lacking in individuals with cancer. To investigate, Hunter Groninger, MD, of Georgetown University School of Medicine and MedStar Health and his colleagues randomized 128 adults with cancer with moderate or severe pain to a 10-minute immersive VR intervention involving calm, pleasant environments or to a 10-minute two-dimensional guided imagery experience on an iPad tablet.

The investigators found that both interventions lessened pain, but VR sessions had a greater impact. Based on patient-reported scores from 0 to 10, patients in the guided imagery group reported an average decrease of 0.7 in pain scores, whereas those in the VR group reported an average drop of 1.4. Twenty four hours after the assigned intervention, participants in the VR group reported sustained improvement in pain severity (1.7 points lower than baseline before the VR intervention) compared with participants in the guided imagery group (only 0.3 points lower than baseline before the active control intervention).

Participants assigned to the VR intervention also reported improvements related to pain “bothersomeness” (how much the pain bothered them, regardless of the severity of the pain) and general distress, and they expressed satisfaction with the intervention. 

“Results from this trial suggest that immersive VR may be a useful non-medication strategy to improve the cancer pain experience,” said Dr Groninger. “While this study was conducted among hospitalized patients, future studies should also evaluate VR pain therapies in outpatient settings and explore the impact of different VR content to improve different types of cancer-related pain in different patient populations. Perhaps one day, patients living with cancer pain will be prescribed a VR therapy to use at home to improve their pain experience, in addition to usual cancer pain management strategies like pain medications.”

Source: Wiley

The Digital Nurse: Redefining the Future of Healthcare in South Africa

Sandra Sampson, Director at Allmed

By Sandra Sampson, Director at Allmed

The South African healthcare landscape is undergoing a transformative shift, driven by the rapid advancement of technology. At the forefront of this change is the rise of the “digital nurse,” a testament to the increasing integration of technology into the nursing profession. This transformation is not only streamlining processes; it is addressing critical challenges like the nation’s nurse shortage while ultimately improving patient care.

Embracing convenience and accessibility

Virtual platforms have become commonplace in the nursing world, facilitating efficient and accessible professional development for nurses through online meetings, networking opportunities, and educational resources. This fosters a more connected and knowledgeable nursing community, better equipped to serve patients.

Telehealth consultations, another facet of digital nursing currently revolutionising patient care, provide convenient and accessible medical consultations from the comfort of one’s home, eliminating long wait times and unnecessary travel.

Mitigating nurse shortages and ensuring quality care

South Africa grapples with a significant nurse shortage, placing a strain on the healthcare system to which digital nursing offers a practical potential solution. By leveraging technology, nurses can effectively manage larger patient volumes, reducing the burden on the existing workforce and optimising resource allocation. Remote monitoring systems and AI-powered tools further empower nurses by providing real-time patient data and facilitating early intervention, ultimately improving the quality of care delivered.

Additionally, embracing technology ensures that patients, even in underserved areas, receive quality care. The efficiency gained through virtual platforms allows nurses to allocate their time effectively, addressing minor health concerns remotely and reducing the strain on healthcare facilities for non-emergency cases.

However, it must be pointed out that although leveraging technology allows nurses to effectively manage larger patient volumes, which can alleviate the strain on the current system, this doesn’t necessarily mean fewer nurses are needed, but rather that technology empowers existing numbers to reach a wider patient base to deliver more efficient, personalised care.

Evolving alongside technology: the digital nurse of tomorrow

As the healthcare industry embraces digital technologies, the role of the nurse will continue to expand. While traditional nursing skills will remain essential, the “digital nurse” of the future must possess additional competencies.  Acquiring proficiency in digital tools and equipment, along with the capability to interpret and analyse digital data, will be crucial for delivering effective patient care. However, the most critical attribute for the digital nurse will be the willingness to adapt and embrace constant technological advancements. This will require a mindset shift that comes with acknowledging that traditional methods might not be sufficient in the face of evolving patient needs.

The challenges and opportunities in change

While the adoption of digital nursing brings numerous benefits, challenges remain. Resistance from individuals accustomed to traditional healthcare practices is one hurdle. However, with the younger generation being more adaptable, the shift towards digital nursing is expected to gain wider acceptance as technology advances. To ensure the success of this digital-first healthcare, it will be necessary to focus our attention on upskilling, which means recognising that continuous training and development programs are vital for nurses to remain proficient in the face of change.

On the flip side, a change in perspective from nursing professionals themselves will be necessary. This means embracing a growth mindset and being open towards new technologies to adapt and thrive in the digital age. Lastly, healthcare professionals as a whole need to bear in mind that transformation is essential to meet the evolving needs of patients, which includes catering to a growing preference for digital healthcare solutions. Continuing to meet the needs of patients is the only guaranteed way for nursing professionals to ensure their relevance in the future. By embracing technology and fostering a culture of continuous learning, South Africa can empower its nurses to become the digital healthcare leaders of tomorrow.

AI-based App can Help Physicians Diagnose Melanomas

3D structure of a melanoma cell derived by ion abrasion scanning electron microscopy. Credit: Sriram Subramaniam/ National Cancer Institute

A mobile app that uses artificial intelligence, AI, to analyse images of suspected skin lesions can diagnose melanoma with very high precision. This is shown in a study led from Linköping University in Sweden where the app has been tested in primary care. The results have been published in the British Journal of Dermatology.

“Our study is the first in the world to test an AI-based mobile app for melanoma in primary care in this way. A great many studies have been done on previously collected images of skin lesions and those studies relatively agree that AI is good at distinguishing dangerous from harmless ones. We were quite surprised by the fact that no one had done a study on primary care patients,” says Magnus Falk, senior associate professor at the Department of Health, Medicine and Caring Sciences at Linköping University, specialist in general practice at Region Östergötland, who led the current study.

Melanoma can be difficult to differentiate from other skin changes, even for experienced physicians. However, it is important to detect melanoma as early as possible, as it is a serious type of skin cancer.

There is currently no established AI-based support for assessing skin lesions in Swedish healthcare.

“Primary care physicians encounter many skin lesions every day and with limited resources need to make decisions about treatment in cases of suspected skin melanoma. This often results in an abundance of referrals to specialists or the removal of skin lesions, which in the majority of cases turn out to be harmless. We wanted to see if the AI support tool in the app could perform better than primary care physicians when it comes to identifying pigmented skin lesions as dangerous or not, in comparison with the final diagnosis,” says Panos Papachristou, researcher affiliated with Karolinska Institutet and specialist in general practice, main author of the study and co-founder of the company that developed the app.

And the results are promising.

“First of all, the app missed no melanoma. This disease is so dangerous that it’s essential not to miss it. But it’s almost equally important that the AI decision support tool could acquit many suspected skin lesions and determine that they were harmless,” says Magnus Falk.

In the study, primary care physicians followed the usual procedure for diagnosing suspected skin tumours. If the physicians suspected melanoma, they either referred the patient to a dermatologist for diagnosis, or the skin lesion was cut away for tissue analysis and diagnosis.

Only after the physician decided how to handle the suspected melanoma did they use the AI-based app. This involves the physician taking a picture of the skin lesion with a mobile phone equipped with an enlargement lens called a dermatoscope. The app analyses the image and provides guidance on whether or not the skin lesion appears to be melanoma.

To find out how well the AI-based app worked as a decision support tool, the researchers compared the app’s response to the diagnoses made by the regular diagnostic procedure.

Of the more than 250 skin lesions examined, physicians found 11 melanomas and 10 precursors of cancer, known as in situ melanoma. The app found all the melanomas, and missed only one precursor. In cases where the app responded that a suspected lesion was not a melanoma, including in situ melanoma, there was a 99.5% probability that this was correct.

“It seems that this method could be useful. But in this study, physicians weren’t allowed to let their decision be influenced by the app’s response, so we don’t know what happens in practice if you use an AI-based decision support tool. So even if this is a very positive result, there is uncertainty and we need to continue to evaluate the usefulness of this tool with scientific studies,” says Magnus Falk.

The researchers now plan to proceed with a large follow-up primary care study in several countries, where use of the app as an active decision support tool will be compared to not using it at all.

Source: Linköping University

Harnessing Technology to Improve Tuberculosis Outcomes

Dr Phathokuhle Zondi, Clinical Lead: Unu Health

Few realise the extent of the global burden of tuberculosis (TB) or know how many people still succumb to this disease every year. The Centres for Disease Control in the United States estimates that two billion people – a quarter of the world’s population – may be infected with TB, with 10.6 million becoming ill each year. Although TB is preventable and treatable, around 3 500 people lose their lives to it every day, making up an annual mortality rate of 1.3 million people. This means that TB ranks third to only COVID-19 and HIV/Aids as the world’s most deadly infectious disease. 

These statistics are alarming and demand immediate attention from all sectors of society. It is crucial to recognise the potential of technology and digital platforms in revolutionising treatment outcomes. By harnessing the power of innovation, we can transform the way in which TB is diagnosed, treated and managed, ultimately saving lives and reducing the burden of this disease.

Equally as sobering is the fact that around 30 percent of people who become ill with TB are missed by healthcare screenings and do not get the care they need, leading to poor outcomes and an increased spread of the disease, especially in remote, rural and underserved communities. People infected with TB do not necessarily become ill but can pass on the bacteria that causes the infection to between ten and fifteen other people through coughing, sneezing or the transfer of saliva. Approximately 10% of those infected go on to develop an active form of disease at some time in their lives.

TB in South Africa

In South Africa, the first-ever National Tuberculosis Prevalence Survey, published in 2018, found that the country is one of 30 countries with the highest prevalence of TB in the world. When adjusted for population size, it is often ranked as the country with the highest prevalence in the word.

The power of digital healthcare has the potential to change this scenario radically. The greatest challenges we face are the low rate of diagnosis and poor access to – and compliance with – treatment. That’s where digital platforms have such a significant role to play.

How digital can make a difference

Digital health platforms have the potential to revolutionise the fight against TB by improving early detection, enhancing treatment adherence and strengthening healthcare delivery systems. Through the integration of mobile applications, telemedicine, artificial intelligence (AI) and big data analytics, we can address the key challenges of TB diagnosis, treatment access and patient support.

Firstly, digital tools enable early detection and diagnosis of TB cases. Advanced imaging techniques, supported by AI algorithms, can swiftly identify TB-related abnormalities in medical images, facilitating prompt intervention and preventing the progression of the disease. Predictive analytics can also forecast TB outbreaks and hotspot areas, enabling healthcare authorities to take proactive measures to contain the spread of the disease.

Secondly, digital health platforms facilitate remote consultations and monitoring, which is particularly beneficial for patients in remote or underserved areas. By providing timely medical intervention and personalised support, these platforms promote treatment adherence and improve patient outcomes.

Thirdly, mobile health applications empower patients to actively participate in their care management. Through features such as medication reminders, digital health checks and access to educational resources, individuals can adhere to treatment protocols better, ultimately contributing to improved health outcomes.

In addition, digital health platforms streamline healthcare delivery by facilitating data interoperability and real-time monitoring of TB trends. Innovative technologies such as TB Check, the free service application of the South African National Department of Health, are revolutionising TB testing as they are being used to determine the risk of contracting TB and to provide guidelines on how to access testing and treatment.

Further, applications such as One Impact, a comprehensive digital health platform, connects individuals with TB support groups, provides access to TB services and enables the reporting of difficulties in accessing care. By leveraging such platforms, national TB programmes can gain valuable insights into the needs and concerns of affected communities, leading to more responsive and effective service delivery.

TB is treatable and curable, especially when patients are diagnosed early, have access to the medication they need and can be carefully monitored throughout their treatment programme.

As we observe World TB Day on 24 March, it is encouraging to know that the integration of digital health platforms provides immense promise in transforming TB outcomes. To realise this potential, collaboration among governments, healthcare providers, technology companies and civil society organisations is essential. By prioritising investment in innovative solutions and leveraging digital technologies, we can accelerate progress towards the elimination of TB and save countless lives. It is time to harness the power of technology to combat TB and create a healthier, TB-free world for all.

Is AI a Help or Hindrance to Radiologists? It’s Down to the Doctor

New research shows AI isn’t always a help for radiologists

Photo by Anna Shvets

One of the most touted promises of medical artificial intelligence tools is their ability to augment human clinicians’ performance by helping them interpret images such as X-rays and CT scans with greater precision to make more accurate diagnoses.

But the benefits of using AI tools on image interpretation appear to vary from clinician to clinician, according to new research led by investigators at Harvard Medical School, working with colleagues at MIT and Stanford.

The study findings suggest that individual clinician differences shape the interaction between human and machine in critical ways that researchers do not yet fully understand. The analysis, published in Nature Medicine, is based on data from an earlier working paper by the same research group released by the National Bureau of Economic Research.

In some instances, the research showed, use of AI can interfere with a radiologist’s performance and interfere with the accuracy of their interpretation.

“We find that different radiologists, indeed, react differently to AI assistance – some are helped while others are hurt by it,” said co-senior author Pranav Rajpurkar, assistant professor of biomedical informatics in the Blavatnik Institute at HMS.

“What this means is that we should not look at radiologists as a uniform population and consider just the ‘average’ effect of AI on their performance,” he said. “To maximize benefits and minimize harm, we need to personalize assistive AI systems.”

The findings underscore the importance of carefully calibrated implementation of AI into clinical practice, but they should in no way discourage the adoption of AI in radiologists’ offices and clinics, the researchers said.

Instead, the results should signal the need to better understand how humans and AI interact and to design carefully calibrated approaches that boost human performance rather than hurt it.

“Clinicians have different levels of expertise, experience, and decision-making styles, so ensuring that AI reflects this diversity is critical for targeted implementation,” said Feiyang “Kathy” Yu, who conducted the work while at the Rajpurkar lab with co-first author on the paper with Alex Moehring at the MIT Sloan School of Management.

“Individual factors and variation would be key in ensuring that AI advances rather than interferes with performance and, ultimately, with diagnosis,” Yu said.

AI tools affected different radiologists differently

While previous research has shown that AI assistants can, indeed, boost radiologists’ diagnostic performance, these studies have looked at radiologists as a whole without accounting for variability from radiologist to radiologist.

In contrast, the new study looks at how individual clinician factors – area of specialty, years of practice, prior use of AI tools – come into play in human-AI collaboration.

The researchers examined how AI tools affected the performance of 140 radiologists on 15 X-ray diagnostic tasks – how reliably the radiologists were able to spot telltale features on an image and make an accurate diagnosis. The analysis involved 324 patient cases with 15 pathologies: abnormal conditions captured on X-rays of the chest.

To determine how AI affected doctors’ ability to spot and correctly identify problems, the researchers used advanced computational methods that captured the magnitude of change in performance when using AI and when not using it.

The effect of AI assistance was inconsistent and varied across radiologists, with the performance of some radiologists improving with AI and worsening in others.

AI tools influenced human performance unpredictably

AI’s effects on human radiologists’ performance varied in often surprising ways.

For instance, contrary to what the researchers expected, factors such how many years of experience a radiologist had, whether they specialised in thoracic, or chest, radiology, and whether they’d used AI readers before, did not reliably predict how an AI tool would affect a doctor’s performance.

Another finding that challenged the prevailing wisdom: Clinicians who had low performance at baseline did not benefit consistently from AI assistance. Some benefited more, some less, and some none at all. Overall, however, lower-performing radiologists at baseline had lower performance with or without AI. The same was true among radiologists who performed better at baseline. They performed consistently well, overall, with or without AI.

Then came a not-so-surprising finding: More accurate AI tools boosted radiologists’ performance, while poorly performing AI tools diminished the diagnostic accuracy of human clinicians.

While the analysis was not done in a way that allowed researchers to determine why this happened, the finding points to the importance of testing and validating AI tool performance before clinical deployment, the researchers said. Such pre-testing could ensure that inferior AI doesn’t interfere with human clinicians’ performance and, therefore, patient care.

What do these findings mean for the future of AI in the clinic?

The researchers cautioned that their findings do not provide an explanation for why and how AI tools seem to affect performance across human clinicians differently, but note that understanding why would be critical to ensuring that AI radiology tools augment human performance rather than hurt it.

To that end, the team noted, AI developers should work with physicians who use their tools to understand and define the precise factors that come into play in the human-AI interaction.

And, the researchers added, the radiologist-AI interaction should be tested in experimental settings that mimic real-world scenarios and reflect the actual patient population for which the tools are designed.

Apart from improving the accuracy of the AI tools, it’s also important to train radiologists to detect inaccurate AI predictions and to question an AI tool’s diagnostic call, the research team said. To achieve that, AI developers should ensure that they design AI models that can “explain” their decisions.

“Our research reveals the nuanced and complex nature of machine-human interaction,” said study co-senior author Nikhil Agarwal, professor of economics at MIT. “It highlights the need to understand the multitude of factors involved in this interplay and how they influence the ultimate diagnosis and care of patients.”

Source: Harvard Medical School

Is Home the Next Frontier for Patient-centric Healthcare?

Photo by Asterfolio on Unsplash

Can technological advances enable a new era of patient-centric healthcare that goes beyond the boundaries of healthcare providers and extends to patients’ homes?

This dynamic is already unfolding in the global healthcare sector, says Nazia Pillay, Partner Head at SAP Africa – and Africa isn’t far behind.

“The emergence of patient-centric healthcare holds immense promise for better patient experiences, greater accessibility, and improved healthcare outcomes,” says Pillay. “Supported by rapid advances in a range of complementary technologies and driven by a growing need to expand healthcare access, the adoption of patient-centric healthcare models represents the next step in the evolution of healthcare provision.”

Flipping the healthcare model

Until now, healthcare service models have required that patients navigate through often-complex systems to receive diagnoses, treatment and medical advice. Patient-centric healthcare reimagines this dynamic, building systems around the needs and preferences of the patient and prioritising the quality of their experience.

A 2021 report by KPMG found that 79% of healthcare CEOs believed the sector needed to take a more patient-centric approach in order to better respond to patient needs and preferences. However, only 31% rated their organisation’s ability to do so as ‘excellent’.

“A patient-centric healthcare approach prioritises elements such as patient experience and multi-dimensional team engagement, leading to a more holistic patient engagement, explains Johann Joubert, CEO at Converge Solutions. “This approach also makes healthcare more accessible and affordable as the patient can receive expert services in the comfort of their homes. Home-based patient-centric healthcare also benefits the whole ecosystem as the hospital bed becomes available to patients who require more intensive care, while the overall cost of healthcare delivery can be driven downward.”

He adds that, to achieve this, healthcare providers must consider what might be viewed as non-conventional investments in technology to drive innovation across patient-centric operations. “The healthcare system, for valid reasons, is slow to innovate, but we cannot stagnate. The future of healthcare must be different, if we want better patient outcomes and more affordable and accessible healthcare services.”

Healthcare access reaches patients’ homes

Global healthcare providers are increasingly shifting to home-based care models that provide primary, acute and palliative care at the patient’s home. “Home-based care represents a golden opportunity to improve the quality of care while also lowering healthcare costs,” says Joubert. “The world is not as it was twelve months ago. Rapid advances in a range of enabling technologies such as AI, connectivity and device mobility have already set new thresholds of digital possibilities. What was science fiction two years ago, will be mainstream in the next twenty-four months.”

Joubert adds that, in his view, connected intelligence and microservices is the way of the future. “To try and do everything yourself would put you at a disadvantage. Instead, we hand-pick our partners and then combine the expertise of each partner to ensure rapid, relevant, affordable healthcare solutions with tangible value.”

Pillay adds: “Healthcare providers are increasingly adopting powerful new technologies ranging from advanced analytics to cloud capabilities, as well as a range of tools to improve planning, human capital management, financial processes and CRM-based technologies to enable the delivery of personalised healthcare. Over the next few years, the focus is likely to shift slightly to include emerging technologies that enable home-based care and diagnosis, such as AI and machine vision.”

Technology building blocks for improved healthcare

A McKinsey study noted the growing impact of several technologies on healthcare systems and services, including Generative AI to boost productivity and content development

However, to achieve this, healthcare providers will need to lay the technological foundation that will enable the integration of new healthcare innovations.

“The digital transformation of the healthcare industry at a global level is being enabled across a range of patient-centric technologies, spanning from improved healthcare data and analytics to smart healthcare operations and greater empowerment of healthcare workers,” says Pillay. “The outcomes of this transformation can be felt across patient engagement, patient diagnosis and the broader patient experience, as well as providers’ ability to convert health data into health insights to drive improved patient outcomes. And considering the acute skills shortage throughout the continent’s health sector, the use of technology to drive better employee experiences and improve talent retention is immensely valuable.”

Growing evidence for patient-centric model

According to Joubert, the evidence for a more patient-centric healthcare model is clear. “We survey more than fifty thousand patients every month and their feedback confirms that patients want more curated information, more medical worker engagement and rapid responses to questions. It is not only about the patient though. Healthcare is a collective effort and as much as our focus is on patient outcomes, this means we need to take the nursing community on the journey with us. Informed and knowledgeable collaboration is critical.”

Joubert points to the rapid recent advances in AI as an opportunity for the healthcare sector, with Generative AI becoming ‘mainstream’ just more than a year ago. “At the moment there are multiple schools of thought. Some argue that we are entering a ‘trough of disillusionment’, where we will realise AI is not the answer to every problem. Others argue that we are only now at very advent of the exponential AI explosion that will erupt over the next twelve to twenty-four months. I believe both views hold merit. AI is certainly not the answer to every problem. As in the case of IoT over the last couple of years, we will get smarter in how we apply the technology and, most importantly, how we do so in an ethical manner.”

He adds that the healthcare sector must embrace digital capabilities or risk becoming irrelevant in the next five years. “The healthcare industry, by virtue of erring on the side of caution and being highly regulated, typically steers away from disruption or transformation. But unless the healthcare providers embrace digital capabilities and explore the best applications of technology to improve healthcare outcomes, they won’t survive the years ahead.”

Getting the Most from AI in MedTech Takes Data Know-How

As a leader in Medical Technology innovation, InterSystems, a pioneer in healthcare data platform development, has learned, understood, and incorporated pivotal insights from its extensive experience in digital health solutions. That experience points up the need to give AI a strong foundation.

We understand the importance of leveraging AI to drive transformative change in healthcare. Our latest white paper, “Getting the Most from AI in MedTech Takes Data Know-How,” dives into the challenges and opportunities facing MedTech companies venturing into the realm of AI. From data cleanliness to privacy and security considerations, we address key issues that MedTech companies must navigate to succeed in today’s rapidly evolving healthcare landscape.

AI in MedTech Takes Data Know-How

The promise of AI in revolutionising MedTech is undeniable. AI in varying forms and degrees is forecasted to save hundreds of thousands of lives and billions of dollars a year. But here’s the catch- AI models are only as good as the data they’re built on. An AI application can sift through large amounts of data from various Electronic Health Record (EHR) environments and legacy systems and identify patterns within the scope of its model, but it can’t identify data that exists outside of those boundaries.

If one asks “What risk factors does the patient have for stroke?”, AI can only answer based on the information that’s there. Sometimes, things get lost in translation, and that’s why interoperability – the ability to exchange information in a way that ensures the sender and receiver understand data the same way is crucial.

InterSystems: Your Data Sherpa:

Ever wondered why some AI models in MedTech fall short? It’s all about the data. This means MedTech companies can’t just lean on their currently used standard but should consider all those in which relevant data is captured in the market or build on a platform that does.

With InterSystems by your side, you gain access to a treasure trove of healthcare data expertise. One of the benefits of our business is that it’s much broader than a single EHR. This means providing software solutions like The HL7® FHIR® (Fast Healthcare Interoperability Resources) offering a comprehensive view of patient data, accelerating development timelines, and delivering tangible results that showcase the value of your innovations.

Clean Data Is a Must

Data cleanliness is key in the world of AI. Pulling data from various sources presents its own set of challenges, from ensuring data cleanliness to reconciling discrepancies and omissions. Raw data is often messy, inconsistent, and filled with gaps like missing labels. If the data fed into an AI model is incomplete and error-ridden, the conclusions drawn from its analysis will be similarly flawed and suspect. Thus, maintaining high standards of data quality is essential to ensure the accuracy and effectiveness of AI-driven insights.

Henry Adams, Country Manager, InterSystems South Africa, says: “InterSystems advocates for robust preprocessing, cleaning, and labelling techniques to ensure data quality and integrity. Our platform keeps track of data lineage, simplifies labelling, and aggregates health data into a single, patient-centric model ready for analysis”.

Privacy, Security, and Reliability: The Sweet Success!

Privacy and security are essential across industries, but they are even more critical for MedTech product developers. Handling sensitive patient data necessitates strict adherence to regulations like HIPAA and GDPR to safeguard patient confidentiality and comply with legal requirements. Beyond regulatory compliance, ensuring privacy and security is crucial for maintaining patient safety, preserving reputation and trust, and fostering collaboration within the industry.

To help MedTech companies comply with regulations and safeguard patient data, InterSystems’ platform meets needs across major deployments, such as a nonprofit health data network and uses techniques like redundant processing and queues built into the connective tissue of their software. Reliable connectivity solutions ensure seamless data exchange, even in the most demanding healthcare environments.

Charting the Course Forward

If you are a MedTech company still struggling to make sense of siloed healthcare data for your AI initiatives? We have the answers-collaboration with the right partner is essential for integrating AI into medical practices. An ideal partner understands the need for data acquisition, aggregation, cleaning, privacy, and security regulations. “With InterSystems as your partner and by your side, you can navigate the complexities of AI integration and drive transformative innovation in healthcare, making MedTech excellence easier to attain,” concludes Adams.

You can learn more about our support for MedTech innovation at InterSystems.com/MedTech.

For more information or to download the guide, please visit!