Category: Digital Healthcare

The role of telehealth in the modern healthcare landscape

The Role Of Telehealth In The Modern Healthcare Landscape

April 17, 2023

Telehealth is contributing immensely towards reshaping the entire contemporary healthcare landscape, as we know it. Telehealth meaning is the provision of remote healthcare via telecommunications and other technologies. It can be called a form of virtual care or digital healthcare.  The term gained traction throughout the 1980s and ever since, the sector has expanded considerably, including many new technologies and devices for the transmission of medical data on a real-time basis worldwide. This has enabled the entire healthcare industry to provide services to those under-served or un-served. It is also being deployed now by both private and public providers of healthcare services, with goals including efficient emergency care, treating mental illness, enhancing long-term outcomes for patients, and more. The indispensable functions of telehealth in the healthcare sector  Telehealth is steadily becoming indispensable for the modern healthcare industry, especially with regard to expanding the scope and accessibility of healthcare for more people. Here are some of its vital functions-  Better Multi-Area Expertise- Telehealth enables superior expertise in almost all departments. The usage of medical devices for tracking patient health remotely has naturally ensured higher convenience for providers and patients alike. Telemedicine is thus supplementing in-person or regular visitors to doctors, enabling the creation of more intensive plans for treatments. It may also ensure extra care that would be unavailable otherwise.  Telehealth solutions have ushered in expertise across multiple areas. For instance, a community or group without access to a cardiac care specialist may remotely get the same in case of any requirement. It will not fully do away with visits to the doctor but rather work in tandem with the same for treatment cycles. Healthcare providers and doctors will be able to better monitor and track patients within these cycles while keeping them on track with their treatments. Practitioners will also get access to invaluable data for supporting treatments.  Helping Underserved Patients- Telemedicine consultations have revolutionized access to healthcare for underserved patients. The biggest advantage of telehealth is its capability of ensuring instant access to healthcare, which would have been impossible otherwise. Many communities are under-served or un-served altogether.  Telehealth creates the interface between patients and healthcare providers, ensuring that suitable advice is given along with ensuring health tracking and more specializations that patients would not otherwise obtain. Communities grappling with emergencies or natural disasters can also use telehealth for additional access to critical care solutions. It has already transformed emergency care in recent years.  Ensuring Affordable Solutions for Patients- Telehealth is not just affordable, but it makes things more convenient for patients. Many health insurers are now offering reimbursements for telehealth in tandem with Medicare solutions. Patients are now seeing it as a more affordable way to get healthcare access. Wearables are also contributing towards better health tracking, data gathering, and also evaluating chronic ailments, thereby reducing future costs of patients.  Revolutionizing Mental Healthcare Access- Mental healthcare is one of the biggest segments benefiting from easy access to telemedicine, with easy tracking, access to practitioners and experts, quick consultations, community access, prescriptions, therapy sessions, and more.  How Telehealth will look in the Future Telehealth will evolve greatly in the near future as per expectations. Here are some of the key points worth noting in this regard.  Telemedicine will eventually turn into a standardized solution throughout multiple care-based settings. With patients getting more used to the higher access ensured by telemedicine consultations, it will naturally witness steady growth in the future. More than 1 billion telemedicine visits were reportedly in the works by the end of 2020 alone.  Patients will naturally appreciate personalized experiences, while endeavouring to choose their healthcare providers, platforms, providers/doctors, and hospitals, on the basis of telemedicine access and consultations. Patient wait times will slide hugely and patients will choose those health systems offering virtual care or digital healthcare access.  Healthcare players integrating telehealth will witness significant growth in patient retention and loyalty, along with scaling up their growth in revenues and business alike. More patients now seek telemedicine-based solutions.  Telemedicine could be a future beacon for preventive care. It will enable easy access to follow-up consultations, tracking, specialists, quicker diagnosis, and treatment guidance. Hence, there will be lesser readmissions in the hospital system along with inpatient stays and other issues. The costs will come down and preventive care will be the emphasis for most patients.  Hospital wait times will come down considerably, while there will be round-the-clock access to medical specialists. Hospitals will have speciality centres with networks of physicians and customers can get instant access to the same through telehealth platforms. These facilities will also ensure accessibility towards more focused care solutions for patients. It will go a long way towards greatly enhancing focused care and overall patient experiences along with lowering hiring costs for full-time staff members.  As can be seen, remote healthcare or telehealth is here to stay. And for good reason.  FAQs What is the meaning of telehealth, and how do they work? Telehealth means remote, virtual, or digital healthcare, including tracking, consultations, medical advice, treatment solutions, and diagnosis. This works through dedicated online or mobile platforms.  What are some potential challenges and limitations of telehealth? Challenges include proper internet connectivity, technological hesitation amongst a section of the under-served population globally, and so on.  How can telehealth be used to improve access to healthcare in underserved communities? Telehealth can be a godsend for better healthcare access in underserved communities. People can easily find specializations that they desire in emergencies and also for regular consultations and follow-up care. They can also avoid visits and long-distance travel to hospitals by consulting physicians/practitioners via digital or virtual platforms and getting all necessary medical care and advice.  What impact is telehealth having on the overall cost of healthcare? Telehealth has greatly lowered the overall cost of obtaining healthcare, considering that people no longer need to wait in line at hospitals and go for more expensive procedures or spend money on admissions. Preventive care and tracking are now readily possible via telehealth and this has lowered overall

Read More »
How the Large Language Models like GPT are revolutionising the AI space in all domains (BFSI, Pharma, and HealthCare)

How the Large Language Models like GPT are revolutionising the AI space in all domains (BFSI, Pharma, and HealthCare)

April 6, 2023

Large language models or LLMs are ushering in a widespread AI revolution throughout multiple business and industry domains. DALL-E-2 set the cat amongst the pigeons in the AI segment in July 2022, developed by OpenAI, before ChatGPT came into the picture. This has put the spotlight firmly on the invaluable role increasingly played by LLMs (large language models) across diverse sectors. Here’s examining the phenomenon in greater detail.  LLMs make a sizeable impact worldwide With natural language processing, machine learning, deep learning, and predictive analytics among other advanced tools, LLM neural networks are steadily widening the scope of impact of AI across the BFSI (banking, financial services, and insurance), pharma, healthcare, robotics, and gaming sectors among others.  Large language models are learning-based algorithms which can identify, summarise, predict, translate, and generate languages with the help of massive text-based datasets with negligible supervision and training. They are also taking care of varied tasks including answering queries, identifying and generating images, sounds, and text with accuracy, and also taking care of things like text-to-text, text-to-video, text-to-3D, and digital biology. LLMs are highly flexible while being able to successfully provide deep domain queries along with translating languages, understanding and summarising documents, writing text, and also computing various programs as per experts.  ChatGPT heralded a major shift in LLM usage since it works as a foundation of transformer neural networks and generative AI. It is now disrupting several enterprise applications simultaneously. These models are now combining scalable and easy architectures with AI hardware, customisable systems, frameworks, and automation with AI-based specialised infrastructure, making it possible to deploy and scale up the usage of LLMs throughout several mainstream enterprise and commercial applications via private and public clouds, and also through APIs.  How LLMs are disrupting sectors like healthcare, pharma, BFSI, and more Large language models are increasingly being hailed as massive disruptors throughout multiple sectors. Here are some aspects worth noting in this regard:  Pharma and Life Sciences:  Healthcare:  The impact of ChatGPT and other tools in healthcare becomes even more important when you consider how close to 1/3rd of adults in the U.S. alone, looking for medical advice online for self-diagnosis, with just 50% of them subsequently taking advice from physicians.  BFS:  Insurance:  The future should witness higher LLM adoption throughout varied business sectors. AI will be a never-ending blank canvas on which businesses will function more efficiently and smartly towards future growth and customer satisfaction alike. The practical value and potential of LLMs go far beyond image and text generation. They can be major new-gen disruptors in almost every space.  FAQs What are large language models? Large language models or LLMs are specialised language frameworks that have neural networks with multiple parameters that are trained on vast amounts of unlabelled text with the usage of self-supervised learning.  How are they limited and what are the challenges they encounter? LLMs have to be contextual and relevant to various industries, which necessitates better training. Personal data security risks, inconsistencies in accuracy, limited levels of controllability, and lack of proper training data are limitations and challenges that need to be overcome.  How cost-effective are the Large Language Models? While building an LLM does require sizeable costs, the end-savings for the organisation are considerable, right from saving costs on human resources and functions to automating diverse tasks.  What are some potential ethical concerns surrounding the use of large language models in various industries? Some concerns include data privacy, security, consent management, and so on. At the same time, there are concerns regarding these models replicating several stereotypes and biases since they are trained using vast datasets. This may lead to discriminatory or inaccurate results at times in their language. 

Read More »
Addressing drug shortages with advanced analytics

Addressing drug shortages with advanced analytics

April 3, 2023

Drug shortages have become a part and parcel of modern healthcare systems due to several reasons. While there is a sizeable economic impact of drug shortages for manufacturers and pharmacies alike, there are widespread community and social disadvantages as well. Pharmacies or clinics running out of medicine stocks are representatives of a scenario that is often witnessed worldwide and with frightening consequences.  For example, Europe is already seeing shortages of commonly-used medicines. A survey by the Pharmaceutical Group of the European Union (EU) had 100% of 29 member nations reporting shortages of medicines amongst community pharmacists. 76% also stated how shortages had worsened than the earlier year (the survey was implemented between 14th November and 31st December 2022). The UK is also witnessing HRT shortages according to reports, while hospitals in the U.S. are also reporting issues with procurement for liquid ibuprofen, while ADHD diagnoses have gone up in the U.S. as well, leading to shortages of vital drugs for the same. Mexico is witnessing chronic shortages and unfulfilled prescriptions and supply fluctuations and disruptions have been seen widely throughout Asia too.  What are the reasons for medicine shortages?  Wondering about the reason for drug shortages? There are quite a few that can be noted in this context:  Higher seasonal illness outbreaks in the aftermath of COVID-19, leading to skyrocketing average annual demand for medicines that is higher than normal in several categories.  The inability of pharmaceutical companies to meet such unprecedented demand, with excess capacity restricted for cost control.  Global supply chain impact along with higher energy costs and inflation have impacted global drug manufacturers who have to contend with pricing measures.  Stockpiling by customers due to sudden drug shortages.  Over-prescribing by the system.  Reports estimate that the National Health Service in the UK loses a whopping 300 million pounds annually owing to partially-used or unused medication which cannot be reused or recycled.  Lack of systems for forecasting and identifying supply shortages, while ensuring proper inventory management.  Drug Shortage Solutions That May Work  There are a few drug shortage solutions that may be effective for combating and reducing shortages.  Data and analytics are enabling better access towards medicines worldwide while enabling superior supply and demand management for individual patients and pharmacies alike.  Real-time pharmacy, hospital, and clinical data will enable a proper understanding of the demand for specific drugs/medical products.  Leveraging electronic and public health records for enabling healthcare stakeholders to report demand figures for drugs, without revealing confidential patient data.  Opportunities for better inventory and supply chain management with AI (artificial intelligence) and machine learning (ML).  Generic entities may leverage smarter technologies for lowering manufacturing costs by up to 20% while enhancing production. Smarter and connected factories with proper insights and data analysis can enable higher savings and reliable deliveries.  Companies may look at higher procurement of local active ingredients while depending on go-to nations for the same. Boosting supply and production levels, along with harnessing real-time data analytics will enable tackling this scenario.  Supervised machine learning and analytics models can help in forecasting/predicting shortages for most drugs used throughout various categories, price points, and age groups.  Modelling can enable healthcare stakeholders to understand more about the issues behind drug shortages while analytics can also help predict demand for specific drugs based on historical data and current trends.  Pharmacies and other players may not have access to data on the supply side, although they have demand-side information. They will be able to gain more visibility into the supply chains of manufacturers with an integrated information-sharing system.  Data analytics-driven insights for optimizing orders and eventually lowering the effect of drug shortages on pharmaceutical and healthcare operations.  Systems for tracking and reporting drug shortages, including aspects like the frequency, drugs involved, period, causes, duration, managing strategies, impacts, and future shortages too.  Real-time identification and tracking of patients receiving shorter supplies of drugs by hospitals, clinics, and pharmacies. Immediate patient identification regulations for capturing present drug utilization across multiple categories.  Real-time identification and addressing situations along with finding out drugs in shorter supply. Predictive abilities enable higher time for researching material for alternative agents or making suitable arrangements for drug acquisition from other sites or facilities.  Once supply levels normalize for a drug, pharmacists and healthcare stakeholders may discontinue their surveillance regulations without waiting for technical assistance. Real-time data-filtering and reporting abilities are leveraged for viewing drug usage trends and prescription patterns throughout healthcare systems. These insights may enable higher standardization of drug management across institutions, while also facilitating better training of clinicians for lowering care variations.  Advanced data analytics will help address drug shortages and enable better inventory management simultaneously. However, suitable implementation, technological integration, and awareness are necessary for the same.  FAQs How can advanced analytics be used to address drug shortages? Advanced analytics can be deployed for tackling drug shortages through real-time tracking and surveillance of prescription trends and drug demand, forecasting shortages, and enabling better drug supply management.  What are the benefits of using advanced analytics to address drug shortages? Advanced analytics goes a long way towards helping tackle drug shortages, enabling forecasting future demand and shortages, identifying patterns for better management, and also enabling better global medicine access.  What are the challenges of using advanced analytics to address drug shortages? Challenges include technological integration, legacy systems integration, awareness regarding best practices, quality data generation, and more.  What are the best practices for implementing advanced analytics for drug shortage management? Best practices include unified and integrated public databases, suitable data modelling systems, suitable protocols for data security and privacy, and swift reporting mechanisms for demand and shortages.

Read More »
Data analytics plays a crucial role in clinical trial design and analysis by providing valuable insights into the effectiveness of new treatments and therapies.

The role of data analytics in clinical trial design and analysis

March 31, 2023

What is the role of data analysis in clinical trials? Can there be better clinical trial data analysis using R and other technologies? Is there a case for using big data analysis in clinical trials? Experts would certainly say Yes to all these questions. Clinical trials themselves have gone through sweeping changes over the last decade, with several new developments in immunotherapy, stem cell research, genomics, and cancer therapy among numerous segments. At the same time, there has been a transformation in the implementation of clinical trials and the process of identifying and developing necessary drugs.  To cite a few examples of the growing need for clinical trial data analysis, researchers gain quicker insights through the evaluation of databases of real-world patient information and the generation of synthetic control arms, while identifying drug targets alongside. They can also evaluate drug performance post-regulatory approvals in this case. This has lowered the cost and time linked to trials while lowering the overall burden on patients and enabling faster go-to-market timelines for drugs too.  What is driving data analysis in clinical trials?  Clinical trial data analysis is being majorly driven by AI (artificial intelligence) along with ML (machine learning), enabling the capabilities of collection, analysis, and production of insights from massive amounts of real-time data at scale, which is way faster than manual methods. The analysis and processing of medical imaging data for clinical trials, along with tapping data from other sources is enabling innovation of the entire process while being suitable for supporting the discovery procedure in terms of quickening the trials, go-to-market approaches, and launches.  The data volumes have greatly increased over the last few years, with more wearable usage, genomic and genetic understanding of individuals, proteomic and metabolomic profiles, and detailed clinical histories of patients derived from electronic health records. Reports indicate 30% of the data volumes of the world are generated by the global healthcare industry. The CAGR (compound annual growth rate) for healthcare data will touch 36% by the year 2025 as well. The volume of patient data in clinical systems has already grown by a whopping 500% to 2020 from 2016.  Data analysis in clinical trials- What else should you note?  Here are a few factors that are worth noting:  Synthetic control arm development  The role of data analysis in clinical trials is even more evident when one considers the development of synthetic control arms. Clinical drug discovery and trials may be fast-tracked while enhancing success rates and designs of clinical trials. Synthetic control arms may help in overcoming challenges linked to patient stratification and also lower the time required for medical treatment development. It may also enable better recruitment of patients through resolving concerns about getting placebos and enabling better management of diverse and large-sized trials.  Synthetic control arms tap into both historical clinical trials and real-world data for modelling patient control groups and doing away with the requirement for the administration of placebo treatments for patients which may hinder their health. It may negatively impact patient outcomes and enrolment in trials. The approach may work better for rare ailments where populations of patients are tinier and the lifespan is also shorter owing to the disease’s virulent nature. Using such technologies for clinical trials and bringing them closer to end-patients may significantly lower the overall inconveniences of travelling to research spots/sites and also the issue related to consistent tests.  ML and AI for better discovery of drugs ML and AI may enable a quicker analysis of data sets gathered earlier and at a swifter rate for clinicians, ensuring higher reliability and efficiency in turn. The integration of synthetic control arms in mainstream research will offer new possibilities in terms of transforming the development of drugs.  With an increase in the count of data sources including health apps, personal wearables and other devices, electronic medical records, and other patient data, these may well become the safest and quickest mechanisms for tapping real-world data for better research into ailments with sizeable patient populations. Researchers may achieve greater patient populations which are homogenous and get vital insights alongside. Here are some other points worth noting:  The outcomes of clinical trials are major metrics with regard to performance, at least as far as companies and investors are concerned. They are also the beginning of collaborations between patients, groups, and the healthcare sector at large. Hence, there is a clearly defined need for big data analysis in clinical trials as evident through the above-mentioned aspects.  FAQs How can data analytics be used in clinical trial design and analysis? Data analytics can be readily used for clinical trial design and analysis, expanding patient selection criteria, swiftly sifting through various parameters and helping researchers better target matching patients who match the criteria for exclusion and inclusion. Data analysis methods also enable better conclusions from data while also improving clinical trial design due to better visibility of the possible/predicted risk-reward outcomes.  What are the benefits of using data analytics in clinical trial design and analysis? The advantages of using data analytics in clinical trial design and analysis include the integration of data across diverse sources, inclusive of third parties. Researchers get more flexibility in terms of research, finding it easier to analyze clinical information. Predictive analytics and other tools are enabling swifter disease detection and superior monitoring.  What are the challenges of using data analytics in clinical trial design and analysis? There are several challenges in using data analytics for the analysis and design of clinical trials. These include the unavailability of skilled and experienced resources to implement big data analytics technologies, data integration issues, the uncertainty of the management process, storage and quick retrieval aspects, confidentiality and privacy aspects and the absence of suitable data governance processes.  What are the best practices for implementing data analytics in clinical trial design and analysis? There are numerous best practices for the implementation of data analytics for the analysis and design of clinical trials. These include good clinical data management practices, clinical practices, data governance

Read More »

Overcoming Data Silos In Healthcare For Better Outcomes

March 28, 2023

Data silos in healthcare are a pressing problem for healthcare providers, hospitals, and industry stakeholders due to diverse reasons. Healthcare players now have to contend with big data silos while working out a fine balance between tapping opportunities that arise with more actionable intelligence and insights, while managing increasing technical complexities. The healthcare sector is known for the sheer depth of these data silos, presenting multifarious challenges or obstacles for providers. It begins with medicine R&D patient records and more. Eliminating data silos will contribute towards a win-win proposition for all stakeholders including patients, healthcare service providers, policymakers, and so on. There are however concerns relating to data security with a more complicated data landscape and rapidly evolving threats. The need of the hour is proper healthcare analytics with an emphasis on accompanying privacy-by-design framework, security analytics, encryption, multi-factor authentication, and other techniques. Interoperability is another aspect worth considering. This is the scope of data exchange and interpretation across various IT software applications, systems, and devices. Without proper guiding frameworks for interoperability, data exchange may turn confusing, time-consuming, and complex, hindering information flow and patient care alike. Data complexity has to be reduced by eliminating silos for service providers, doctors, and patients. Challenges for Interoperability There are several hurdles towards interoperability though. While eliminating data silos is possible with big data in healthcare analytics, there are several issues for providers even today. The present interoperability framework is a makeshift system for most healthcare industry players. 93% of hospitals and other healthcare systems make records available online for patients and this has increased from 27% in 2012, as per the Sharing Data, Saving Lives: The Hospital Agenda for Interoperability report in 2019. 88% of hospitals also share their data with ambulatory care as per these reports. However, the critical challenges include the fact that while 90% of hospitals are deploying certified IT solutions, several out-of-the-box options are muddling data exchange owing to silos. Other issues include concerns relating to privacy and security along with restrictions pertaining to the present HIEs (health information exchanges) and also the absence of any compatible linguistic or technical standards for making sure that shared data stays intact and relevant. The Need to Develop Superior Infrastructure A key hurdle towards extensive interoperability is the absence of suitable technology-driven infrastructure. While most providers use EHR (electronic health records) platforms, many of these were not developed keeping data exchange at the forefront. At the same time, health information exchanges were implemented for electronic leveraging of healthcare data and also in a secure manner. Yet, many of them cannot finish total data exchange in a reliable manner through varying source technologies or healthcare systems. A few HIEs also do not facilitate access to patient data which is counterproductive to the actual reasons for their implementation.  This report also mentioned how 97% of hospitals were already using certified EHRs, thereby making the case stronger for doing away with data silos. There is a need for proper systemic infrastructure for recording and transferring vital information securely throughout the ecosystem. Other aspects like APIs (application programming interfaces) are also vital for health data sharing. Accessible, open and FHIR (Fast Healthcare Interoperability Resources) standards-based APIs are seen as some of the best ways to quickly scale up interoperability. More than half of developers of technological solutions will have to ensure access to electronic health data via public and standard APIs in the near future. This should rise further in the current decade.  At the same time, big data in healthcare analytics is steadily attaining higher sophistication and refinement en route towards fusing with better governance and regulatory systems to tap better intelligence and operational efficiencies, along with keeping data silos at bay. Ensuring Greater Security Across The Ecosystem There have to be mechanisms in place for healthcare stakeholders with regard to relying on the accuracy and relevance of the shared healthcare data along with ensuring compliance and security at multiple levels. Privacy issues are still a concern in this space. IT developers and vendors should be able to integrate privacy and security protocols and needs for each infrastructural layer including APIs and third-party applications.  This technological infrastructure should have verification methods for information requests and their authorisation, while each entity which has access to patient information will have responsibility for securing and using data respectively. With more connected health IT systems, there will be growing cyber-security threats and one system’s vulnerabilities may lead to all connected systems getting exposed as a result. This will be an ongoing resolution for healthcare players, with regard to building data privacy and security standards, while complying with regulatory aspects seamlessly. Third-party security layers may also be possible through testing, identifying threats, and evaluation of technological upgrades.  In the end, eliminating silos is a vital task for the global healthcare industry today. Developing big data analytics techniques for penetrating deeper into available data is a key priority for several healthcare players. They are using these technologies for understanding the connections between applications, SSL certificate installation, server functions, and more. Machine and wire data is being analysed and gathered for insights while helping organisations zero in on blockage points which lead to these data silos. Integration of disparate systems across the sector is also vital for accomplishing interoperability at a bigger scale.  FAQs What are data silos in healthcare? Data silos naturally form across several data categories and departments have stored information. These make information inaccurate and inaccessible while hindering effective sharing due to blockages. What are the challenges of data silos in healthcare? The challenges include barriers to sharing critical patient and healthcare data across systems, providers, and the entire network. This impedes quicker decisions and end-consumer fulfilment at multiple levels. At the same time, silos prevent a holistic view of the entire framework for providers. What are the benefits of overcoming data silos in healthcare? The benefits include more accessible and usable data throughout multiple systems and stakeholders along with better collaboration across departments and improved decision-making.

Read More »
Patient Engagement Through Health Apps

Enhancing Patient Engagement Through Mobile Health Apps

March 3, 2023

Patient engagement is a strong buzzword today, particularly at a time when the entire healthcare industry is taking the digital route.  The conventional system is fast evolving into a more efficient and highly effective one with better patient engagement in healthcare being a focal point.  In this context, mobile health apps now make a compelling case for themselves with a view towards bolstering patient engagement, enabling better care quality, easier management of medical records and data exchange, and taking care of several other tasks in the bargain.  There are several kinds of apps that can be leveraged in this context, including urgent or emergency care apps with alerts, general hospital apps for information and engagement, referral apps, outpatient apps, and tracking apps to name a few.  How Mobile Health Apps Are Boosting Patient Experience   Mobile health apps are ushering in major changes for the entire mainstream healthcare ecosystem, enabling several aspects including the following: EMR/EHR Integration This is one of the key things to note if a healthcare provider is wondering how to engage patients in their healthcare journey. This helps patients manage, store, and control their health records, while also enabling hospitals with population health management. Healthcare interoperability also goes up considerably throughout hospitals as a result.  Notifications, Feedback, And Updates This is another patient engagement-related feature. SMS notifications keep patients updated about ongoing treatments and appointments while giving them a robust feedback mechanism at the same time. Profiles Of Doctors Another one of the unique patient engagement solutions is to have transparent information on the profiles of doctors, their credentials, their expertise, and soon. Healthcare providers can also leverage analytics for matching patients with specific doctors.  In-App Consultations Patient engagement also goes up considerably with mobile health apps facilitating in-app consultations with doctors and healthcare providers. This can be either through video or text.  It can be integrated with the ability to instantly access patient history and dispatch prescriptions that patients can use thereafter. This can also solve the remote healthcare problem in under-served areas throughout the country.  At the same time, it also helps patients who wish to get timely advice without sitting in lines at outpatient centres. Tele-health is one of the biggest emerging trends in the global healthcare space and this is where mobile health apps hold the key towards its successful implementation.  Appointment Scheduling Scheduling appointments with doctors can be a simple affair through suitable apps, completely eliminating the need for calls and anxiety. Patients can choose their doctors and book dates and times when they are available.  Medicines And Test Bookings Patient engagement can also be boosted with app-based access to medicines and other essentials, based on prescriptions issued by doctors. At the same time, they can also avail of features like booking tests across a variety of categories, either at healthcare centers or their homes, depending on what is feasible.  Why Mobile Health Apps Are The Future Of Better Patient Engagement  There are several advantages to using mobile health apps for stellar patient engagement. These include the following: Higher transparency in terms of doctor information, availability, costs, and patient information.  Easier management/access as far as patient details are concerned, with physicians being able to access data swiftly. Boosts interoperability across hospitals and enables better treatments.  Quicker physician/doctor consultation, higher quality of care with more personalization, and easy tracking of progress.  Greater control of patients over their healthcare treatments.  Saves the time and energy of patients, while freeing up resources at hospitals.  FAQs How can mobile health apps enhance patient engagement? Mobile health apps can enhance patient engagement with easier data management and access, appointment bookings, telehealth consultations, and higher control over healthcare treatments.  What features should a mobile health app have to be effective? Mobile health apps should have features for data integration, appointment bookings, consultations, prescriptions, payments, test bookings, notifications, and other helpful resources.  Are there any privacy concerns with using mobile health apps? Privacy concerns exist in terms of the proper management of confidential patient information in a secure manner by healthcare providers. The Cybersecurity and governance protocols have to be strong in this regard.  How can healthcare providers encourage patients to use mobile health apps? Healthcare providers can encourage more patients to use mobile health apps by advertising their offerings and promoting the key benefits of saving time, money, and energy. 

Read More »

Services

Industries

Company

Resources

Press Kit

MENU
Book a free consultation
CONTACT US

Let’s connect!

    Privacy Policy.

    Book a free consultation
    Almost there!

    Download the report

      Privacy Policy.