AI in healthcare | Dogtown Media https://www.dogtownmedia.com iPhone App Development Thu, 18 Jan 2024 22:49:17 +0000 en-US hourly 1 https://wordpress.org/?v=6.6.1 https://www.dogtownmedia.com/wp-content/uploads/cropped-DTM-Favicon-2018-4-32x32.png AI in healthcare | Dogtown Media https://www.dogtownmedia.com 32 32 Healthcare-Focused Language Learning Models (LLMs) https://www.dogtownmedia.com/healthcare-focused-language-learning-models-llms/ Thu, 18 Jan 2024 22:37:22 +0000 https://www.dogtownmedia.com/?p=21319 After reading this article, you’ll: Grasp the significance and potential applications of Language Learning Models...

The post Healthcare-Focused Language Learning Models (LLMs) first appeared on Dogtown Media.]]>

After reading this article, you’ll:

  • Grasp the significance and potential applications of Language Learning Models (LLMs) in healthcare, including their roles in diagnosing patients, enhancing patient communication, improving medical documentation, and streamlining healthcare operations.
  • Understand the emergence of specialized healthcare-focused LLMs such as MedLM, Hippocratic AI, and Meditron, and their capabilities in providing tailored, efficient, and accurate services in various medical settings.
  • Recognize the challenges, considerations, and future prospects of LLMs in healthcare, including issues of accuracy, ethical concerns, integration needs, and the importance of continuous learning and adaptation to maintain relevance and effectiveness in the rapidly evolving healthcare sector.

Healthcare LLMs

Language learning models (LLMs) such as ChatGPT and Bard have rapidly advanced artificial intelligence capabilities in recent years. These large neural networks can analyze human language and generate remarkably human-like text. The healthcare sector has been quick to explore applying LLMs’ unique skills to improve patient outcomes. However, without customization, general-purpose LLMs struggle to adequately understand complex medical concepts and patient populations’ diverse needs.

Healthcare-focused LLMs provide a tailored solution. By pretraining models on vast datasets of medical journals, patient records, and clinical guidelines, developers can better equip LLMs to serve clinicians’ and patients’ needs. Specialized models can more precisely parse health information, answer medical questions, predict outcomes, and generate diagnostic and treatment recommendations.

This article will argue that developing and responsibly deploying healthcare-focused LLMs will allow for more accurate, equitable, and effective integration of AI capabilities into medicine.

What are Language Learning Models in Healthcare?

Language learning models (LLMs) are a class of large neural networks that are trained on vast datasets of text data. They develop a statistical understanding of real-world language which allows them to complete language tasks like analyzing the meaning of passages, answering questions, summarizing texts and more. In healthcare, LLMs offer the potential to parse and generate the highly-specialized, technical language found in patient health records, medical journals, clinical guidelines and other medical texts.

Early healthcare applications of general LLMs demonstrated their raw aptitude for language tasks but also exposed their limitations without medical customization. Developers have since created a variety of specialized models tailored to medicine, including clinical semantics models, symptom checkers, patient history analyzers, risk predictors, and triage and diagnostic assistants. As model architecture advances and healthcare data availability grows, increasingly advanced models are emerging.

Types of LLMs applied in healthcare

LLMs are being customized for nearly every healthcare discipline and specialty. Some major categories include:

  • Public health surveillance models that synthesize population health and outbreak data
  • Imaging analysis models that can detect abnormalities and annotate scans
  • Literature analysis models that rapidly synthesize the latest medical research
  • Clinical workflow assistance models integrated with EHR systems
  • Patient-facing symptom checkers and virtual assistants
  • Disease/condition-specific models focusing on key areas like cancer, diabetes or cognitive decline

The diversity of applications highlights healthcare’s vast demands for language-based AI.

Applications of LLMs in Healthcare

Diagnosing Patients

LLMs promise to drastically augment physicians’ differential diagnostic capabilities by serving as powerful clinical decision support aids. Trained on immense datasets spanning medical literature, patient cases, and provider notes, LLMs can rapidly cross-reference full patient profiles against known diseases and evidence-based guidelines. Within seconds, they can statistically correlate vague initial symptoms and test results to generate likely diagnostic hypotheses and recommend additional confirmatory testing to efficiently arrive at accurate diagnoses.

Improving Patient Communication

LLMs can provide real-time interpretation services during clinical visits, helping patients and physicians discuss medical issues unencumbered by language gaps. Models fluently analyze complex health terminology across languages, accurately interpreting intricate details like symptoms, family histories, medication instructions, test results and treatment considerations. This allows patients to fully disclose concerns and physicians to clearly explain conditions, care plans, and necessary lifestyle changes. As patient populations grow more culturally diverse due to global migration, LLMs become essential to ensuring vital health concepts are communicated properly across linguistic divides in dynamic clinical settings.

Enhancing Patient Understanding and Compliance

LLMs also promise to enhance patient understanding and willingness to undertake treatments by generating personalized education materials tailored to individual backgrounds. For instance, chatbots leveraging LLMs could field patients’ private questions and explain conditions and care regimens in simple terms accounting for personal demographics, histories and preferences. Consent forms could also be dynamically localized based on readability levels, languages, and cultural considerations. By improving comprehension of disease mechanisms and treatment tradeoffs, LLMs can empower patients to make fully informed care decisions, contributing to better adherence and outcomes.

Medical Documentation and Record Keeping

LLMs present immense potential to reduce documentation burdens for physicians through automated and highly accurate medical transcription. Models can listen to clinical conversations or narrations and reliably document key details like family histories, past and current medications, allergies, adverse reactions, diagnoses, treatment directives and additional commentary. Modern integrations with speech recognition technology even allow doctors to fluidly narrate such details on the go, with transcriptions automatically populated into structured fields in electronic records, improving completeness.

Improving accuracy in patient documentation

And as institutional datasets grow, LLMs have an ever-growing trove of patient information to cross-reference chart entries against, helping flag improbable or conflicting documentation for physician review. They can also match patients to relevant public health warnings, clinical trials, or similar cohort outcomes studies based on profile commonalities, improving care personalization.

Benefits of LLMs in Healthcare

Enhancing patient care and safety

By efficiently handling time-intensive documentation and translation tasks, LLMs give physicians more bandwidth to focus on delivering quality, attentive patient care. The models also mitigate risks stemming from language barriers or chart inconsistencies. LLMs analyze population health patterns in real-time as well, helping institutions get ahead of emerging crises and safety issues. As a result, properly developed LLMs can profoundly improve medical accessibility, reduce misdiagnoses, enhance patient satisfaction, and save lives.

Streamlining healthcare operations

On the operational side, LLMs boost efficiency and cost-effectiveness throughout healthcare systems’ vast technical and administrative infrastructure. Models excel at rapidly manipulating coded terminology for functions like medical billing, procedure logging, supply reordering and analytics. They also monitor troves of insurance policies, regulations and clinical best practice guides to optimize workflows, identify cost savings and ensure compliance standards are programmatically upheld. And seamless integration with existing health IT systems is smoothed by LLMs’ programming versatility.

Facilitating international collaboration and research

Additionally, specialized LLMs break down language and geographic barriers that traditionally hindered global medical research and knowledge sharing. Skilled models now rapidly translate emerging study findings and clinical insights among the international research community. They also synthesize vast sets of international medical literature and population health data to identify macro trends and opportunities for cross-institutional trials. The future of worldwide pandemic response and collaborative advancement of personalized treatments both rely heavily on LLMs capacities in this realm.

Supporting diversity and inclusion in healthcare

Finally, thoughtfully constructed LLMs can counteract systemic disparities that lead to unequal quality of care and health outcomes across patient demographics. By ingraining cultural awareness and voices from marginalized communities throughout training, models can help providers better understand patient populations’ diverse needs. LLMs also aid scalable provider training around harmful biases. Over time, sensitively deployed LLMs can directly address healthcare’s severe diversity and inclusion failings.

Healthcare-Focused LLMs in the Real-World

MedLM

Google has recently unveiled MedLM, a groundbreaking collection of foundation models specifically designed for the healthcare industry. This advanced technology is now available to select Google Cloud customers in the United States through the Vertex AI platform. MedLM encompasses two distinct models built upon the foundations of Med-PaLM 2. The first model, larger in size, is tailored for complex healthcare tasks, while the second, medium-sized model, is optimized for fine-tuning and excels in scaling across a variety of tasks. MedLM represents a significant step forward in the application of AI in healthcare, promising to enhance efficiencies and address the growing demands of this vital sector.

Hippocratic AI

Hippocratic AI is a safety-focused LLM designed specifically for healthcare. It aims to improve healthcare accessibility and health outcomes by providing services like dietary advice, medication reminders, answering pre-op questions, onboarding patients, and delivering test results. Hippocratic’s AI model reportedly outperforms leading language models like GPT-4 and Claude on more than 100 healthcare certifications.

Meditron

An open-source LLM specifically tailored for medical applications, Meditron is trained on curated medical data from reputable sources like PubMed and clinical guidelines. It represents a significant advancement as a more focused and potentially more reliable tool for medical practitioners

Challenges and Considerations

Accuracy and reliability issues

Like any AI model, errors or unintended biases in the training data can lead healthcare LLMs to make unsafe medical recommendations. Extensive validation on real-world data is essential to ensure robust, trustworthy performance, especially when models directly guide clinical decisions.

Ethical concerns and privacy

Patient privacy must be secured when developing, training and deploying health LLMs. HIPAA compliance and techniques like data de-identification, encryption and access controls help mitigate risks from potential data breaches or misuse. Ethical standards around informed consent and responsible AI practices also come into play.

Integration with existing healthcare systems

For seamless adoption, LLMs need standardized interfaces and validation protocols to integrate into electronic health records, clinical decision support systems, virtual health assistants and other platforms. Multi-disciplinary collaboration is key.

Continuous learning and adaptation needs

Healthcare evolves extremely quickly, so LLMs require ongoing model updates, retraining and tune-ups to stay relevant. Institutions must decide appropriate cycles and rigor to maintain quality and performance. Diverse user feedback also helps flag areas for improvement.

With deliberate development and coordination across healthcare stakeholders, LLMs can usher in tremendous progress. But the work is never done – responsible maintenance, monitoring and iteration will be crucial going forward.

Future of LLMs in Healthcare

Ongoing advances in model architecture, training techniques and computing power will enable a new generation of incredibly capable and specialized healthcare LLMs. Larger models trained on broad data including multi-modal inputs beyond text have the potential to develop sophisticated reasoning on par with physicians across many medical subdomains. Augmentations like causality detection, confidence calibration, and trustworthiness measures will also be critical to enable safe, transparent clinical deployment.

Over the next decade, LLMs will likely progress from passive clinical decision support aids to semi-autonomous healthcare assistants. Shared control models allowing tools to execute lower risk interventions under supervision, as well as robots leveraging LLMs to fluidly assist surgical procedures or rehabilitation activities, will emerge first. Eventually autonomous LLM-based systems could enable drastic cost savings and standard of care improvements, provided fail-safes and human oversight govern use cases.

The scalable knowledge and pattern recognition capacities modern AI techniques unlock will undoubtedly revolutionize healthcare. However, integrations must always emphasize leveraging predictive analytics to augment, not replace, human expertise and judgement. LLMs should summarize available data and guide clinical inquiries, not make outright decisions without practitioner evaluation of model rationale. Centering human needs through responsible, equitable development of AI will ensure transformative benefit.

With conscientious coordination across healthcare stakeholders throughout the rapid evolution ahead, LLMs technology holds immense potential to heal.

Frequently Asked Questions (FAQs)

  1. What are Language Learning Models (LLMs) in Healthcare? LLMs in healthcare are specialized large neural networks trained on extensive medical data, including patient records, medical journals, and clinical guidelines. They understand and generate medical language, aiding in tasks like diagnostics, patient communication, medical documentation, and improving healthcare operations.
  2. How do LLMs improve patient diagnosis and treatment? LLMs enhance diagnostic accuracy by analyzing vast datasets of medical literature, patient cases, and provider notes. They can correlate symptoms and test results, generating diagnostic hypotheses and recommending confirmatory tests. For treatment, LLMs facilitate personalized patient education and compliance, aiding in understanding disease mechanisms and treatment plans.
  3. What are some examples of specialized healthcare-focused LLMs? Notable examples include Google’s MedLM, designed for complex healthcare tasks and available on the Vertex AI platform; Hippocratic AI, which focuses on safety and accessibility in healthcare; and Meditron, an open-source LLM trained on curated medical data for medical practitioners.
  4. What are the main challenges in implementing LLMs in healthcare? Key challenges include ensuring accuracy and reliability, addressing ethical concerns and privacy, integrating with existing healthcare systems, and the need for continuous learning and adaptation to keep up with the rapidly changing medical field.
  5. What is the future outlook for LLMs in healthcare? The future of LLMs in healthcare includes the development of more capable and specialized models, integrating multi-modal data inputs, and augmenting clinical decision-making. They are expected to evolve from support aids to semi-autonomous healthcare assistants, and eventually, to systems capable of executing lower-risk interventions under supervision. However, these integrations will emphasize augmenting human expertise and judgment, not replacing it.
The post Healthcare-Focused Language Learning Models (LLMs) first appeared on Dogtown Media.]]> The Role and Importance of Data Collection in Healthcare https://www.dogtownmedia.com/data-collection-in-healthcare/ Mon, 15 May 2023 19:55:37 +0000 https://www.dogtownmedia.com/?p=21042 In the constantly evolving landscape of healthcare, data has emerged as a critical component driving...

The post The Role and Importance of Data Collection in Healthcare first appeared on Dogtown Media.]]>

In the constantly evolving landscape of healthcare, data has emerged as a critical component driving transformations that are both profound and invaluable. Data collection in healthcare, an intricate process that involves gathering, analyzing, and interpreting copious amounts of information, has become an indispensable tool in the quest for improving health outcomes, enhancing patient experiences, and ultimately, shaping the future of global health.

The role of data collection extends far beyond the simple act of accumulation; it is the backbone of evidence-based decision-making, a fundamental pillar for quality assurance, and an essential driver of innovative research. In this age of digital revolution, the importance of data collection in healthcare is only amplified, ushering in a new era of data-driven healthcare that promises improved diagnostics, personalized treatments, and a more patient-centered approach to care. Dogtown Media is well versed in developing mHealth apps that can collect and protect patient data. 

What is Data Collection in Healthcare?

 

Data collection in healthcare is the process of gathering, measuring, analyzing, and interpreting various types of information regarding patient health, healthcare services, and healthcare outcomes. This data can come from numerous sources, such as electronic health records (EHRs), medical imaging, laboratory results, patient surveys, wearable technology, and insurance claims, among others.

In a broad sense, the scope of data collection in healthcare includes but is not limited to:

Clinical Data: This includes patient medical histories, diagnoses, treatment plans, imaging, lab tests, and medication data. It is mainly collected from EHRs and used to provide and improve patient care.

Administrative and Claims Data: This is data about the utilization of healthcare services, including visits, procedures, and prescriptions, as well as costs, patient demographics, and insurance coverage. It’s used for billing, insurance reimbursement, and managing healthcare operations.

Patient-Generated Data: This type of data is provided directly by patients, often through wearable devices or patient surveys. It may include data about lifestyle, wellness, and health behaviors.

Research and Registry Data: This data is collected in the course of clinical trials or other research studies, or from disease registries. It can be used to advance medical knowledge and develop new treatments.

Social Determinants of Health Data: This refers to information about the conditions in which people are born, grow, live, work and age that can affect a wide range of health outcomes.

Why is Data Collection Important in Healthcare?

Enhancing Patient Outcomes

Effective data collection in healthcare is crucial to enhancing patient outcomes. It enables early identification of diseases and conditions, which leads to timely and targeted interventions. In the case of chronic diseases such as diabetes or heart disease, early detection can significantly improve patient prognosis.

Data collection also plays a vital role in personalizing treatment and care. By collecting and analyzing patient data over time, healthcare providers can tailor treatments to individual patients, taking into account their unique health histories, genetic profiles, lifestyle factors, and preferences. This personalization can lead to more effective treatments and improved patient satisfaction.

Furthermore, data collection contributes to improving patient safety. By tracking adverse events and other patient safety indicators, healthcare organizations can identify patterns and trends, implement changes to prevent future incidents, and monitor the effectiveness of those changes.

Optimizing Healthcare Processes

Data collection is equally important in optimizing healthcare processes. It can help reduce medical errors and adverse events by providing accurate and up-to-date patient information, identifying potential risks, and highlighting areas for improvement.

Data-driven insights can also help streamline clinical workflows. For instance, predictive analytics can forecast patient demand and help with scheduling and resource allocation, thereby reducing wait times and improving patient flow.

Additionally, data collection can lead to improved resource utilization and efficiency. By tracking the use of resources such as hospital beds, medical equipment, and staff time, healthcare organizations can identify inefficiencies and implement strategies to optimize resource use. This can lead to cost savings and improved quality of care.

Best Practices for Data Collection in Healthcare

As healthcare organizations continue to expand and innovate, the importance of high-quality data collection has become increasingly essential. Accurate data collection is necessary for informed decision-making and improved patient outcomes. Here are some best practices for data collection in healthcare.

Standardizing Data Collection

Standardizing data collection protocols is the first step towards ensuring that data is collected accurately. Standardized protocols reduce the risk of errors caused by inconsistent data collection practices across different healthcare providers. Additionally, using standardized data collection tools helps in collecting data that is consistent, reliable, and can be easily analyzed.

Regular staff training on data collection processes is essential. It ensures that all healthcare professionals understand the importance of data collection and how to collect it accurately. Training programs should focus on the use of standardized data collection tools, data privacy and security requirements, and the use of technology in data collection.

Using Health Information Technology (HIT) for Data Collection

The adoption of electronic health records (EHRs) has revolutionized how healthcare organizations collect and manage patient data. EHRs enable the secure storage and retrieval of patient information in a digital format. The use of mobile health (mHealth) technologies for remote data collection has also improved the accuracy and efficiency of data collection.

Healthcare organizations can also integrate HIT systems for efficient data management. This integration enables automated data capture and analysis, making the process much faster and more accurate. HIT systems provide real-time insights into patient data, which enables healthcare professionals to make informed decisions about patient care.

Ensuring Data Privacy and Security

Ensuring data privacy and security is crucial when collecting patient data. Healthcare organizations must comply with regulatory requirements such as HIPAA, which governs the use and disclosure of protected health information. Implementation of appropriate security measures such as encryption is also necessary to prevent unauthorized access to patient data. Regular audits and assessments help identify potential vulnerabilities and ensure that data is protected adequately.

Healthcare organizations need to implement best practices for data collection to ensure that patient data is accurate, reliable, and secure. Standardizing data collection protocols, using HIT systems, and ensuring data privacy and security are all important steps towards achieving this goal. By prioritizing these best practices, healthcare professionals can continue to improve patient outcomes and provide high-quality care.

The Future of Data Collection in Healthcare

The future of data collection in healthcare points towards increased sophistication, digitalization, and personalization. With advancements in technology, we are likely to see greater use of data analytics, artificial intelligence, and consumer-driven data collection. Dogtown Media has developed a variety of AI apps that utilize healthcare data. 

Data Analytics and Artificial Intelligence

Data analytics and artificial intelligence (AI) are already having a profound impact on healthcare, and their role is only set to grow in the future. Data analytics involves the systematic computational analysis of data or statistics, allowing healthcare providers to draw insights and make informed decisions. Coupled with AI, data analytics can transform the way we understand and interpret healthcare data, leading to numerous benefits for patients, healthcare providers, and the overall healthcare system.

The integration of AI can greatly enhance data analysis capabilities. Machine learning, a subset of AI, can be used to analyze complex healthcare data sets, identify patterns and trends, and make predictions about future outcomes. For instance, AI algorithms can analyze patient data to predict the likelihood of readmission or the risk of complications, helping clinicians to intervene early and prevent adverse outcomes. The use of AI in data analysis also holds the potential to dramatically speed up the process of diagnosing diseases by analyzing medical images or laboratory results.

Moreover, data analytics and AI are integral to the development and implementation of precision medicine. Precision medicine is a healthcare approach that tailors treatment and preventive strategies to individuals based on their genetic makeup, lifestyle, and environmental factors. By analyzing genomic data along with other personalized health information, healthcare providers can identify the most effective treatments for each patient, thereby improving treatment outcomes and reducing the risk of adverse effects.

Consumer-Driven Data Collection

In the future of healthcare, consumers – or patients – will play an increasingly active role in data collection. Advances in technology have led to the proliferation of wearable devices and sensors, such as fitness trackers and smartwatches, that can collect a wealth of health-related data. This includes data on physical activity, heart rate, blood pressure, sleep patterns, and more, much of which would not typically be captured in a clinical setting.

These technological advancements are empowering patients to take control of their health data. They can monitor their health status in real-time, track their progress over time, set health goals, and make informed decisions about their health and lifestyle. Furthermore, patients can share this data with their healthcare providers, contributing to a more comprehensive and holistic view of their health and enabling more personalized and proactive care.

Increased patient engagement and ownership of health data is likely to have far-reaching implications for healthcare. It can lead to greater patient satisfaction, as patients feel more involved and informed about their health. It can also lead to better adherence to treatment plans and improved health outcomes, as patients can monitor their progress and make adjustments as needed.

Additionally, consumer-driven data collection can contribute to research and public health initiatives. The rich, real-world data collected by patients can be used to study patterns and trends in health behaviors and outcomes, identify risk factors for diseases, and develop effective interventions. It can also provide valuable insights into the social determinants of health, which are the conditions in which people live, learn, work, and play that affect their health.

In the contemporary healthcare setting, the role of data collection is undeniably monumental. It serves as a robust bridge between the realm of theoretical knowledge and the practice of medicine, making it the fulcrum around which patient care, research, and policy revolve. The benefits of data collection are manifold, facilitating everything from individualized care to global health initiatives, and acting as a conduit for scientific advancements.

As we navigate the intricacies of the healthcare ecosystem, the importance of data collection becomes increasingly evident. It is the heartbeat of evidence-based decision making, the fuel for innovation, and the key to unlocking the potential of personalized medicine. As we continue to harness the power of data, we inch closer towards a future where healthcare is not only reactive, but also predictive, proactive, and precision-oriented.

The journey of understanding healthcare data is vast and complex, but it is undeniably critical to the evolution of healthcare. As we stand on the precipice of a new era in medicine, we must continue to champion the collection and use of quality data, ensuring its place at the heart of all healthcare discussions. For it is through this commitment to data-driven decision making that we can usher in a new age of healthcare—one that is rooted in evidence, empowered by data, and dedicated to the improvement of health outcomes for all. Dogtown Media is a custom mobile app development company that can help you better understand and utilize healthcare data for improved patient outcomes.

May Find Interesting

 

The post The Role and Importance of Data Collection in Healthcare first appeared on Dogtown Media.]]> 5 Important mHealth Development Considerations Every CEO Should Known https://www.dogtownmedia.com/5-important-mhealth-development-considerations-every-ceo-should-known/ Fri, 29 Apr 2022 04:39:11 +0000 https://www.dogtownmedia.com/?p=20190 mHealth – a subcategory of telecommunications defined by the use of mobile phones to facilitate...

The post 5 Important mHealth Development Considerations Every CEO Should Known first appeared on Dogtown Media.]]> mHealth – a subcategory of telecommunications defined by the use of mobile phones to facilitate healthcare-related services – is very likely the future of healthcare. Spurred by the global pandemic, mHealth has witnessed astonishing growth over recent years, so much so that upwards of 65% of patients agree that mobile healthcare will be a great asset toward care delivery. As a dedicated mHealth app developer with a presence in New York hyper-focused on delivering top-tier mHealth applications, these evolutions in mHealth space couldn’t be more important to our organization.

As evolving market conditions continue to drive mHealth adoption, mobile app development has taken the mobile market by storm. Here, emerging mHealth apps offer key services such as facilitating patient-doctor communication, reminding patients to take medication, monitoring health metrics remotely or even enabling patient access to critical healthcare information. This results in a decrease in physical visits to the doctor’s office and opens a world where patients can access care anywhere around the world. 

As a dedicated provider of mHealth iPhone app developer for healthcare organizations with a presence in Los Angeles, New York and London, we want to use this opportunity to share 5 important development considerations every CEO should take into consideration when bringing a new app to the market. 

1.Addressing User Needs:

One of the most foundational aspects of bringing an mHealth application to the market is addressing user needs. With so much evolving in what patients are comfortable regarding remote healthcare services, it’s critical to invest in adequate market research to ensure your concept is something the market is ready for. The best way to do this? Bringing an MVP (minimum viable product) to your market in an alpha or beta test to assess market response. Here, it’s critical that this MVP is developed in an agile and swift iterative manner so as to test the validity of a solution early. Gain information quickly and iterate appropriately.

2. Longevity: 

Technology is moving fast, but mHealth apps that meet a critical need have been shown to stand the test of time. Take, for example, MyFitnessPal. This app that enables users to track the meals they eat throughout the day has been around for years. Now, as one of the top promoted health apps for Apple, it has an astonishing 1.4M ratings. This acts as a perfect example of a mHealth app that delivers a critical market need and thus has stood the test of time.

3. Effective UI/UX:

When bringing an application to the market, it’s not simple enough to simply deliver an effective solution but to do so in a way that is initiative and easy to use. UI/UX – design concepts standing for user interface and user experience – is a huge component to any newly designed application. We like to put it this way, you can have the best solution or the best intentions with an app, but if the user interface or user experience stifles a user’s ability to reap the value of the application, it’s likely that the app will fall behind other competitive applications that exceed in the category of value and the UI/UX.

4. Simplicity: 

Simplicity is king when it comes to bringing applications to the market, independent of industry or use case. That said, one of the key priorities organizations should consider when considering developing an application or working with a third party in bringing an application to the market is to prioritize simplicity. For a basic design scaffolding, most mHealth applications require some sort of user portal or user login, followed by some dashboard to facilitate the basic value of the application. Nailing these two key components in a simplistic manner is a major key to success for any mHealth application.

5.Cross-Device Accessibility: 

One often overlooked aspect of bringing an application to the market is cross-platform accessibility. Often, an organization can have a strong presence on one type of device or platform and neglect emphasizing development that is compatible across other operating systems or platforms. Our advice is to work with a trusted partner who has experience in supporting cross-platform compatibility.   

Next Steps

Understanding these key development considerations when bringing a mHealth application to the market is critical.
Working with a trusted and experienced app developer to integrate these trends into an innovative app is a winning strategy. Dogtown Media has launched over 200 apps, and counting, with an expertise in mHealth design.

If you’re interested in learning how Dogtown Media can help bring your solution to the market contact us. We’d love to help!

The post 5 Important mHealth Development Considerations Every CEO Should Known first appeared on Dogtown Media.]]> Dogtown Media Supports Connected Health Initiative’s Request for Biden-Harris Administration to Combat COVID-19 With Digital Health Tech https://www.dogtownmedia.com/dogtown-media-supports-connected-health-initiative-request-biden-harris-combat-covid-19-digital-health/ Wed, 25 Nov 2020 18:00:57 +0000 https://www.dogtownmedia.com/?p=15777 The coronavirus pandemic has wreaked unfathomable damage on the lives of Americans and the country’s...

The post Dogtown Media Supports Connected Health Initiative’s Request for Biden-Harris Administration to Combat COVID-19 With Digital Health Tech first appeared on Dogtown Media.]]>

The coronavirus pandemic has wreaked unfathomable damage on the lives of Americans and the country’s economy. To move forward in the right direction, we must take new and drastic action to address this crisis. This includes employing cutting-edge developments in medical technology to the best of our abilities.

Dogtown Media fully supports the Connected Health Initiative’s request for the Biden-Harris administration to extend the existing declaration of a public health emergency (PHE) so that the use of connected health technology is available to all U.S. citizens during this time of need.

Innovation Can Help Flatten the Infection Curve

It has become painfully clear that we must leverage connected medical technologies such as telehealth if we are to ever beat COVID-19. But antiquated regulations will stand in the way unless the declaration of PHE is extended. The Connected Health Initiative (CHI) aims to make that happen.

An initiative of ACT | The App Association, CHI is a coalition of healthcare industry stakeholders and partners that strives to lead efforts to effect policy changes that allow medical providers to harness the power of technology in order to improve patient engagement and outcomes. Its steering committee consists of the American Medical Association, Apple, Intel Corporation, Microsoft, Dogtown Media, and many other notable figures in the tech and healthcare space.

medical app developer

CHI recently sent a letter to the Biden-Harris administration that not only congratulates them on their victory in the 2020 Presidential Election but also urges them to continue to use digital health technologies such as telemedicine, remote patient monitoring (RPM), artificial intelligence (AI), and other modalities to defeat the coronavirus pandemic. Each of these innovative modalities allow us to implement effective and necessary measures to flatten the infection curve in the United States.

Telehealth Technology Is Key to Managing The COVID-19 Crisis

Alongside CHI, we believe that congressional action focused on permanent telehealth policy changes can help provide immense relief from the damage caused by the COVID-19 pandemic. Such changes would play an integral role in defeating this crisis. Not only this, but permanent policy changes for connected healthcare tools can also help to modernize American medical laws so that they correctly reflect the value that these paradigms offer. We also think that Congress should prioritize providing all Americans with the high-speed broadband infrastructure needed to use these digital health tools.

Of course, we know that pursuing these congressional efforts can take time. So it’s critical that the Secretary of Health and Human Services (HHS) continues to extend the existing declaration of PHE. This provides millions of Americans with the allowance to use connected health technology. During the COVID-19 pandemic, telehealth and RPM have been invaluable in preventing, diagnosing, and treating American citizens as we all adhere to social distancing guidelines. These must not only be maintained throughout the entirety of the pandemic but also built upon so that this country is better equipped to handle future health crises.

Should the PHE expire before the Biden-Harris administration is in place, the new HHS Secretary under them should immediately reinstate it.

Actions That Can Help Us Fully Leverage Digital Health Tools

CHI’s letter to the Biden-Harris administration is meticulous in detail and spans several suggestions. Below, we’ve outlined some of the most crucial ones:

The Centers for Medicare & Medicaid Services (CMS) should ensure that Federally Qualified Health Centers (FQHC) and rural health clinics (RHC) can provide RPM services.

Both FQHCs and RHCs are key actors on the frontlines of America’s medical ecosystem. Thus, they should be able to monitor key patient-generated health data (PGHD) metrics for the populations they are serving on a permanent basis. This includes those receiving treatment for COVID-19.

CMS should provide Anti-Kickback Statute relief for digital health.

Many clinicians are remotely monitoring COVID-19 patients. This has raised concerns that any equipment or access to software platforms provided free of charge could inadvertently trigger Anti-Kickback Statute (AKS) liability. The CHI has requested that HHS Office of the Inspector General (OIG) provide clarity that access to software platforms for PGHD or telehealth at low or no cost doesn’t violate the AKS.

HHS should provide certainty with regard to HIPAA’s application to various remote technologies during the PHE.

The HHS’s Office of Civil Rights (OCR) recently announced enforcement discretion for the Health Insurance Portability and Accountability Act (HIPAA) that clarifies that the use of private, secure telehealth tools which aren’t part of the provider’s official offerings will not draw a penalty as long as the provider makes their patients aware of the risks. CHI is urging OCR to issue guidance that certain telehealth tools are merely “conduits” and thus don’t require business associate agreements (BAAs). The guidance should also clarify that the providers of these telehealth services should only store electronic protected health information (ePHI) temporarily.

HHS should leverage AI-enabled technology to combat the COVID-19 pandemic.

There’s no doubt that AI has incredible technology to augment healthcare by preventing hospitalizations, reducing complications, and improving patient engagement. Unsurprisingly, public health experts and providers are already using AI to combat COVID-19. This has given rise to a variety of opportunities and challenges for U.S. policymakers to consider (e.g., bias, inclusion, and transparency). As a coalition with many leading developers of AI, CHI urges for the design of healthcare AI systems to be informed by human-centered design, real-world workflow, and end-user needs.

Unrecognizable woman doctor using tablet in office with double exposure of creative ncov coronavirus covid 19 treatment and vaccine search icons. Toned blurry image

COVID-19 Will Come To an End

From New York City to our hometown of Los Angeles, the COVID-19 pandemic has brought unprecedented tragedy and hardships to many of our lives. The Biden-Harris administration represents an opportunity for America to tackle this problem in a new and better way.

We stand by CHI’s request of the President-Elect and Vice President-Elect to fully leverage telehealth and other emerging technologies to mitigate this crisis. With time, proper guidance, and unity, this too shall pass.

The post Dogtown Media Supports Connected Health Initiative’s Request for Biden-Harris Administration to Combat COVID-19 With Digital Health Tech first appeared on Dogtown Media.]]> 5 Health Tech Trends Accelerated by COVID-19 https://www.dogtownmedia.com/5-health-tech-trends-accelerated-by-covid-19/ Wed, 16 Sep 2020 15:00:22 +0000 https://www.dogtownmedia.com/?p=15534 The COVID-19 crisis is accelerating technological innovation across a multitude of fields, and healthcare is...

The post 5 Health Tech Trends Accelerated by COVID-19 first appeared on Dogtown Media.]]> medical app developerThe COVID-19 crisis is accelerating technological innovation across a multitude of fields, and healthcare is no exception. Because we are working against the clock for a solution to this pandemic, it’s the right time to start experimenting with new methods and expand the capabilities of emerging technologies. Experienced business leaders agree: per a recent Accenture survey of 259 payer and provider executives, more than 50% of respondents say that rapid advancements in science and emerging technologies are going to disrupt healthcare.

Here are five healthcare applications that emerged before the pandemic but are now being accelerated by the coronavirus.

The Patient Experience

The vast majority (85%) of leaders polled think that technology has become inseparable from the human experience. And they’re correct; patients expect more from digital experiences today across retail, social media, and even healthcare services like online appointment booking and telehealth appointments.

medical app developer

Patients want more personalized experiences from their healthcare providers, and they want to feel important and seen. Not only that, but they also want to feel protected by the healthcare system. 70% of healthcare consumers polled were concerned about commercial tracking and data privacy in their online behaviors, activity, interests, and location. 70% of healthcare consumers polled said they expect their relationship with technology to become more prominent in the next three years.

Dr. Kaveh Safavi, a Chicago-based physician and lawyer, is a senior managing director of Accenture’s Health team. He says that the COVID-19 pandemic is accelerating the intersection between healthcare experiences and digital technology. According to Dr. Safavi, “Leading the future of care will demand rethinking core assumptions about the intersection of people and technology.”

Because people’s relationship with and perceptions of technology are evolving, the healthcare system must adapt by redesigning digital experiences.

AI in Healthcare

Many leading healthcare organizations are using AI and other algorithmic technology to improve their existing workflows and automate their operations processes. With well-designed human-AI collaboration, the patient and provider experience can blossom into a fruitful and long-term relationship.

medical app developer

Accenture’s research found that 69% of healthcare executives polled are already adopting or piloting artificial intelligence (AI) applications. Patients interfacing with AI will experience fluid interactions between themselves and the machine.

But we must be careful to consciously design AI medical applications with human-centered design principles and easy-to-use features. Indeed, only 39% of executives polled said they’ve included human-centric design principles or inclusive design to support a large variety of patient interactions.

A Growing Internet of Medical Things

The Internet of Things (IoT) encompasses a wide variety of devices, sensors, software, and industries. And now there’s a subset of IoT development that includes the vast amount of equipment, sensors, thresholds, and procedures involved in healthcare and medicine. Industries outside of healthcare are beginning to launch products that can be updated with the ability to expand experiences and services in the future, which affords customers flexibility when their needs, expectations, and demands change.

Healthcare should be the next industry to offer this new type of product to customers, working to offer patients adaptable products and ecosystems that can accommodate ongoing changes and updates. This type of “co-ownership” product, wherein the company and the customer are connected closely throughout the lifetime of the product, is becoming indispensable in the race against the pandemic.

medical app developer

Disruptive DNA Innovation

Innovation DNA differs from regular human DNA in that it has nothing to do with genetics. Instead, innovation DNA refers to an organization as it grows to accommodate the future: scientific advancements that are disruptive but discrete; maturing digital technology that is accessible and more commoditized; and the use of emerging technologies like blockchain, augmented and virtual reality, AI, and quantum computing to scale rapidly.

Helpful Robots

Robots are no longer confined to warehouses and factory floors. They’re a major lifeline for hospital staff these days, offering sanitization, vital measurement, and visitor information for hospital patients and visitors. With the imminent rollout of 5G technology and the ever-decreasing cost of hardware, robotics will become more and more ubiquitous outside of the warehouse and other production facilities.

71% of the executives polled think that robotics will enable the next generation of products and services for patients in the physical world. In healthcare, staff is stretched extremely thin already, so there is no end to the potential for robotics applications. But 54% of executives polled believe that their employees will face challenges in figuring out how to work with them. Dr. Safavi adds, “As robotic capabilities extend beyond controlled environments, healthcare organizations will face new challenges around talent investments, data collection, and human-machine interaction and collaboration.”

medical app developer

Healthcare’s Finally Evolving With the Times

Technology, especially outside of healthcare, is evolving and becoming more necessary for our lives. That’s why it is so impactful when applied to healthcare: it augments and greatly improves a service of which we’re already lifelong customers. 78% of leaders polled said they believe the stakes for innovation are the highest they’ve ever been, and “getting it right” is going to necessitate new innovations with organizations across many industries.

The COVID-19 pandemic is accelerating technology to break new boundaries more than ever before. To stay in the game, healthcare organizations must explore and apply emerging technologies to their products and services to improve the patient and medical experience.

What do you think of COVID-19’s effect on medical technology? As always, let us know your thoughts in the comments below!

The post 5 Health Tech Trends Accelerated by COVID-19 first appeared on Dogtown Media.]]> AI: Our Best Shot at Longer Lives? https://www.dogtownmedia.com/ai-our-best-shot-at-longer-lives/ Thu, 18 Jun 2020 15:00:23 +0000 https://www.dogtownmedia.com/?p=15216 Artificial intelligence (AI) is just getting started in healthcare, and venture capitalist Dmitry Kaminskiy thinks...

The post AI: Our Best Shot at Longer Lives? first appeared on Dogtown Media.]]> Artificial intelligence (AI) is just getting started in healthcare, and venture capitalist Dmitry Kaminskiy thinks it’s the perfect tool to extend the human life span. He’s incredibly passionate about longevity and knows quite a lot about the human aging process, too.

From inflammatory proteins to senolytics research (involving suspending cells in cell cycle arrest), Kaminskiy’s trying to move the needle on the trillion-dollar human life extension industry. And AI may be the engine he needs to do so.

A New Perspective on AI and Longevity

AI wasn’t seen as a promising medical application until just a few years ago. Likewise, the longevity industry didn’t gain traction until recently, mainly because many experts were skeptical. According to Kaminskiy, applying AI and data science to medicine was considered extremely futuristic. But with steady improvement over the years, we’re starting to see AI compete with some of the best doctors in the world.

Research firm CB Insights says that AI in healthcare investments are booming; the industry is growing faster than almost any other sector of the economy. Last year, promising AI medical startups garnered nearly $1.6 billion in funding. $550 million of these investments came from one organization: London-based mobile app health service provider Babylon Health.

The company uses AI to make recommendations for patients by collecting data, analyzing it, and finding comparable matches. A substantial amount of its investment funds went towards Insilico Medicine and Juvenescence, two companies that focus on longevity research and development.

The True Cost of Health Problems

Insilico Medicine has become known for its innovative AI applications that use reinforcement learning and general adversarial networks to speed up the drug discovery process. The company recently published an industry-changing paper about how its AI generates a drug candidate in 46 days.

Alex Zhavoronkov is the Co-Founder and CEO of Insilico Medicine. He believes that extending human life spans would let humans lead more productive lives and help bolster the global economy as well. To understand how, we must examine current costs.

The US healthcare market is controlled by insurance and pharmaceutical companies, making it the most expensive medical system in the world. US citizens spend more money on healthcare than any other country. But life expectancy for US citizens is dropping. A study by the American Medical Association found that American life expectancy has decreased since 2014 for young and middle-aged adults. The causes range from societal to health-related.

According to the Milken Institute, in 2016, the US spent $1.1 trillion on chronic diseases with cardiovascular conditions, diabetes, and Alzheimer’s being the most costly diseases to treat. When you take into account lost economic productivity, the cost jumps from $1.1 trillion to $3.7 trillion — almost 20% of the national GDP.

Investing in Unprecedented Life Spans

Kaminskiy sees enormous benefit in extending human longevity for the economy and financial institutions. Of course, it’s paramount that we include a variety of support for those living longer lives. After all, it isn’t simply about quantity; quality in terms of healthier functionality and better productivity are integral aspects to consider.

He’s co-authored a book with Margaretta Colangelo, who is a managing partner at Deep Knowledge Ventures. The company launched an investment fund, longevity.capital, that specializes in investing in the human life extension industry. According to Kaminskiy, there are probably 20 similar investment funds that focus on investing in longevity and companies.

At the AI for Longevity Summit in London in November, Kaminskiy and his team announced the launch of the Longevity AI Consortium, an initiative that allows academics and industry professionals to collaborate at the King’s College London. The goal is to provide an AI Longevity Accelerator program that interfaces between UK investors and startups.

Deep Knowledge Ventures already committed $9 million to the accelerator program over the next three years. The company also wants to establish similar programs across the world.

Delaying the Aging Process

Franco Cortese is a partner at longevity.capital, and he directs the Aging Analytics Agency, which focuses on publishing papers to accelerate research and development in human life extension. One recent paper details an outline of Biomarkers for Longevity.

Biomarkers can indicate underlying issues like disease or a general decline in the patient’s health due to aging. They’re measurable data points that can be used universally. For example, BMI is a biomarker that indicates whether a patient is obese, a quality that brings about a host of health issues. Other biomarkers include the measurement of telomeres, which are the protective caps of our chromosomes that shrink as we age over time.

It’s imperative that we compile enough data from a variety of biomarkers so that analyses yield ways we can use them to improve longevity. Some researchers are focused on a “magical cure,” but Kaminskiy believes the answer lies in the biomarkers of aging.

With biomarker tracking, eventually, patients could see their own biomarker health and progress. Sensors would measure different biomarkers and alert us to a potential incoming case of the flu or warning signs of diabetes.

For this vast amount of data generation, AI will be essential to organize, make sense of, and analyze the data. And when used to find new drugs, AI would be instrumental in finding new treatments, therapies, and dosage amounts to help patients maintain bodily homeostasis.

AI Is Still Young

Kaminskiy imagines the data generated from biomarker sensors will be uploaded to a cloud computing system that monitors them in real-time. He says sophisticated AI algorithms are needed to make longevity work on a large scale as well as at the individual patient level.

It’s difficult to gauge how far we are from promising findings in this field. AI itself is still quite young. But it presents immense hope for a healthier future for humanity. Teams around the world are invested in making this combination of healthcare and technology work. Hopefully, we see breakthroughs before we become too old to benefit from them.

What do you think of the human life extension industry? Do you think AI will lead the way towards life-changing discoveries? Would you want to live to 200 years old? As always, let us know your thoughts in the comments below!

The post AI: Our Best Shot at Longer Lives? first appeared on Dogtown Media.]]> The Biggest Ways 5G Will Impact Healthcare https://www.dogtownmedia.com/the-biggest-ways-5g-will-impact-healthcare/ Thu, 19 Dec 2019 16:00:21 +0000 https://www.dogtownmedia.com/?p=14546 5G will bring the next big mobile transformation; with more stable connections, faster data transfer...

The post The Biggest Ways 5G Will Impact Healthcare first appeared on Dogtown Media.]]> 5G will bring the next big mobile transformation; with more stable connections, faster data transfer speeds, and 10x the speed of current mobile data connectivity, the way we interact with our medical providers is going to change. No longer will we have to travel to see a doctor or visit a hospital. Instead, telemedicine, remote at-home monitoring, and improved spatial computing mean we can keep our pajamas on while we undergo a check-up.

For many clinics and healthcare facilities at the moment, the cost of unlimited data does not beat current operating costs. Additionally, the strain that healthcare puts on current network speeds and latency is massive, and it seriously impacts patients’ experiences and outcomes. This issue isn’t unique to healthcare, and it affects any company doing business with IoT (the Internet of Things) implemented in its operations.

But 5G promises to repair the network strain, improve patient experience, and introduce more seamless transfer for large files. Here are five ways that 5G will help healthcare organizations provide better experiences for patients and their providers.

1. The Doctor Will See You… Right Now!

Video telecommunication can be laggy and difficult to connect in rural areas. Even in urban areas that have slow Internet speeds, video chatting quality isn’t great. But with 5G and IoT, telemedicine is forecasted to grow at a compound rate annually of 16.5% from 2017 to 2023. These huge growth numbers are attributed to the increase in demand for healthcare in rural areas and government initiatives in implementing 5G and IoT across the nation.

In telemedicine, patients and providers cannot afford any miscommunication through broken audio or laggy video; for most patients, this technology requires 5G as a foundational system upon which it can grow and expand. With telemedicine, patients can get faster treatment, prescription orders, and appointments with specialists.

2. Better AR, VR, and Spatial Computing

We know that robotic surgery has existed for the past decade, but with the addition of AR, VR, and spatial computing, doctors can train independently to offer less invasive and more innovative treatments.

Many critically ill and mentally unwell patients have been trialed with AR and VR headsets to introduce post-operative therapy and relaxation exercises. This approach is working well for many patients, and it can save these patients the discomfort and stress of physically going into an office once a week.

Dallas-headquartered AT&T is one of the mobile companies at the forefront of 5G, and they’re working with VITAS Healthcare to study how 5G-enabled AR and VR affect patient engagement. AT&T’s ultimate goal is to lessen anxiety and pain for patients with terminally ill conditions by giving them distracting and calming content through their AR and VR headsets.

Even if the patient is video conferencing a doctor across the world, a translator could join and act as the intermediary communicator between the patient and provider.

3. Sending Files Faster

Generating giant images with the tiniest details showing clearly is very important when doctors are trying to find signs of cancer, bone loss or break, and other conditions. Although an MRI can take the photo, it is often sent to a specialist in a different facility for analysis. This data transfer costs time, money, and network resources. Often, the image takes hours to upload, only to output a notification that it could not successfully send the image.

For the patient, however, waiting for news about potential cancer or immediate surgery is nerve-wracking. The image transfer process creates a bottleneck in the process for both the provider and the patient.

With 5G, however, sending large image files or large patient electronic medical records should become a thing of the past. We will be able to stably and reliably send massive amounts of data to specialists all over the country with 5G technology. In particular, for rural and elderly patients, this will transform their experience with medicine and doctors by introducing faster access to care and better quality of care.

Jason Lindgren is the CIO of Austin Cancer Center, which has a PET scanner that creates massive files: up to 1 gigabyte per patient per study. This adds up very quickly, but Lindgren says that 5G has helped the Austin Cancer Center overcome this bottleneck.

“We used to have to send the files after hours,” he says. “Now, as soon as the patient leaves the scanner, the study is already on its way. It’s beneficial to doctors because they can get the results that they need quicker.”

4. Remote At-Home Monitoring in Real-Time

Wearables are growing in popularity with consumers, and these connected devices will be key in remote monitoring. Wearables are projected to save hospitals 16% in costs over the next five years.

Moreover, an Anthem study found that 86% of doctors surveyed said that wearables increase patient engagement with their health and care.

With 5G and IoT, providers and insurance companies can monitor and encourage patients remotely and in real-time while gathering data to incorporate into the patient’s treatment plan. This data generation can also help personalize care, and providers can rest assured knowing that the data is accurate and current.

5. AI’s Impact

AI is already helping healthcare analysts determine patient treatment patterns, find potential diagnoses, and predict which patients will have postoperative complications. By giving each patient a risk score, providers can devote more time to those who need it. And for those patients who are determined to have preventable issues, providers can work earlier to mitigate any risks.

5G will allow AI algorithms to expand their training datasets, add new data in real-time, and run analyses concurrently without taxing the facility’s network.

Healthcare app developers can also create data-rich dashboards for providers to check using their mobile devices.

2020 and Beyond

5G will introduce so many different medical applications that it’s hard to fathom what they’ll be. The possibilities are truly endless for innovation in this space. With 5G in healthcare, we look forward to better quality of care, patient experiences, and patient outcomes.

Costs should go down as a result, and providers can (hopefully) expect to experience a much better work-life balance, while treatment and care will become more preventative, personalized, and predictive.

How do you think 5G will impact healthcare? Which of the items on this list was your favorite? As always, let us know your thoughts in the comments below!

The post The Biggest Ways 5G Will Impact Healthcare first appeared on Dogtown Media.]]> AI in Healthcare: The Opportunities & Challenges Ahead — Part 2 https://www.dogtownmedia.com/ai-in-healthcare-the-opportunities-challenges-ahead-part-2/ Mon, 07 Oct 2019 15:00:28 +0000 https://www.dogtownmedia.com/?p=14266 The world of healthcare is highly specialized; providers must acquire specific knowledge and expertise in...

The post AI in Healthcare: The Opportunities & Challenges Ahead — Part 2 first appeared on Dogtown Media.]]> The world of healthcare is highly specialized; providers must acquire specific knowledge and expertise in a subset of medicine to excel in their chosen specialty. In contrast, the world of artificial intelligence (AI) development often takes a more holistic approach. Although AI can be focused on one area, it can find patterns and abnormalities from a large set of data taken from a variety of users and devices.

When these two fields collide, they can produce beneficial and innovative solutions. In a previous post, we discussed how AI is a huge helping hand for healthcare providers. Not only is it helping to address the needs of patients in underserved areas, but it’s also helping identify diseases faster and even assisting surgeons in the operating room. In case you missed it, you can read it here.

But with these new solutions come new challenges. Many of these difficulties weren’t predicted, while others have become intensified through the integration of these two fields. In this follow-up post, we’ll cover the challenges, biases, and cybersecurity concerns of integrating AI into healthcare.

Treading Carefully Into AI Innovation

Dr. Bob Kocher, MD, teaches at the Stanford University School of Medicine; he warns that “if we are not careful, AI could…unintentionally exacerbate many of the worst aspects of our current healthcare system.”

This isn’t a warning to stop integrating AI into health applications; instead, it’s a caution to tread slowly and carefully. Not everything is rainbows in AI or healthcare, and bringing the two fields together requires finesse and a critical mindset.

Often, when adding AI into any industry, many developers only see the opportunities and benefits without paying enough attention to the risks, security, and life-changing effects on users. But in order for AI innovation to usher in a smarter era for healthcare, strong consideration must be given to these potential pitfalls.

Inaccurate Diagnoses

Because AI uses data across patient populations to draw conclusions, find patterns, and alert us of any abnormalities, we have to place quite a bit of trust in it. A human could never mentally aggregate that amount of data on his or her own within their lifetime.

But the quality of an AI algorithm depends heavily on the quality of its training data. If data is outdated, sourced from a small group of patients, or not enough for sufficient training, it can cause problems without alerting providers or developers of the issue.

If any of these possibilities occur, AI could incorrectly diagnose patients, and it’s up to doctors to take the technology’s recommendation with a grain of salt. It may be spot-on, but without an extensive review of the patient’s medical and family history, procedures, and past diagnoses, the provider cannot trust the AI with a 100% level of confidence.

Perpetuating Prejudices

AI algorithms can be racist, sexist, classist, and even ageist. They lack a fundamental understanding of humans and their brains, thoughts, and emotions. AI lacks compassion, empathy, and sympathy. Without a built-in criticizing engine, the AI won’t ever doubt its result, either.

In a study by MIT News, three AI algorithms had up to a 34% error rate due to skin-color biases. These facial analysis algorithms performed the worst for dark-skinned women, creating a considerable risk for missing diagnosis and treatment for skin cancer.

Dr. Rebecca Pearson is the Chief Technology Officer of Chicago-based ThoughtWorks. She stresses that biases in AI algorithms are mostly always unintentional. Many of these biases are a result of the actual biases we have in our current healthcare system. Therefore, economic and social biases in algorithms are troubling because they help perpetuate the cycle through future technology and gathered data.

To adequately address these biases in algorithms and the healthcare system, experts recommend that both doctors and AI developers take an interest in sociology, economics, family dynamics, and other people- and money-based fields. Cultivating a greater understanding and more sympathy for different groups of patients can vastly improve biases in technology and the exam room.

Cybersecurity Risks

Just as any computer in a medical office or hospital needs regular security updates, so do all AI applications. Because these applications often contain massive amounts of sensitive patient data, cybersecurity concerns are a significant facet of AI technology. It’s easier for a doctor to not blab about their patients’ conditions than it is for an AI application to keep that information stored away perfectly safely.

As such, AI applications need regular maintenance; their code needs to be brought to the most up-to-date security and AI standards often. According to a study by ScienceDaily, AI innovation creates a threat to patients’ personal and health data.

AI Is a Tool, Not a Replacement

One thing is for sure: AI shouldn’t ever take over every aspect of healthcare; it can be a great supplement of knowledge and analytics for providers, but the provider must have the last say in the treatment, surgery, or diagnosis. We like to say AI’s intelligence is “book smart, but not exactly street smart,” and that’s where humans are needed to fill the gaps.

Ultimately, integrating AI into healthcare won’t be fast and it won’t be easy; hiccups will happen, and they will surely make us doubt whether this technology has a place in medicine or not. We’ll need to have multiple regulatory parties checking over AI health applications regularly to ensure risks remain diminished. We’ll also certainly need more research and case studies into AI in healthcare. More education for every stakeholder involved, whether that be patients, providers, or developers, is a necessity.

And as the co-founder of the Machine Intelligence Research Institute, Eliezer Yudkowsky, says, “By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.” We must not allow AI 100% control or authority over any aspect of the healthcare system until it’s proven itself multiple times.

Now that you’ve gotten an in-depth look at both the benefits and challenges that AI brings to healthcare, what do you think of this technology’s future in this field? Let us know your thoughts in the comments!

Do you have an idea for a disruptive medical device, but you don’t know where to begin? Dogtown Media is an FDA-compliant developer with extensive experience in bringing health tech innovations to life.

Contact us today for a Free Consultation!

The post AI in Healthcare: The Opportunities & Challenges Ahead — Part 2 first appeared on Dogtown Media.]]> 3 Ways Artificial Intelligence Is Transforming Healthcare (Part 4) https://www.dogtownmedia.com/3-ways-artificial-intelligence-is-transforming-healthcare-part-4/ Mon, 16 Sep 2019 15:00:07 +0000 https://www.dogtownmedia.com/3-ways-artificial-intelligence-is-transforming-healthcare-part-3-copy-2/ If you’ve been keeping up with our “AI Transforming Healthcare” series, you’ve learned about some...

The post 3 Ways Artificial Intelligence Is Transforming Healthcare (Part 4) first appeared on Dogtown Media.]]>

If you’ve been keeping up with our “AI Transforming Healthcare” series, you’ve learned about some cool ways that AI is impacting the medical field; selfies as diagnostic tools, electronic health records as risk predictors, and better brain-computer interfacing are just a few examples we covered. In case you missed our previous entry, you can get up to speed here.

In this fourth and final part of the series, we’ll go over how AI is improving cancer treatment, enhancing access to resources for patients in rural and underserved areas, and creating better end-of-life tools for elderly and ill patients.

Advanced Immunotherapy for Cancer Treatment

Cancer attacks the immune system and stops it from attacking tumor cells. As a result, immunotherapy is one of the best cancer treatment methods right now. It strengthens the patient’s immune system and helps stop tumors from spreading. Unfortunately, most patients don’t respond to immunotherapy, and oncologists don’t exactly know why.

An AI application could potentially help elucidate this quandary; oncologists could identify patterns in patients who do respond well to immunotherapy and figure out if it’s a viable treatment option for others. But this all depends on the data.

According to Dr. Long Le, MD at the MGH Center for Integrated Diagnostics in Boston, the key to success is massive amounts of patient data. Any patient undergoing new therapies should opt into sharing their data so that other patients can benefit. Sharing patient data between hospitals, states, and even countries could further accelerate therapy results.

And the field is still growing with knowledge and new experimental treatment options, making things much more complicated. “Recently, the most exciting development has been checkpoint inhibitors, which block some of the proteins made by some types of immune cells. But we still don’t understand all of the disease biology. This is a very complex problem,” he says.

Addressing a Lack of Resources

During a 2018 World Medical Innovation Forum (WMIF) panel, speakers pointed out that more radiologists work in Boston’s hospitals than in all of West Africa. And this disparate spread of doctors isn’t the only problem. We’re facing a worldwide shortage of doctors, technicians, and radiologists. Lack of information and knowledge kills more people than disease alone.

With AI, some diagnostic tools could be replaced to give the current batch of doctors time to see more patients. Jayashree Kalpathy-Cramer, Ph.D., thinks this type of technology has immense potential to increase access to healthcare.

But, as Dr. Ziad Obermeyer, an Assistant Professor of Emergency Medicine at Brigham and Women’s Hospital, also touched upon, these algorithms must not contain any bias or single-population training data. They must, if possible, include data from patients all over the world and in every socioeconomic class.

Kalpathy-Cramer says that disease and populations can vary wildly from country to country, which underscores the importance of an unbiased, fully-trained algorithm. “As we’re developing these algorithms, it’s very important to make sure that the data represents a diversity of disease presentations and populations – we can’t just develop an algorithm based on a single population and expect it to work as well on others.”

AI’s Bedside Manner

Healthcare’s shift to focusing on the baby boomers entering old age will bring with it a new set of challenges and requirements. Predicting disease, risk factors, and worsening symptoms are the main priorities for any doctor, but AI can help speed up processes.

Using predictive analytics and decision-making tools, providers can be alerted to patient issues well before the patient comes in for a visit. Not all illnesses are able to be monitored 24/7, but more common conditions, like sepsis or seizures, can be tracked with an algorithm that’s fed tons of complex data.

AI will help support decision-making about continuing care for patients whose condition has deteriorated or are critically ill. For example, patients in a coma after cardiac arrest are unable to speak for themselves. In situations where family members aren’t available or alive anymore, AI can help determine whether the patient could pull out of the coma or if they should go peacefully.

Dr. Brandon Westover, MD, says that doctors are required to check over the EEG data visually. But it takes a lot of time, and the accuracy rate varies between each provider; as such, patients may receive differing opinions from various doctors, which can be incredibly stressful.

“In these patients, trends might be slowly evolving. Sometimes when we’re looking to see if someone is recovering, we take the data from ten seconds of monitoring at a time. But trying to see if it changed from ten seconds of data taken 24 hours ago is like trying to look if your hair is growing longer,” he says.

However, AI can shed new light on patients’ conditions. Using an algorithm and varied training data across many patient populations, AI can find patterns and generate actionable insights for providers. Subtle signs of improvement could go unrecognized by a tired doctor, while an AI algorithm could detect it immediately, giving the patient a better chance of recovery.

Smarter Healthcare Is Coming to a Hospital Near You

We hope you’ve enjoyed this series on how AI is transforming healthcare. This technology is already having a profound effect on health tech development. But this is just the beginning. With opportunities to improve cancer treatment options, access to resources, and monitoring of patients, the possibilities with AI are really endless.

Which AI application discussed in this series has the most potential to advance medicine? What other areas of healthcare are in dire need of some AI innovation? As always, let us know your thoughts in the comments below!

The post 3 Ways Artificial Intelligence Is Transforming Healthcare (Part 4) first appeared on Dogtown Media.]]> 3 Ways Artificial Intelligence Is Transforming Healthcare (Part 3) https://www.dogtownmedia.com/3-ways-artificial-intelligence-is-transforming-healthcare-part-3/ Mon, 09 Sep 2019 15:00:32 +0000 https://www.dogtownmedia.com/?p=14186 Welcome to the third part of our series focusing on the countless ways that artificial...

The post 3 Ways Artificial Intelligence Is Transforming Healthcare (Part 3) first appeared on Dogtown Media.]]>

Welcome to the third part of our series focusing on the countless ways that artificial intelligence (AI) is revolutionizing medicine. In our previous article, we discussed how AI is impacting electronic health records (EHRs), how it can leverage EHRs as risk predictors, and how it’s helping to assess the risks of antibiotic resistance. In case you missed it, you can read it here.

In this article, we’ll cover how AI is enhancing medical devices, wearables, and even making it possible to use selfies as diagnostic tools.

Smart Medical Machines and Devices

Intelligent medical devices are used to monitor patients’ health and stability in the intensive care unit, the emergency room, and during surgery. With AI, the devices are being augmented to alert doctors faster about risk factors, abnormalities in recorded data, or complications after invasive surgery.

Dr. Mark Michalski, MD, is the executive director at the MGH & BWH Center for Clinical Data Science in Boston. He says that AI is truly the best fit for the job. “When we’re talking about integrating disparate data from across the healthcare system, integrating it, and generating an alert that would alert an ICU doctor to intervene early on – the aggregation of that data is not something that a human can do very well.”

Boosting an AI system with more advanced algorithms can add more power to its ability to monitor patients and their conditions while reducing cognitive load for providers, supply and medication budgets for hospitals, and billing amounts for insurance companies.

Wearables Help Patients and Providers

The consumer market for wearables and smartphone health apps is booming. The options are endless, and data is generated on the go, all day, for a multitude of data points. With all of this data, a standard data dashboard can get boring for the patient really quickly. But personalized recommendations, updates sent to the provider, and encouragement from AI can improve the data analytics in wearables.

And AI excels when it’s fed tons of data. Using data across multiple patients to find a pattern can uncover insights into population health. It would allow AI to compare healthcare populations across locations and socioeconomic backgrounds.

Often, the major cause of concern for patients is data privacy. Because wearables monitor patients around the clock, there isn’t much the patient has control over. Dr. Omar Arnaout, MD, says that because of the Cambridge Analytica data privacy scandal with Facebook, society is not as lenient with data privacy as it used to be. Now, he says, patients are more careful about what types and who they share their data with.

On the other hand, he says, patients trust their providers more than companies like Facebook. “There’s a very good chance [wearable data will have a major impact] because our care is very episodic and the data we collect is very coarse. By collecting granular data in a continuous fashion, there’s a greater likelihood that the data will help us take better care of patients.”

Selfies as Diagnostic Tools

As smartphone cameras become more robust and powerful, many medical experts and AI developers believe that selfies hold a treasure trove of information waiting to be mined for insights. Some fields already using selfies as supplemental data include dermatology and ophthalmology. These images will be especially important for populations in underserved areas and in developing countries.

Hadi Shafiee, Ph.D., says that smartphone selfies introduce a lot of potential for providers. “This is a great opportunity for us. Almost every major player in the industry has started to build AI software and hardware into their devices. That’s not a coincidence.”

In the U.K., researchers created a tool that finds possible developmental diseases from a photo of a child’s face. The algorithm looks for specific features, like the eyes, jawline, nose, and more. If anything looks abnormal, it could mean the child has a craniofacial abnormality. The tool currently works to identify more than 90 disorders.

In underserved areas without specialized doctors and facilities, photos from larger cities and hospitals of lesions, infections, wounds, medications, and more provide more information to accurately diagnose and help their patient.

Shafiee says that we generate tons of data every day, to the tune of 2.5 million terabytes of data. With smartphones, manufacturers are using that data in conjunction with AI to provide a faster, smarter, and more personalized user experience. “There is something big happening,” says Shafiee, who believe that we can take advantage of smartphone and AI technology to help fix problems at the provider and patient level.

A Healthier Future

AI’s foray into healthcare is going to power new tools, empower doctors and patients, and create greater efficiencies in the healthcare system.

Because of this technology’s ability to self-optimize over time, any health application leveraging it will only continue to improve. Our diagnostics, care, treatments, and outcomes will become cheaper, faster, and safer. Everyone wins with AI in healthcare.

What’s your favorite wearable right now? How does it leverage AI’s capabilities? And what do you think about the potential of selfies to improve diagnoses? Let us know in the comments below!

The post 3 Ways Artificial Intelligence Is Transforming Healthcare (Part 3) first appeared on Dogtown Media.]]>