Category: Healthcare

Unveiling the Tech Trends Reshaping the Healthcare Industry in 2023

Posted on by Mehala

Introduction

In the dynamic landscape of the medical world, innovation knows no bounds. As we step into 2023, we begin to experience a new level of tech advancement in every domain. From innovations to personalized treatment, the future of medicine holds immense progress. Let us explore the top technology trends shaping a brighter future for all. 

Healthcare Trends in 2023

Who would have imagined healthcare advancement once a challenge could today become a reality? To stay ahead of the competition, one should be updated about breakthroughs. Before knowing about top trends, let us have a look at their impact:

Improved healthcare access

Accessing healthcare to all is one most common barriers. Delayed treatments are due to high price, and distance. Telemedicine enables patients to receive treatments in the comfort of their homes. 

Workflow optimization

Tedious paperwork and long wait times are the reasons for failure in timely and coordinated care. Workflow optimization streamlines paperwork and ensures smooth department transitions. Instant patient information access improves decision-making and team coordination in healthcare.

Better diagnosis & treatment

Diagnosis is bettered in every way – from accuracy to accessibility. With access to advanced tools, diagnosis and treatment of an illness are more efficient. For instance, physicians provide accurate health results by using wearable technology. 

Patient engagement

Prior to advancement, patients have to rely on healthcare experts for guidance. Communication relied on an in-person visit resulting in a communication gap. Technology allows for secured messaging, virtual consultations, and electronic health records. Patients may monitor their health, obtain personalized medical information, plan appointments, and receive health reminders through online health portals, smartphone applications, and wearable devices. This enhanced involvement promotes patient education, self-management, and treatment plan adherence.

By embracing the following trends, healthcare organizations can stay ahead of the curve with better healthcare and profits. 

Artificial intelligence 

Staffing shortage is a prime concern in the healthcare sector. AI bridges the gap between staffing shortages and automates tedious tasks. Tasks like data entry and documentation are automated. AI frees healthcare personnel’s time to focus on more critical aspects of patient care by easing their duties.

AI promptly delivers a complete patient history to doctors. AI-based remote monitoring devices analyze the presented data and alert in case of any abnormalities. AI-driven chatbots can diagnose health issues quickly. Automating routine tasks – appointment schedules, and other administrative tasks. Chatbots significantly reduce organizational costs. Even while handling multiple conversations, they ensure consistent information delivery. Other AI solutions include – digital consultation, healthcare system analysis, and helping therapy.

Remote patient monitoring

RPM addresses limited healthcare access for remote individuals by providing monitoring, counseling, and assistance without in-person visits. Home health monitoring reduces unnecessary medical visits, saving money. RPM monitors health data, enabling timely intervention and care. It detects early signs, enables proactive interventions, reduces complications, and improves patient outcomes.

RPM makes use of specialized IoT devices, and apps connected to these devices are used to collect data. These alert doctors immediately in case of abnormalities. This helps to reduce the strain on healthcare resources. Look for the best cloud-based AI services, as they can deliver extra value.

Internet of medical things

Managing and accessing large volumes of data in any industry is a challenge. A centralized platform for data integration, storage, and analysis is the best solution. It can promote efficient data management, supporting data-driven decisions. With less or no human interaction, IoMT can provide healthcare services. People can gain real-time data access with the help of trackers. Doctors, for their part, receive notifications and participate in patients’ care. This new technology generates a wealth of data, including sleep patterns, eating habits, average activity levels, and much more. 

Robotics process automation

Managing multiple accounts of the same patient during settlement is challenging. Manual calculations are prone to errors, making it a tedious task. Healthcare workflow involves numerous processes – patient details, inventory management, and much more. Healthcare providers must source miscellaneous data from dispersed technology systems to receive reimbursement from insurance companies.

RPA manages accounts in one location, allowing quick calculations with just a few clicks. RPA simplifies the healthcare insurance claim process. Automation ensures that the process is done promptly and that needed compliance is satisfied. 

Blockchain technology

Unauthorized access is still a concern in any domain. Information stored within the database needs to be protected. Blockchain is a game changer, making the transaction completely transparent. Transactions are shared only with authorized networks, making them safe and secure. Blockchain streamlines and improves the efficiency of medical research. It enables the secure sharing of anonymized patient data for research purposes while protecting patient privacy.

Cognitive automation

Cutting-edge technology that combines AI, ML, and RPA to streamline efficiency. Cognitive Automation solutions are beginning to make their presence known in hospitals. They can boost accuracy, and patient care, and minimize human error. Hospitals may free up one of their most valuable resources, human capital, by implementing these techniques. Find the top cloud-based AI services with SolutionChamps.

Conclusion

In 2023, innovations revolutionized medical diagnosis, treatment, and management, transforming diagnostic, and management methods. 

Firstly, technology optimizes workflows, increasing efficiency and productivity. Secondly, technology improves patient engagement, and loyalty in healthcare through interactive platforms. Additionally, technology helps healthcare companies overcome staffing shortages. Moreover, technology facilitates data-driven decisions contributing to overall healthcare companies’ growth.

Focused on catching the best tech trends for your organization? Our team of specialists is prepared to assist you in each stage of the process. 

Top 5 AI Trends in Healthcare (2023)

Posted on by Mehala

Introduction

The integration of technology within the healthcare sector opens up fresh opportunities for both healthcare professionals and patients, fundamentally reshaping the industry. It achieves this primarily through task automation, thus freeing up valuable time for the staff. Let’s explore in-depth the five prominent AI trends that are reshaping the healthcare industry, along with their significant advantages.

AI Trends in Healthcare 

Technologies such as AI, ML, 5G, and IoT are a few examples of advancements in the medical field. Here are a few trends of AI that are advancing the healthcare industry and driving digital transformation:

Robot-assisted Surgery

This is one of the most common applications in the healthcare sector. AI and collaborative robots have advanced surgical procedures. The robotic assistance enhances precision, control, and flexibility during operation. The benefits include: 

  1. Less pain and blood loss
  2. Quick recovery, making a less hospital stays for patients
  3. Small or unnoticeable surgery scars 

Drug Discovery 

With AI in drug discovery already commenced, experts expect more AI adoption in the drug discovery process. With the promise of short development time and lower cost, it can treat incurable diseases at the earliest. 

Medical Image Analysis

Medical image analysis by AI is far superior to traditional image analysis methods. AI algorithm helps diagnose, monitor, and treat complex medical conditions. They are widely used to:

  1. Identify any complex pattern in the imaging data
  2. Detect different image modalities during various stages of medical treatment including tumors. 
  3. Discover various disease characteristics that cannot be detected by the human eye.

Telehealth

With the onset of the pandemic, everyone prefers to have a safer alternative for in-person visits. With more and more people using telehealth, providers could spend more time with their patients. This is part of a shift towards a more patient-centric approach to care, which has been proven to improve patient outcomes. Though the patient won’t visit the healthcare provider in person, they can still seek medical attention, and in some cases, a prescription. This time-saving approach allows patients to secure same-day appointments, eliminating the need for them to wait for days to visit in person.

Hospital Management

Routine administrative tasks – scheduling appointments, and medical inventory optimization are time-consuming. Handling a large data volume and providing quality care is quite challenging.

The AI-powered platform helps to optimize hospital operations. They can be integrated with the existing workflow and offer customized solutions. This approach could reduce downtime, and ensure uninterrupted, high-quality patient care. AI can be used to look at healthcare information and figure out fraudulent activities, if any.

Benefits of AI Trends

Right from business to government sectors, implementing AI offers various advantages. AI in healthcare has numerous benefits: 

Better Patient Care

The deployment of AI in the healthcare sector provides one of the most promising advancements – better patient care. 

For instance, when a doctor diagnoses a patient with an illness, the treatment typically involves a prescription for medication. In the realm of digital therapeutics, the physician may additionally prescribe multimedia-rich videos. These videos serve a dual purpose by comprehensively explaining the patient’s condition and offering clarity on the prescribed medication. They guide the patient on when and how to take the medication. They can inform them about potential side effects, making the treatment process more informative.

Increase Workflow Efficiency

Managing vast patient data is a complex task. AI can help healthcare providers streamline data entry, scheduling, and retrieval, so they can get the information they need quickly. Organizations can complete a wide range of tasks by AI-powered workflow automation. 

Better Data-driven Decision 

The healthcare sector needs to put patient care at the center. However, when required data is unavailable, we cannot derive the best result. 

The effectiveness of big data along with AI supports clinical treatment. Under heavy schedules, it may be difficult for doctors to find necessary documents. Rather than manual finding, AI assists in this process. AI helps in collecting the necessary documents from the cloud upon request. With the correct information at hand, better decisions can be made. 

Saves Time

Doctors need to spend more time on documentation works for each patient. This is one time-consuming burden laid on doctors. 

Deploying AI in healthcare can help doctors and staff to free up their time to prioritize other tasks. AI completes major works with fewer or zero user clicks. These include: 

  1. Clinical documentation
  2. Medical record management 
  3. Billing and claim processing 
  4. Revenue management 

Billing and claim processing are tedious tasks for administrators. When AI is used in billing and claim settlement, the process gets streamlined while reducing turnaround time. Tech companies are working together with healthcare organizations to streamline operations better. 

Personalized Treatment Plan

Every treatment plan can be compared with a roadmap. One common roadmap cannot offer the right way to every destination. 

With precise treatment care, there has been significant progress in patient health. It can analyze a vast amount of data, including lifestyle choices, genetic makeup, and other factors to develop personalized treatment plans. This in turn offer various benefits to healthcare organization – increased revenue, and better patient-therapist relationships. 

SolutionChamps specializes in delivering cutting-edge AI software development services, empowering businesses across diverse industries.

Early Diagnosis 

In the medical field, it is important to identify certain medical conditions at the earliest to mitigate their severity and facilitate effective management. Early-stage detection simplifies the treatment and control process. 

AI monitors the patient’s health behavior, thereby detecting the changes at the early stage. Early disease detection can include various advantages – reducing patient costs, the need for expensive tests, and improving quality care and the outcome of patient life. These in turn can reduce the burden for healthcare organizations. 

Conclusion

The inclusion of AI in the healthcare industry is a trend that is worth recognizing. AI-infused technologies help to drive incredible value to healthcare organizations and patient care. This not only presents an opportunity within the profession but also assists in dealing with complex tasks and aids in automation. As accuracy and efficiency continue to improve over time, let’s delve into the potential of AI in healthcare and explore its future advantages. Elevate your business capabilities with our AI software development services, where we harness the power of artificial intelligence to create tailored solutions that drive efficiency and foster innovation.

Digital Transformation in Healthcare: Challenges & Solutions

Posted on by Mehala

Introduction

To become more competitive and survive in the digital era, one should know how to tackle challenges in adopting technology. In traditional industries like healthcare, a digital transformation is a complex procedure. But digital transformation can include major benefits – enhanced security, optimize workflow, and increased profits. A company needs to have a clear strategy to implement advancement.

Many security challenges hinder tech evolution. There are several challenges associated with digital transformation in healthcare. 

Tech challenges & solutions

Data security

Challenge: When it comes to digital transition, cyberattacks are a significant concern. One may consider malware attacks on hospitals to be low-risk. Yet, the hospital will lose its reputation among common people. Digital transformation leads to increased patient data, which should be protected. 

Solution: Appropriate security measures are necessary. With the right security solutions, data against threats can be protected. 

Investing in robust cybersecurity measures is the right option. Cybersecurity can deal with: 

  • Use modern cryptography to encrypt data during storage. 
  • For access, fingerprint, face, or retina recognition. This will protect data from illegal activities. 
  • Allow robust spam filters to block phishing emails.

Blockchain – a digital transaction technology built on a decentralized network of computers can help with:

  • Patients can now view and share their health data
  • No security breaches are possible
  • Detect inaccuracies in patients’ medical records

AI necessitates vast volumes of data, raising issues about security. It is essential to safeguard patient data from unauthorized access. The industry needs to provide patients with a choice over the usage of data.

Lack of transparency

Challenge: Lack of transparency – the difficulty in comprehending how AI arrives at predictions. It leads to distrust and resistance among healthcare practitioners. Patients may be sceptical of AI-driven healthcare interventions due to a lack of interpretability. It leads to unease and uncertainty.

Solution: Professionals need to have confidence in the algorithms they use to make critical decisions, and transparency to ensure accuracy. Without this, they may hesitate to adopt AI and ML technologies. AI that provides insight into decision-making processes can increase trust. Document AI modelling helps to improve transparency. It involves independent evaluation and scrutiny of AI to understand its limitations, strengths, and potential biases. 

Independent third-party organizations can evaluate and certify AI technologies based on predefined criteria and standards. It provides an additional layer of transparency and ensures reliability, accuracy, and fairness.

Budget concerns

Challenge: Adopting new technology in the healthcare industry could be expensive. Smaller healthcare companies with limited budgets could find it challenging. The cost mainly depends on the type of technology, its complexity, and the size of the company. Expenses can be like:

  • Implementation cost: It includes installation and configuration of software and hardware. Furthermore, staff training for adoption will be incurred.
  • Maintenance cost: After implementation, maintenance takes the lead. Charges can increase over time, resulting in a significant financial burden.
  • License cost: The license should be renewed periodically for certain technologies. 

Solution: Partnering with technology companies to negotiate costs and get help deploying new technology solutions. Start with a smaller initiative to demonstrate the potential benefits of technology. It can reduce expenses and risk. 

One can reduce costs and improve patient outcomes by prioritizing investments. Conducting a cost-benefit analysis is also a great option. 

Cloud-based technologies provide a cost-effective alternative to on-premises solutions. It comes with greater scalability and flexibility.

Data quality

Challenge: Healthcare data are usually fragmented and stored in disparate systems. Furthermore, a lack of data could hinder transformation practices as it is difficult to make educated judgments.

Solution: Investing in ML is a better option. ML operates with minimal quantities of data to produce meaningful insights. Investing in ML helps insights and make data-driven choices by leveraging their current data.

Resistance to change

Challenge: Some professionals may be hesitant to adapt to the technology advancement. So, they stick to the old solutions that stop them from improving the process. Digital transformation – is not just about software implementation. It is also about employees’ way of handling advancements. 

Solution: Resistance to change could be a common difficulty during technology implementation. Discuss how the technology will improve patient care, increase efficiency, and result in favourable outcomes. Emphasize the importance of change and the benefits it has on business. Comprehensive training, including people in decision-making, is the better option. Representing the benefits of digital transformation may assist in overcoming reluctance to change.

User experience

Challenge: Poor user experience and lack of human-centred design in tech development lead to frustration. 

Solution: Adopting a human-centred design approach with the end-user in mind. User feedback, usability testing, and iterative design processes should be used to create intuitive interfaces. Training and support should be provided to enable healthcare professionals to maximize the benefits of these technologies.

Conclusion

Tech advancement can hold immense potential to improve the healthcare business. However, addressing major challenges in implementation is requisite. 

Unlock the full potential of healthcare technology by contacting our experts. 

Cloud Computing in Healthcare 2022 and Beyond

Posted on by Shanth Babu

The healthcare industry is undergoing a rapid change. Thanks to technological advancements, doctors can now diagnose patients without ever seeing a patient face to face.  From algorithms that can predict a patient’s prognosis to remote monitoring solutions and robots that perform can surgeries, technology impact is in all areas of healthcare.

When we think of the latest trends in technology, Blockchain, AI and IoT come to mind. The backbone of all these technological trends is cloud computing. Cloud computing makes innovations like AI-powered chatbots and IoT based healthcare applications possible. The digitization of healthcare data has paved the way for massive shifts in the consumption, storage, and sharing of medical data.

In this article, let’s take a look at the types of cloud computing and the benefits of cloud computing in healthcare. But first, let’s cover the basics.

Overview

What is Cloud Computing in Healthcare?

From Netflix to Gmail and online banking, we use a bunch of cloud computing solutions everyday without even realizing it.

Cloud computing in the healthcare industry is the concept of leveraging the power of the internet to store, manage, and process healthcare data from a remote server. In contrast to traditional data centers, cloud computing is inexpensive, scalable and supports collaboration.

It gives patients access to their Electronic Medical Records (EMR) and also enables them to receive on-demand remote consultation. From a healthcare provider’s standpoint, cloud computing breaks down location barriers.

Types of Cloud Computing in Healthcare

Cloud computing allows customers to leverage a cloud providers’ infrastructure, platforms, and software in a flexible and cost-efficient manner . There are two models of cloud computing in healthcare – distribution and deployment models. Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PasS) are all options of deploying the distribution model . Private, Community, Public, and Hybrid deployment models are all options of deployment cloud computing models.

Cloud Computing by Deployment  Model

  • Private – The cloud network is private. Only the healthcare group/hospital can use the cloud facility. 
  • Community – The cloud network is shared by a group of healthcare bodies.
  • Public – The cloud is open. All the stakeholders have access to the network. This aids in faster sharing of knowledge in the medical field.
  • Hybrid – This model is a combination of some elements of all the other deployment models.

Cloud Computing by Distribution Model

  • Software as a Service (SaaS) – In this cloud computing distribution model, a cloud provider hosts the healthcare applications and makes them available to clients.
  • Infrastructure as a Service (IaaS) – The cloud provider sets up the IT infrastructure, operating system in this case for the client to deploy applications.
  • Platform as a Service (PaaS) – The cloud provider distributes a ready-to-use platform for the client. The IT infrastructure, operating system, applications, and other components are distributed and the client can set up the environment quickly.

Benefits of Cloud Computing in Healthcare

In short, cloud computing supports healthcare technologies such as electronic medical records, patient portals and innovations such as IoT healthcare devices, and big data analytics.Let’s take a look at how cloud computing can benefit the healthcare industry.

Cost-Effective Healthcare Data Storage

Maintaining patient data is a cumbersome task. Especially in this era where data needs to be collected and stored from various sources, such as EMRs, prescriptions, insurance claims, healthcare app data, and wearables. Cloud computing allows hospitals to pay as they go for IT infrastructure. Hospitals and healthcare providers no longer need to purchase expensive data storage hardware and software or manage the infrastructure on their own. This helps hospitals to grow faster and offer better service.

Telemedicine

Ever since the pandemic, telemedicine has gained popularity. Cloud-based applications and telehealth systems allow patients to reach out to the healthcare professionals without location or time constraints. From video conferencing medical sessions to even tracking consumption of medicines, telemedicine has become an integral part of healthcare. In a nutshell, cloud computing is the key to better telemedicine.

Improved Patient Experience

Healthcare groups and clinicians can now provide a patient with real-time access to lab test results, medical information, and even doctor’s notes, thanks to cloud computing. Patients have the flexibility to share their medical records and get a second opinion with another clinician in a short span of time. Documented health records on cloud prevents patients from being overprescribed or dragged into unneeded testing. Medical data can be archived and retrieved easily when stored on the cloud.

Enhanced Collaboration

Cloud computing in healthcare plays a major role in boosting collaboration. Patients no longer need to carry medical records while visiting a doctor. Doctors can share a patient’s history with other specialists, check earlier consultations with other healthcare professionals as well. Cloud computing facilitates collaboration which in turn enables doctors to provide a more accurate treatment.

Risks of Cloud Computing in Healthcare

Despite all the benefits that healthcare cloud computing offers, there are still some risks.

Implementation Risks

Switching from an on-premises installation to the cloud is a specialist task. Clinicians or healthcare groups would need to locate experienced developers and cloud experts who can integrate new technology without glitches. Otherwise, your company may experience outages, poor data processing, or information leaks. Next, there is a learning curve, hospitals need to train their staff on how to work productively in the cloud.

Security risks

Storing medical data in the cloud comes with a risk of attack. While cloud networks provide security tools to monitor the environment for threats and deal with threats, it is not foolproof. Currently in the US alone, there are over 500 cases cases of security breaches leading to a hack of the patient’s health information. Hospitals need to invest in a team that could monitor and tackle attacks such as DDoS effectively.

Regulatory Compliance

Patient data is one of the most sensitive held by healthcare providers. It is protected by regularities such as HIPAA and the GDPR. Healthcare providers have a legal obligation to protect patients’ data and to notify them of data breaches as part of these legislation’ duties. Failing to protect confidential patient data can result in a hefty charge. To ensure that patient data is protected, security mechanisms such as access controls, authentication, and storage security must be implemented. This is one reason why most healthcare providers are reluctant to make the shift to the cloud.

Storage reliability

Selecting a cloud service provider who is capable of supporting your workload is the key to avoiding unnecessary downtime. Most cloud providers offer the flexibility to pay on the go. If your usage requirements surpasses your current cloud computing strategy, you may face some issues accessing data on-demand or face performance difficulties such as latency. It is very important to choose a trusted cloud service provider for improved security and reduced chance of unplanned downtime.

Final Thoughts

While moving to the cloud carries some significant risks, it also presents a wonderful potential for healthcare organizations to improve patient care. All of the risks can be mitigated by completing thorough study and determining what security measures are required to safeguard data stored in the cloud. Part of it is understanding your cloud provider’s duties, as well as your own, so you don’t expose your firm to legal or financial danger.

At SolutionChamps, we have years of experience in digital transformation and implementing IoT based healthcare solutions. Discuss your project idea and get an attractive quote today!

How Remote Patient Monitoring Solutions in Telehealth and Telemedicine Are Impacting Our Lives

Posted on by Shanth Babu

Artificial Intelligence and IoT have caused technological breakthroughs in all sectors and healthcare is certainly no exception. Over the years and especially during the pandemic crisis, we’ve witnessed cutting edge innovations and applications such as remote patient monitoring (RPM), telehealth, predictive analytics and so on being used to save lives. 

In this blog post, let’s explore a little on telehealth, telemedicine and remote patient monitoring. Let’s dive in.

Overview

Telemedicine and Telehealth – Is There a Difference?

Although the terms telemedicine and telehealth are often used interchangeably to refer to technology and electronic communications in healthcare, there is a subtle difference between these terms. The term telehealth denotes online health care, specifically to remote healthcare services such as telemedicine, clinical services, health education and so on. Telemedicine on the other hand refers to a healthcare professional diagnosing and treating patients via online communication. In essence, telemedicine is a form of telehealth service. Telemedicine serves as a means to discuss a patient’s real-time medical needs over the internet. Telemedicine process is quite similar to a traditional doctor-patient office visit; the only difference is that the service is delivered remotely. 

Types of Telemedicine Services

Some of the common types of telemedicine services offered these days are store-and-forward, remote monitoring, and real-time interactive services. 

Store-and-forward

Store-and-forward telemedicine has revolutionized healthcare.  In store-and-forward telemedicine patient information such as x-rays, videos, and lab reports are stored and transmitted to different locations for a consultation. This is a huge boon for healthcare teams working in remote locations or third-world countries who need to coordinate with a specialist living in a different location.

Medical images such as X-rays, CT scans and biosignals such as EEG, ECG, EMG are sent to specialists for consultation in this type of telemedicine. Store and forward practice are common in the fields of dermatology, radiology, pulmonology and pathology. It not only saves time but a historical record of a patient is also documented to avoid misdiagnosis. 

Real-time Interactive Telemedicine

As the term indicates, this denotes real-time video or phone consultation that patients have with medical professionals. Medical professionals from any part of the world can now consult, assess, diagnose a patient and even prescribe medicines.

Some of the popular real-time interactive telemedicine services include teleneuropsychology, telepharmacy and even telerehabilitation. Neuropsychologists help people cope with mental illnesses such as dementia and psychoses, ADHD, Down syndrome and other similar ailments. Teleneuropsychology is a feasible alternative to in-person consultations which can help these patients to connect with medical professionals anytime. Through telenursing services, a nurse can teach, consult, triage, and provide nursing services to patients in a remote setup. As other forms of telemedicine, this can be made in real-time or over the phone. Nurses can assist patients with chronic medical conditions or minor ailments that don’t require medical care.

Telepharmacy services such as refilling prescribed medications have been done for ages. It facilitates pharmaceutical services to patients without direct contact with the pharmacist. Telerehabilitation is a boon as it helps deliver therapy to individuals suffering from disabilities, mental disorders, or other impairments. These patients can skip travelling to the therapy facility for long-term care and consultation. It is not just convenient but also cheaper than traditional rehabilitation practices. 

Remote Monitoring

Remote monitoring is also known as self-monitoring or self-testing service. This type of telemedicine service is used in the management of long-term chronic diseases. For example patients with years of diabetes, cardiovascular problems, asthma can be remotely monitored. Remote monitoring is not just cost-effective, it gives the patient a sense of control over their health as they can frequently monitor their progress and consult doctors as required when they find health deteriorating.

Common Applications of Remote Patient Monitoring

At SolutionChamps, we’ve implemented an IoT based personal healthcare platform solution for RooiDigital. With a hub, portal and cloud the IoT based remote patient management system empowers caregivers and patients to connect biosensors and devices to monitor conditions such as pulse, blood sugar, heart health. The IoT based personal healthcare platform also keeps track of a patient’s medical adherence, activity level and sends reminders. It also allows caregivers and patients alike to connect via video conferencing, voice calls, and messaging.

Essentially, any device that can monitor and send a patient’s vital signs or physiological data to caregivers is helpful in remote patient monitoring. Let’s look at some other use cases of remote health monitoring. 

Cancer

A recent study has shown that remote patient monitoring has helped cancer patients to avoid unnecessary hospitalizations. Common side effects of chemotherapy such as fatigue, loss of appetite, dehydration, nausea, constipation, vomiting and depression can be monitored through remote patient monitoring. Bluetooth-enabled devices such as pulse oximeters and blood pressure monitors can be used to measure the patient’s vital signs so that prompt care can be given in the case of deterioration.

Chronic Obstructive Pulmonary Disorder (COPD)

Patients with the severe chronic obstructive pulmonary disorder are benefitting from remote monitoring tools that can predict exacerbations. Many hospitals used remote monitoring to oversee blood oxygen levels and post-discharge in long-COVID patients. Digital spirometry, smart nebulizers, and pulse oximetry are some of the common applications of COPD remote monitoring. 

Diabetes

Unmanaged diabetes can cause complications, such as heart disease, nerve damage, kidney failure and amputations and so on. Remote patient monitoring can help track blood pressure, blood glucose and other vital stats. Healthcare providers can also manage a patient’s diabetes via RPM by setting alerts to monitor patients whose diabetes crosses a normal threshold.

Congestive Heart Failure (CHF)

CHF is the main cause of death around the globe. Early detection of abnormalities is very important in identifying and managing heart failure. Cardiac implantable devices (such as pacemakers) can be used to deliver critical data to help care teams to remotely monitor patients. CHF not only reduces mortality rates, but it also immensely reduces the need for lengthy hospital stays and improves the patient’s quality of life.

Conclusion

Telehealth and Telemedicine, especially Remote Health Monitoring services is a huge leap forward in improving the world’s accessibility to healthcare. Apart from cost-effectiveness, these services offer a huge level of convenience when compared to traditional in-person appointments.

These services are a huge boon in a post-pandemic world. Senior citizens and people with disabilities can stay in the comfort of their own homes to get crucial care.

From implementing an IoT based personal healthcare platform solution to creating a supporting mobile application for AI-powered smart stethoscopes, SolutionChamps have years of experience in engineering modern-day healthcare innovations. Let’s start talking. Get in touch with us to discuss your project idea and get an attractive quote. 

AI Use Cases in Healthcare

Posted on by Shanth Babu

Medical science is experiencing a huge paradigm shift. We are witnessing some early adopters embrace AI and machine learning to get insights from patients’ data. Many organizations are looking for sustainable ways to dig out raw data and put it to good use. 

According to Forbes, healthcare is one of the top 16 industries that would see rapid growth in 2022 and beyond. The AI healthcare market is expected to grow to $194.14 billion by 2030.   From the early interpretation of patient symptoms for a quick diagnosis to robotic surgeries, AI is rapidly being implemented in the healthcare industry. 

In this blog, let’s look at some of the top AI applications or use cases in healthcare.

Overview

AI Chatbots for Clinical Diagnosis

Robot handshake human background, futuristic digital age

Did you know that 2 out of 5 people misdiagnose their symptoms by searching their symptoms online? Hospitals can use chatbots as a good start point in engaging with patients. An AI-based chatbot with machine learning algorithms and natural language processing techniques can be trained to evaluate a patient by prompting a series of questions regarding the symptoms. An extensive list of symptoms and diagnostic steps from trusted clinicians can be a part of the algorithm to make an initial diagnosis. Healthily and Mediktor are good examples of AI-based popular symptom checkers. The AI chatbots can handle patients’ queries on matters as simple as billing, appointments, or medication refills or as complex as suggesting a diagnosis. 

Prescription Auditing

A wrong prescription can be life-threatening. AI can help in prescription auditing. Some examples of a prescription audit by AI systems include alerting dose miscalculations, wrong drug selection and confusion over similarly named drugs, similar packaging.  Medication errors may be due to human errors, but it often results from a flawed system with inadequate backup to detect mistakes. 

Currently, AI, big data analytics and machine learning algorithms are used for analyzing large-scale Electronic Medical Records (EMRs) data and identifying how physicians treat patients in real-life scenarios. When a doctor is about to prescribe a medication that is way beyond the typical treatment pattern, the AI system can be designed to flag a potential error so that the physician could double-check.

Patient Triaging

Normally people tend to handle minor symptoms at home and visit the doctor only when the situation gets worse. Patient triaging is a method where medical professionals evaluate the degree of a patient’s emergency to prioritize time-sensitive treatments. Doctors rely on clinical judgment to identify high-priority patients which can be challenging. Over-triaging can happen when a doctor is unsure of an evaluation. This can lead to people being sent to intensive care treatment unnecessarily. Under-triaging can cost human lives and have irreversible negative consequences. In recent years, predictive analytic capabilities, AI models are proven to be more effective in automating the triaging process.  

Hospitals have adopted AI triage models to triage patients during Covid-19 crisis. Another example of artificial intelligence-based triage is the application of AI in triaging patients with acute abdominal pain. From predicting cardiac arrest risks to scanning the severity of incoming patients based on multiple clinical findings, the AI triage decision-making process is a critical life-saving innovation.

Genome Analysis

Understanding genes and their components have always been a frustrating experience for scientists. Unpacking the genome and predicting the impact of gene edits involves scrutinizing a lot of data. The use of AI and machine learning is removing the bottlenecks in this field. From analyzing the vast data in a single person’s DNA to comparing the genomes between a malignant and normal tissue, AI deep-learning and neural networks techniques are helping in automating the diagnosis of diseases including cancer. 

Robot-assisted Surgeries

According to DataBridge Market research, the Global surgical robots market is expected to reach USD 19,413.63 million by 2026. Robotic surgery or robot-assisted surgery has gained traction in the United States and Europe. Robots have helped doctors to perform complex procedures with precision and minimal invasiveness, less pain and blood loss. With a camera arm and mechanical arms of surgical instruments, a surgeon can control the robotic arms from a computer screen in the operation room. Robot-assisted surgeries are safe and more accurate as the camera sends out clear pictures of the surgical site for the surgeon to evaluate the process. From taking out brain tumours to total knee replacement, heart operations to spine surgeries, robots are well-suited for complex procedures as the robotic arms can perform repetitive movements without fatigue. 

Medical Imaging

In many countries, patients have to wait for days for a radiologist to assess a radiograph. AI-backed medical imaging machines can help in assessing, and diagnosing patients precisely in a faster way. AI medical imaging and cloud technology is a boon for researchers and doctors workers working in remote locations. The technology is especially useful in chest and brain imaging in detecting and diagnosing life-threatening diseases. Early detection of breast cancer is completely treatable but conventional mammogram screening misses out 1 in 5 cancer detection. Google’s AI powered Lymph Node Assistant (LYNA) claims to detect breast cancer metastasis with 99% accuracy which is commendable when compared to human pathologists who miss early detection 62% of the time. From analyzing full-body MRI scans to detecting skin cancer using photos of your skin. 

Drug discovery

Drug discovery research is the identification of medicines that can prevent or treat a disease. Traditional drug discovery methods work well for targets that have a well-defined structure and well-understood intracellular molecules. However, these methods do not work in the case of complex cellular interactions. AI in drug discovery uses complex algorithms and machine learning to detect the interactions between molecules and complex cellular pathway components. The AI advancements in drug discovery have helped Moderna to develop a COVID vaccine in record time.

Brand Building

As the healthcare industry shifts to a more technology-driven interaction with the patients, the opportunity to build relevance and personalized marketing. Artificial intelligence helps hospitals to create the most optimal marketing strategy for the brand. The data from social media platforms, blogs and other patient touchpoints can be used in analyzing consumer behaviour and search patterns. From market research to identifying target segments and offering personalized service, AI helps hospitals to gain competitive intelligence. Medication reminders and pharmacy refills can be automated to increase brand loyalty. Artificial intelligence empowers marketers to make data-driven decisions to improve customer journeys.

Process Automations

From the point of admission to discharge, patients go through a series of tedious and tiring processes. Hospitals can give a better patient experience by streamlining these processes. Automating the admission process can leave a lasting impression on the patient’s mind. Hospitals can also streamline ER operations and triage patients for emergency care with process automation. From automating Electronic Health Records (EHR) to staff boarding and roster management, the possibilities of process automation are endless.

Apart from these most popular AI use cases in healthcare, there are numerous possibilities in adopting this technology. If you are looking to create a custom machine learning or AI solution, feel free to contact us for a free quote.

The Role of Artificial Intelligence in Healthcare Diagnosis

Posted on by Shanth Babu

Access to quality healthcare services is a basic human right that is out of reach for many in the world. With covid raging havoc, now more than ever, the need for technological innovations in the healthcare division is the need of the hour. Healthcare Artificial intelligence (AI) and data science is a growing market that undoubtedly is going to impact our lives. 

Many hospitals have already embraced chatbots for appointment scheduling. An AI-powered chatbot can take the load of the front desk by handling more than just appointment queries. From diagnosing respiratory illnesses to forecasting diseases, AI as a tool can be trained to correctly interpret health data by feeding large complex datasets. Hospitals need to embrace healthcare artificial intelligence in the coming years to improve their service level.

At SolutionChamps Technologies, we have years of experience working with healthcare clients and creating AI-backed tools. In this article, we will explore how AI-backed tools and technologies are aiding the healthcare industry. 

Overview

AI in Disease Diagnosis

It takes years of medical training and hands-on experience for a doctor to skillfully and correctly diagnose a disease. From assessing a patient’s symptoms and analysing the test results, diagnostics is a crucial and time-consuming medical process. In many branches of human science, there is a huge demand for expert physicians and patients need to wait for days for clinical diagnosis even in developed countries. This is where AI-backed Machine Learning and Deep Learning algorithms play a pivotal role. AI-backed clinical diagnostics of diseases are accurate, cheap and convenient. 

A recent study published by Science Daily says that AI systems help in detecting even the most subtle changes in a patient at a very early stage. From remarkable detection of breast cancer to ECG, EEG or even X-ray images, machine learning and AI techniques are paving the way for healthcare advancements.

How AI diagnoses Work 

Just as a doctor would need to identify patterns to diagnose a disease, machine learning algorithms are pre-trained with datasets to identify patterns. Multiple predictive analysis techniques and deep neural networks (DNNs) are used in AI clinical diagnosis. 

 Machine learning algorithms trained by expert professionals can help in an accurate diagnosis. 

AI-backed machine learning is currently in action to perform diagnoses such as-

  • Detect lung cancer and stroke based on CT scans
  • Predict cardiovascular risks by measuring blood flow and diagnose cardiovascular disease based on electrocardiograms and cardiac MRI images
  • Classify skin lesions using Skin images and patterns
  • Detect diabetic retinopathy by accurately interpreting retinal scans
  • Identify areas containing tumor or healthy tissue using MRI imaging 
  • Detect cancer-causing lesions in Endoscopic procedures

AI techniques and Clinical diagnosis

Artificial Intelligence in healthcare Diganosis

Clinical diagnosis using artificial intelligence is implemented using the following AI techniques and AI fields: 

  • Computer vision
  • Time-series analysis
  • Speech recognition, and
  • Natural language processing

Computer Vision

Computer vision and machine learning are fields of AI that involve training the AI system’s algorithm to see and process images just like a human would. The system is trained using digital images, videos and other visual inputs. The supervised or unsupervised AI system then takes actions or makes a recommendation. When it comes to healthcare, computer vision is applied in tumor and cancer detection, medical imaging, in the fight against and health monitoring. According to a recent study, computer vision in the healthcare market is projected to reach USD 1,457 million by 2023 from USD 210 million in 2018, at a CAGR of 47.2% during the forecast period. 

Time-Series and Predictive Analysis 

Time series analysis is beneficial in collecting observational data over a period of time frame. It is helpful in predicting a patient’s prognosis and potential risk factors. From cardiac arrest prediction tools to predicting brain tumour and even predicting the prognosis of respiratory illnesses and the prognosis of a covid patient, this branch of AI is churning out accurate predictions.

Speech Recognition Techniques

Documentation is a time-consuming administrative task that takes away the time of a clinician. Speech recognition tools are used in healthcare to increase productivity. Less paperwork has already benefited clinicians and frontline workers to focus on the patients. When it comes to clinical diagnosis, speech-recognition techniques are used in identifying neurological disorders. Recently, Amazon has applied for a patent for Voice-based determination of physical and emotional characteristics of users. From detecting sore throat to analysing a person’s voice and predicting a risk of suicide, researchers are hoping to use speech analysis in diagnostics.

Natural Language Processing 

AI-based chatbots can interpret natural language and do a preliminary assessment of a patient’s symptoms. Natural Language Processing is also helpful in extracting data from digital health record data made using Optical Character Recognition (OCR).  

At SolutionChamps, we help organizations in their digital transformation journey by incorporating innovative techniques and resilient solutions. Contact us today for a quote.