Tag: Digital Healthcare

Data Analytics in Health Insurance for Better Risk Management

2024 Trends: Data Analytics in Health Insurance for Better Risk Management

January 8, 2024

The health insurance industry has witnessed a rapid evolution in terms of digitisation, which has also swiftly transformed several other sectors worldwide. One of the biggest contemporary trends in health insurance (and poised to last for the foreseeable future) is the harnessing of data analytics to ensure superlative risk management among other benefits. Pharmacy managers, insurance companies, healthcare providers, and other industry stakeholders are already leveraging analytics to tackle issues of fast-growing healthcare costs. Insurance companies are already using them to identify high-risk patients and operate accordingly. Why Data Analytics is Indispensable From an operational standpoint, data analytics is indispensable for health insurance companies. They keep generating huge data volumes internally through sales and engagement but also get sizable chunks from varied external sources. When this data spreads out through multiple systems, it becomes an uphill task for insurers to effectively use and track the same. This is why analytics-based solutions that can fuse and consolidate data from multiple touch points and sources have gained relevance in recent times. A centralised data gathering system with consistent analytics and actionable insights is thus one of the key trends in health insurance currently. How Data Analytics Enables Better Risk Management Data analytics is undoubtedly contributing towards enabling insurers to manage their risks better. Here are some aspects that should be highlighted in this regard. These benefits have gained even more traction in recent years if one considers how insurers lose a whopping $40 billion per annum on account of fraudulent claims as per Gartner reports. Many insurance companies also estimate 10-20% of claims to be fraudulent while identifying less than 20% of the same. Suspicious and fraudulent behaviour and patterns can be identified with data analytics, with insurers creating diverse models to enable swift detection based on historical data and activities. For instance, analytics has reportedly helped the Czech Republic’s Allianz Insurance to save a whopping US$4.5 million annually by lowering fraudulent claims paid by it. This is just one of the instances that testify to the need for insurance companies to rapidly adopt data analytics into their operational frameworks. Analytics can be used to conduct risk evaluation in real-time which will help organisations respond swiftly in volatile scenarios. For example, in the case of auto insurance, an accurate assessment of risks posed by specific drivers will help insurers create more competitive premiums. Cars connected to the internet will help them gather large volumes of data accordingly. Insurance firms can now predict the chances of drivers being involved in accidents by analysing driving habits and behavioural data. Some other advantages of data analytics include easier customer lifetime value (CLV) prediction and prospective claim forecasting. FAQs How can data analytics contribute to more precise risk assessment in the health insurance industry? Data analytics can enable more accurate risk assessments in the health insurance sector. It can help identify fraudulent behavioural patterns and flag the same for review before the actual payout. What impact do 2024 trends in health insurance data analytics have on customer experiences and personalised offerings? 2024 trends in health insurance data analytics will have a positive impact on customer experiences. Insurers will be able to personalise their offerings for customers based on their behavioural data and also offer custom premiums with incentives for recommended and healthier actions.

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Protecting Patient Data in the Digital Age

Protecting Patient Data in the Digital Age

November 22, 2023

Patient data protection is the need of the hour for the healthcare industry, particularly when it comes to a more digital way of doing things. In fact, data privacy in healthcare is even more important, considering the growing costs of cyber-attacks in the sector. As per IBM’s Cost of a Data Breach 2022 report, $10.1 million is the average cost of a data breach in healthcare. This indicates 10% growth as compared to 2021 and a whopping 42% increase in comparison to 2020. The financial effect of data breaches covers costs related to responding to incidents, legal charges, remediation, and regulatory fines, if applicable.  At the same time, the sheer cost of a data breach goes well beyond regular financial implications for healthcare players. Digital age patient data protection is a must and if information is compromised, then it leads to lower confidence in the system and loss of business as patients go to other competitors. The long-term impact of data breaches can be immensely negative for healthcare players. In the current landscape, where protecting medical data goes hand in hand with digital innovation, there is a pressing need to ensure best practices at all operational levels.  Steps for Patient Data Protection  When it comes to ensuring healthcare data security, healthcare players should first evaluate the possible risks and then take measures to safeguard valuable data accordingly. The healthcare industry has become a major target for cyber-criminals owing to the sensitive nature of the data that it holds. Outdated systems and multiple entry points further scale up the risk levels in this case. A major attack point is through web applications with a view towards compromising patient data. These are applications and tools that healthcare professionals depend on these days. Cyber-criminals are always on the lookout for gaining unauthorised entry into patient data platforms.  Hackers have also come up with novel methods such as BOLA (broken object-level authorisation). This equates to the manipulation of the rules of the system along with ensuring unauthorised access to restricted information. They can thus go through confidential data and even erase the private records of patients. There is also the Internet of Medical Things (IoMT), which is a network of multiple devices transmitting data in real time for better patient care. While it offers innumerable advantages, the vast amount of sensitive information available makes it an easy target for cyber-criminals as well. Here are some steps that the industry can consider for protecting medical data.  Some Other Patient Data Protection Strategies  As can be seen, regular risk assessments should be conducted while implementing a defensive strategy that has multiple layers. Healthcare institutions should also be informed about emerging cyber-security threats and other vulnerabilities along with engaging experts to augment internal resources along with providing extensive management and tracking. Through the adoption of best practices in this space, healthcare players can minimise the risks of data breaches to a large extent.  FAQs What are the most pressing security challenges in protecting patient data in today’s digital healthcare landscape? Some of the biggest security challenges in safeguarding patient data include continual vulnerabilities related to unauthorised access, hacking threats, system compromises, multiple entry points, and the lack of proper awareness and education among employees.  How can healthcare organisations ensure compliance with data protection regulations while managing and storing patient information? Healthcare organisations can ensure adherence to data protection regulations while storing and managing patient information. They can do this by complying with requisite information handling and data privacy guidelines issued by the authorities in their respective regions, while also having proper data security protocols and audits in place.  What cyber-security measures and best practices should be adopted to safeguard patient data from cyber threats? Some of the best cyber-security measures and best practices for protecting patient data include vendor security measures, encryption, data minimisation, regular tracking and audits, access control, de-identification, and more.  What role do emerging technologies like blockchain and encryption play in enhancing the security of patient data in the digital age? Emerging technologies like encryption help make medical data unreadable to unauthorised individuals. At the same time, blockchain also enhances data security through smart contracts and inherent principles of cryptography, consensus, and decentralisation. It offers greater control over sensitive data and is near-impossible to tamper with. 

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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

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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. 

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