In the grand scheme of things, it’s becoming increasingly evident that data is the new black gold. Industries across the board are awakening to the realization that data is no longer just an afterthought or an add-on; it’s an essential component of success. In the 19th century, oil was the lifeblood of the global economy and politics. In the 21st century, data is controlled to take on the same critical role.

Of course, data in its raw and unrefined form is essentially useless. It’s only when data is skillfully gathered, integrated, and analyzed that it starts to unlock its actual value. This value can manifest in many ways, from enhancing decision-making capabilities to enabling entirely new business models. In the healthcare industry, data is playing a particularly pivotal role. Refined data is helping professionals make better-informed decisions, improve patient outcomes, and unlock new frontiers of medical research. The future of healthcare is all about data, and those who know how to wield it will undoubtedly emerge as leaders in the field.

However, healthcare providers’ timely access to real-time or just-in-time information can significantly enhance patient care, optimize clinician efficiency, streamline workflows, and reduce healthcare costs.

Investing in robust electronic health record (EHR) systems encompassing all clinical data is crucial for healthcare organizations to understand patient conditions and comprehensively predict patient outcomes.

Is Data a Real Game Changer in the Healthcare Industry?

The answer to whether the analytical application of existing data will shape the future of healthcare is a resounding “yes.” With advances in data-collecting tools and healthcare technology, we’re witnessing a new era of healthcare delivery that will revolutionize the industry.

Imagine a world where wearable medical devices warn you of potential health risks or medical advice apps offer personalized guidance based on your unique DNA profile. These are just a few examples of how cutting-edge technology is making its way into the healthcare space, enabling data-driven decisions that improve patient outcomes and drive down costs.

Real-time data is a game-changer for case review and clinical time management, allowing healthcare professionals to understand patient situations and forecast outcomes more effectively. To fully realize the potential of data-driven healthcare, healthcare organizations must implement robust data management systems that can store all clinical data and provide the necessary tools for data analysis. By doing so, healthcare professionals will be empowered to make informed decisions that enhance patient care, improve outcomes, and ultimately transform the healthcare landscape.

Also, read the best approach to testing digital healthcare.

How do you use data for a better future?

When it comes to healthcare, data is everything. However, with the massive amounts of data that healthcare professionals must contend with, the sheer volume of information can be overwhelming.
As the industry has shifted toward electronic record keeping, healthcare organizations have had to allocate more resources to purchasing servers and computing power to handle the influx of data. This has led to a significant surge in spending across the sector.

Despite the clear advantages of data-driven healthcare, managing such large amounts of information presents unique challenges. Sorting through and making sense of the data requires robust data management systems and advanced analytical tools. However, with the right approach, healthcare professionals can leverage this data to make informed decisions that improve patient outcomes and transform the industry.

How does data analytics benefit the healthcare industry?

A small diagnostic error can have devastating consequences in the healthcare industry, potentially costing lives. The difference between an actual positive malignant tumor and a benign one can be the difference between life and death. This is where data analytics comes into play, helping to eliminate the potential for error by identifying the most relevant patterns in the available data and predicting the best possible outcome.

Beyond improving patient care, data analytics can also assist hospital administration in evaluating the effectiveness of their medical personnel and treatment processes. As the industry continues to shift toward providing high-quality and reasonable care, the insights derived from data analysis can help organizations stay on the cutting edge of patient care.

With data analytics, healthcare professionals can harness the power of big data to identify patterns and trends, predict patient outcomes, and improve the overall quality of care. Healthcare organizations can optimize their processes by leveraging data-driven insights, minimizing errors, and ultimately delivering better patient outcomes.

Approaches of Data Analytics

Data analytics is a complex process involving various approaches, E.g., predictive analysis, descriptive analysis, and prescriptive analysis, including feature understanding, selection, cleaning, wrangling, and transformation. These techniques are applied depending on the type of data being analyzed.

Analysts must first understand the features and variables relevant to the analysis to derive insights from the data. From there, they can select the most relevant features and begin cleaning and wrangling the data to ensure accuracy and completeness.

Once the data has been prepared, analysts can apply various transformation techniques to derive insights and patterns. The specific methods used will depend on the nature of the data being analyzed but may include methods such as regression analysis, clustering, and decision trees.

Predictive Analysis

Analysts leverage sophisticated techniques such as relational, dimensional, and entity-relationship analysis methodologies to forecast outcomes. By applying these powerful analytical methods, they can extract insights from large and complex datasets, identifying patterns and relationships that might otherwise be obscured.

Whether analyzing patient data to forecast disease progression or studying market trends to predict demand for new medical products, these advanced analytical techniques are essential for making informed decisions in today’s data-driven world. By leveraging the latest tools and techniques, healthcare professionals can stay ahead of the curve, improving patient outcomes and driving innovation in the industry.

Descriptive Analysis

In the data analytics process, descriptive analysis is a powerful technique that can be used to identify trends and patterns in large datasets. Unlike more complex analytical methods, descriptive analysis relies on simple arithmetic and statistics to extract insights from the data.

Analysts can gain a deeper understanding of data distribution by analyzing descriptive statistics such as mean, median, and mode, helping to identify common trends and patterns. This information is invaluable during the data mining phase, assisting analysts to uncover hidden insights and identify opportunities for further analysis.

Prescriptive Analysis

In data analytics, prescriptive analysis represents the pinnacle of analytical techniques. Beyond simple descriptive or predictive analysis, prescriptive analysis offers recommendations for proceeding based on insights gleaned from the data.

This highly advanced analysis is the key to unlocking new opportunities in the healthcare industry, enabling professionals to make more informed decisions about everything from treatment protocols to resource allocation. By leveraging sophisticated algorithms and machine learning techniques, prescriptive analysis can identify the optimal path forward for any situation, helping organizations optimize processes, maximize efficiency, and drive better patient outcomes.

Gathering Real-time Data in Healthcare

Real-time data refers to data that is immediately obtained upon its creation and can be collected using various methods, including:

• Health Records
• Prescriptions
• Diagnostics Data
• Apps and IoTs

Real-time data is crucial for managing the healthcare industry’s patient care, operations, and staffing routines. By leveraging real-time data, the industry can optimize its entire IT infrastructure, gaining greater insight and understanding of its complex networks.

Examples of Real-time Data Technologies in Healthcare

Role of AI/ML in healthcare

Regarding medical diagnostics, the power of data analytics cannot be overstated. Thanks to cutting-edge machine learning and deep learning methods, it’s now possible to analyze medical records and predict future outcomes with unprecedented precision.

Take machine learning, for example. By leveraging this technology, medical practitioners can reduce the risk of human error in the diagnosis process while also gaining new insights into graphic and picture data that could help improve accuracy. Additionally, analyzing healthcare consumption data using machine learning algorithms makes it possible to allocate resources more effectively and reduce waste.

But that’s not all. Deep learning is also a game-changer in the fight against cancer. Researchers have achieved remarkable results by training a model to recognize cancer cells using deep neural networks. By feeding the model a wealth of cancer cell images, it could “memorize” their appearance and use that knowledge to detect cancerous cells in future images accurately. The potential for this technology to save lives is truly staggering.

RPA (Robotic process automation) in healthcare

The potential for RPA in healthcare is fascinating. By scanning incoming data and scheduling appointments based on a range of criteria like symptoms, suspected diagnosis, doctor availability, and location, RPA can dramatically boost efficiency. This would relieve the burden of time-consuming scheduling tasks from the healthcare staff and probably improve patient satisfaction.

In addition to appointment scheduling, RPA can also be used to speed up health payment settlements. By consolidating charges for different services, including testing, medications, food, and doctor fees, into a single, more straightforward payment, healthcare practitioners can save time and avoid billing errors. Plus, if there are any issues with cost or delays, RPA can be set up to email patients with customized reminders.

But perhaps the most exciting use of RPA in healthcare is data analysis. By leveraging this technology to produce insightful analytics tailored to each patient’s needs, healthcare providers can deliver more precise diagnoses and treatment plans. Ultimately, this can lead to better outcomes and an enhanced patient care experience.

Role of Big Data in Healthcare

In today’s world, the healthcare industry needs an innovation that can empower medical practitioners to make informed decisions and ultimately enhance patient outcomes. Big data is the transformative force that can revolutionize how we approach healthcare. With the ability to analyze massive amounts of data from various sources, big data can provide medical practitioners with the insights they need to understand better and treat diseases. By leveraging this data, doctors can develop more targeted treatments and therapies that have the potential to improve patient outcomes drastically.

Beyond the immediate benefits of improved treatment options, big data also plays a vital role in driving new drug development. Through advanced clinical research analysis, big data can predict the efficacy of potential new drugs, making it easier for scientists to identify the most promising candidates for further development. This is just one example of how big data is revolutionizing the way we approach healthcare, and the benefits will only continue to grow as we explore more ways to harness its power.

Finally, big data is helping healthcare practitioners to create focused treatments that are tailored to improve population health. By analyzing population health data, big data can detect patterns and trends that would be impossible to identify through other means. With this information, medical professionals can develop targeted treatments that can be applied on a large scale, ultimately improving health outcomes for entire populations. This is just one of the many ways that big data is changing the way we approach healthcare, and it’s clear that the possibilities are endless. As we continue to explore this transformative technology, there’s no doubt that we’ll discover even more innovative ways to leverage big data to improve health outcomes for patients around the world.

Wrapping Up

In conclusion, real-time data analysis is a transformative force in the healthcare industry that has the potential to revolutionize the way we approach patient care. With the ability to analyze vast amounts of data in real-time, medical practitioners can make faster and more informed decisions, resulting in improved patient outcomes and ultimately saving lives.

From predicting potential health risks to identifying disease outbreaks and monitoring patient progress, real-time data analysis is driving innovation in healthcare and changing the way medical professionals approach treatment. By leveraging cutting-edge technologies and advanced analytics tools, healthcare organizations can collect and analyze data from various sources, including wearable devices, electronic health records, and social media, to better understand patient needs and provide personalized care.

As the healthcare industry continues to evolve, it’s clear that real-time data analysis will play an increasingly important role in delivering better health outcomes for patients worldwide. Real-time data analysis can improve patient care, reduce costs, and save lives by giving medical practitioners the insights they need to make more informed decisions. The possibilities for the future of healthcare services are endless, and I’m excited to see the continued innovations that will arise from this transformative technology.

The post Real-Time Data Analysis and its Impact on Healthcare appeared first on Indium.

Remarkable medical innovations mark today’s healthcare landscape, where self-aware medical facilities are becoming a norm in healthcare units to assist patients and professionals with a more sophisticated experience. Tech giants, such as Google, invest time and money in research and development to incorporate advanced technologies that elevate, enhance, and empower healthcare systems with intelligence and automation. While technology is the overarching catalyst, the nuances of Generative AI, including models like GAN, VAE, Transformer-based models, RNN, LSTM, and Data Augmentation, reshape medicine, pharmaceuticals, medical equipment, and broader healthcare systems. Even if its daily applications might go unnoticed, the impact of Gen AI is undeniable. Its ability to replicate high-quality images, videos, and 3D models and generate text based on existing data patterns underscores its pivotal role in healthcare.

Be confident to invest as the Gen AI market is expecting a trajectory of USD 118.06 billion by 2032.

Recognizing the significance of large language models, Google has introduced its LLM tailored particularly for the medical domain, Med-PaLM-2, where users can extract insights of medical terms summaries from doctors; it also scored 85% in assisting users by answering complex medical queries without bias or potential harm. Thus, the world of medical science is crafting serious innovative solutions that provide assistance and serve as a lever for the discovery of many new inventions in the field of healthcare.

This blog delves into the less-explored progress of Gen AI that helped transform and revolutionize the medical field. Additionally, the blog highlights a significant milestone where Gen AI surpasses previous human achievements. It presents a healthcare system brimming with Gen AI applications that are set to reshape our engagement with medical science.

Exploring Gen AI applications in healthcare

The standout attributes of Generative AI 

Nature of learning: The transition from being a discriminator that just classifies or predicts outcomes to being able to generate new data samples based on input helped Gen AI stand tall as a remarkable evolution of artificial intelligence. This characteristic of Gen AI proved to be significant in cases where patient privacy was the greatest concern.

Unsupervised learning: The potential to explore the vast data feed and analyze them for significant correlations highlights Gen AI’s role in advancing healthcare analytics. As a result, it leads to explicit learning for Gen AI models to figure out the complex patterns that might hold essential information, such as a newfound synergy between two medicinal compounds.

Breakthrough discoveries: The ability to explore extensively by considering multiple dimensions of the existing drugs, understand their molecular formula, and propose new medicines that have an enhanced effect on targeted diseases makes Gen AI reliable and rapid technological dependence. Thus, the assistance to formulate new treatment plans by studying historical data of patients with a more significant percentage of success.

Iterative refinement: The adaption to learn from feedback, especially those in adversarial setups, continuously refine their outputs, leading to progressively better generation results compared to other subsets of AI. Thus, it acts as an accelerator in the innovation process, where it doesn’t stop after an inevitable discovery.

Wide adaption: The flexibility to get trained by vast inputs that range from data, sequential text, and images position Gen AI as an invaluable tool for the multifaceted health sector. This approach helps drive informed decisions, posing no barrier for medical professionals in deciding on the data format or other irrelevant jobs apart from diagnosis and consultation.

To be precise, the usage of Gen AI in healthcare has truly defined a new space for patients and professionals who look to articulate the profession of care and diagnosis more sophisticated and transformative in every aspect.

The evolving role of Gen AI in redefining modern healthcare

1. Bridging the gap in data access

With strict regulations on patient data utilization for training, Gen AI has now seen augmented importance through its powerful advanced model, which significantly creates synthetic data, thereby safeguarding the privacy and security of personal data. As the rare disease dataset is minimal and available only in small regions from particular patients, the need to look for vast data in some instances is solved through GAN’s ability to mirror original data. Various medical research and diagnoses can be subjected to ascertain the anticipated treatment through the reproduced data, thus causing no harm to humans. The adoption of GAN for its synthetic data creation through one of its neural networks, the generator aids a more considerable significance for the healthcare facility.

2. Optimizing trial run

Based on a sequential process, the experimentation on the trial run involving time series data of patients is analyzed through LSTM. It is famous for predicting outcomes through time-series data that include patient’s vitals, drug response time, progression of the disease, and other health indicators. As LSTMs are less prone to vanishing gradient problems, through their three gate operations(initial gate, forget gate, and output gate), they get trained on historical data that determines which part of the data is to be stored and discarded. This approach assists in predicting the outcome for various trial parameters. The Gen AI model helps finalize the positive trial after adjusting responses from past clinical trials in real-time from a group of patients. Thus, the LSTM’s role in processing and predicting from previous attempts helps the healthcare department to optimize the trial run with fewer amendments and better resource allocation.

3. Personalized treatments and care

The approach to queries of patients is forecasted through wearables by Gen AI and addressed personally. From collecting data or synthesizing the vitals of patients to structuring the data based on the NLP model, Gen AI’s role is significant. Training the model with numerous data helps learn and understand the medical terms. Once trained, the model can take large amounts of medical data and generate explanations based on the patient’s implications. The model can be interactive if integrated into a chatbot interface, where it can directly question the patients and provide recommendations or follow-up actions. Thus, the road to personalized care is vast, yet it can be streamlined by applying Generative AI, which is the future of many more advancements.

4. Molecular design for drug discovery

Working on medicine research is time-consuming, and discovering a new drug to treat an existing disease is a prolonged process that needs several tests, accuracy, and extensive research. The COVID-19 impact is an excellent example of the drug-discovery process, where many trial runs are still being processed to achieve a more stable molecular compound that reacts the best when projected at the target. Generative AI has significantly streamlined this journey. It rapidly suggests potential drug structures by analyzing chemical databases, ensuring high efficacy and safety. By examining extensive biological data, it identifies and confirms potential drug targets, boosting the success of drug development. Additionally, it predicts drug interactions, advocates for repurposing existing drugs, and facilitates personalized treatments by analyzing genetic and clinical data, aiming for optimal results with fewer side effects.

Paige received the first FDA approval for its AI-based prostate detection, which showed 70% accuracy.

5. Medical simulations

Administering experimental treatments and conducting drug research directly on patients is unethical and dangerous. To prevent this, the medical community is turning to Generative AI models to create virtual medical environments, known as medical simulations. These simulations use synthesized patient data to create virtual patients, encompassing past medical histories, current vital signs, and genetic information. This approach ensures that absolute patient privacy remains uncompromised. These virtual patients are then integrated into VR platforms, offering medical professionals, budding surgeons, and students a safe space to practice diagnoses and explore treatment options.

Gen AI’s significance in generating medical images is especially beneficial for training radiologists or simulating uncommon medical conditions. The breakthroughs in medical simulation are particularly evident in drug research, where the effects of new drugs are virtually tested on patients, leading to more personalized treatment plans. Additionally, Gen AI can simulate surgical procedures, predicting real-time responses of tissues, potential bleeding, and other surgical outcomes. In essence, Gen AI’s role in medical simulations offers invaluable insights and hands-on experience without risking human health.

6. EHR

With digitalization completely taking off-guard the old practices of taking medical notes, electronic health records act as the perfect replacement for surgeons and assist in lowering the administrative expenses in most healthcare facilities. This record acts as a repository where the entire history of patients is in place for medical professionals to view and arrive at immediate recommendations when contacted for minor consultations. Using the EHR has made physicians invest 4.5 hrs daily, and their interactions with patients have significantly reduced. Thus, leading IT giants Microsoft and Epic, the pioneer and premier in clinical software, are working to integrate the Gen AI advancement ChatGPT to revert to patients automatically. This approach is instrumental for the healthcare system to regulate and be optimized in responding to queries that don’t require immediate attention or action.

Gen AI breakthrough impact in early lung cancer detection

Assessing critical medical conditions in advance has to be the most outstanding innovation humankind has ever discovered. The application of the deep learning module has assisted the team of MIT researchers in building an AI model that detects the risk of lung cancer for patients in the future. Citing as a valuable and imperative need, with the risk of cancer detection and treatment, the team developed Sybil, the AI that successfully endured the analysis of Low-dose computed tomography (LDCT) image data without assistance from a radiologist. As the model is designed to predict early lung cancer, the imaging data used has to have minimal impact on cancerous cells. This posed a challenge for the researchers, given that early-stage lung cancer typically affects only a small portion of the lung — a minute fraction of the hundreds of thousands of pixels comprising each CT scan.

To assist Sybil in early detection, the team marked 100 CT scans with visible lung cancer marks before projecting the model without signs of cancer. Sybil outrageously predicted the lung and the side that would eventually develop lung cancer, which was not visible to humans. Thus, the predictive power of Sybil gave confidence to the team to screen the lungs of various people, especially those who are non-smokers, who thought they wouldn’t be infected by lung cancer. Also, with no advanced technologies in the early detection of lung cancer, Sybil’s assistance proved to be a reliable diagnosis for patients and professionals who could save millions of lives.

Innovative alliances for healthcare: Indium Software’s Gen AI solutions

Employing the right expertise for your technological solutions is essential in your innovations and advancements. Indium Software assistance is an undeniable opportunity for healthcare professionals who wish to reap the maximum benefits of Gen AI in their organization. With the right combination of NLP expertise, Gen AI consultants, and other experienced professionals, the company offers services that stand tall among its competitors and peers. With endurable support extended to clients, Indium Software ensures they bridge the gap in assessing the latest use of Gen AI technology that assists medical professionals and healthcare systems in streamlining and optimizing various business operations. Leverage Indium Software’s LLM model, designed by 100+ data scientists and 150+ data analytics experts, with the following capabilities:

Conversational systems:

Offer tailored patient assistance and healthcare interactions using AI-powered medical chatbots and diagnostic inquiry bots for intuitive patient conversations.

Summarization:

Condense detailed medical documents or studies for quick insights into diagnoses, treatments, and critical observations, ensuring efficient information retrieval.

Transformation:

Use advanced machine learning to translate medical content across languages and classify text, like segregating patient comments and clinical notes.

Inferring and text expanding:

Enrich medical documents by filling in gaps and expanding on partial text. This boosts the precision of analyzing patient sentiments, tracking feedback, and intelligent medical searches.

Conclusion

In summary, the adoption of Gen AI has directed a new era in healthcare, achieving feats once thought beyond human capability. Introducing such cutting-edge tools has simplified complex healthcare processes, enabling tailored patient care, expedited diagnoses, and novel solutions to medical dilemmas. Gen AI offers a promising future, fostering optimism among healthcare professionals and patients as it helps tackle formidable diseases. Its capabilities range from alleviating doctors’ administrative tasks to early disease detection and aiding patients in accessing medical data. The dominance of Gen AI in healthcare is poised to last, and its ethical and responsible adoption, primarily through collaborations like Indium Software, is set to redefine a more streamlined and individualized healthcare landscape.

The post Generative AI: A Catalyst for Rapid Insights in Healthcare Analytics appeared first on Indium.

Developing software applications involves leveraging technologies and established practices. When building a healthcare solution like an interactive chatbot, integrating Chat-GPT, Test-Driven Development (TDD), and the Cucumber framework enhances development and delivers user-centric solutions.

a) Chat-GPT, an OpenAI language model, generates human-like text responses. By integrating Chat-GPT into a chatbot, developers create conversational user experiences using natural language processing.

b) Test-Driven Development (TDD) prioritizes creating automated tests before coding. Following TDD ensures the chatbot functions as intended, meets requirements, and provides expected responses.

c) Cucumber, a popular Behavior-Driven Development (BDD) tool, enables collaboration between technical and non-technical stakeholders. It creates human-readable feature files that describe system Behavior. With Cucumber, developers validate the chatbot, generate tests, and maintain a shared understanding of requirements.

Why do we need integrated technology in healthcare applications?

Without Chat-GPT and Test-Driven Development in the Cucumber framework, the healthcare industry may face several challenges and limitations in terms of communication, efficiency, and quality assurance.

2.1 Let’s understand how these technologies can support and enhance healthcare applications:

Communication Challenges:

• Limited interactive communication with patients, leading to misunderstandings.
• Inability to provide personalized responses, resulting in unsatisfactory experiences.
• Difficulty handling patient inquiries efficiently.

Efficiency Concerns:

• Manual handling of queries is time-consuming for healthcare professionals.
• Inability to automate tasks, increasing workload.
• Potential errors in responses due to human factors.

Quality Assurance Limitations:

• Inconsistent responses across healthcare professionals.
• Difficulty validating healthcare applications, leading to bugs and security issues.
• Inadequate documentation of software requirements.

2.2 Integrating Chat-GPT and Test-Driven Development in the Cucumber Framework addresses these challenges:

Improved Communication:

• Chat-GPT enables interactive and personalized communication with patients.
• Test-Driven Development ensures accurate and reliable responses.

Enhanced Efficiency:

• Chat-GPT automates query handling, freeing up professionals’ time.
• Test-Driven Development identifies and addresses issues early.

Quality Assurance and Documentation:

• Test-Driven Development with Cucumber validates application functionality.
• Cucumber supports clear documentation and a shared understanding of requirements.

This integration enables developers to build intelligent and reliable chatbots, improving user experiences in healthcare and other domains.

How do we integrate the technology into a healthcare application?

Flowchart on the end-to-end process structure

A detailed example of how we can integrate Chat GPT, Test-Driven Development (TDD), and the Cucumber framework in a healthcare solution:

3.1 Define feature files:

Create a feature file using the Gherkin syntax provided by Cucumber.

For example, let’s consider a feature file called “HealthcareChatbot.feature” with the following scenario:

3.2 Write failing step definitions:

3.3 Implement the chatbot system:

To integrate the Chat GPT API using Python, follow these steps:

a.  Set up the project:
• Create a new directory and set up a virtual environment.

b. Install the necessary libraries:
• Install the required Python libraries (e.g., Flask, requests).
• Install the Chat GPT API library, if provided.

c. Create the chatbot module:
• Create a Python file for the chatbot and import the necessary libraries.
• Set up Flask for user interactions.

d. Implement user input processing:
• Define a Flask route to receive user inputs.
• Extract the user’s query from the request and pass the query to the chatbot logic for a response.

e. Integrate with the Chat GPT API:
• Import the API library or module and authenticate with valid API credentials.
• Make a request to the API with the user query and retrieve the generated response.

f. Process and format the API response:
• Extract relevant information from the response in a user-friendly format.

g. Implement the chatbot logic:
• Handle healthcare queries and generate responses.
• Integrate with external healthcare APIs and databases if needed.

h. Connect the chatbot module with Flask:
• Define routes and endpoints for interactions and responses.
• Invoke the chatbot logic and return the response.

i. Test and refine:
• Interact with the chatbot through defined routes for testing.
• Continuously improve based on user feedback and requirements.

j. Deploy and scale:
• Deploy the chatbot on a suitable hosting platform.
• Configure the infrastructure for scalability.

Here is an example of how we can implement the chatbot module that handles user inputs and generates responses based on healthcare-related queries:

Flowchart on explaining a simplified healthcare chatbot

This is a simplified implementation of a healthcare chatbot in Python. The process_user_input function generates responses based on predefined rules for healthcare queries. If a query doesn’t match the rules, it calls the get_chat_gpt_response function to fetch a response from the Chat GPT API. The get_chat_gpt_response function sends a POST request to the Chat GPT API endpoint with the user query as the prompt.

The API response is extracted and returned as the chatbot’s response. To customize the chatbot, expand the process_user_input function for more healthcare queries and advanced logic. Remember to replace ‘YOUR_API_KEY’ with your actual Chat GPT API key.

Benefits of Integration

1. Engaging experiences: Chat-GPT creates interactive and satisfying interactions.

2. Reliable system: TDD ensures requirements are met and bugs are caught early.

3. Collaborative communication: Cucumber supports BDD for inclusive behaviour definition.

4. Iterative development: TDD enables incremental enhancements and easier maintenance.

5. Clear requirements: Cucumber’s feature files serve as executable documentation.

6. Faster feedback: TDD and Cucumber provide quick issue identification and adjustments.

Combining Chat-GPT with TDD in the Cucumber framework builds a reliable and user-centric chatbot. TDD’s iterative approach and Cucumber’s collaboration enhance development and communication, resulting in realistic and high-quality responses that meet requirements.

Conclusion

The implementation is a healthcare chatbot system that integrates the Chat GPT API for realistic responses. It uses TDD principles with the Cucumber framework. Developed in Python with Flask, the chatbot processes user inputs, integrates the Chat GPT API, formats responses, and implements healthcare logic. The Flask web server handles user interactions, while the chatbot handles healthcare queries and potentially integrates with external healthcare APIs or databases.

The post Enhancing Healthcare Solutions with Chat GPT, TDD and Cucumber appeared first on Indium.

The healthcare paradigm is shifting from a reactive approach to a proactive and holistic model. Preventive care is important for staying healthy and identifying problems early before they lead to other complications or become more difficult to treat. While early intervention has proven instrumental in advancing diagnostics and treatments, a critical element has been missing until now: the incorporation of social determinants of health (SDOH). Recognizing that health outcomes are intricately woven into the fabric of our lives, the integration of SDOH into preventive care emerges as a transformative solution.

Beyond genetics and clinical data, social determinants encompass factors like socioeconomic status, living conditions, education, and access to nutritious food. By embedding these key influencers into preventive care, healthcare providers gain an unprecedented understanding of their patients’ lives, empowering them to offer personalized and proactive interventions.

Social Determinants of Health: Impact on healthcare outcomes

The non-medical elements that affect health outcomes are referred to as social determinants of health (SDOH). Socioeconomic position, education, physical environment and neighborhood, job, and social support systems are a few of these variables. SDOH has a major effect on health and can impact healthcare outcomes in a number of ways.

For example, a patient with a lower socioeconomic status is more likely to have chronic diseases, such as diabetes and heart ailment. By understanding this patient’s social determinants, a healthcare provider can recommend preventive care measures that are tailored to their needs, such as financial assistance for medication or enrolling them in wellness programs.

Patient 360: A holistic view of patient data

Patient 360 is a comprehensive view of a patient’s health information, including their medical history, social determinants, and other relevant data. By integrating SDOH into patient 360, healthcare providers can gain a better understanding of the factors that are affecting their patients’ health and make more informed decisions about preventive care.

Here are some of the benefits of leveraging SDOH parameters in the patient 360 framework:

Better patient care: Integrating SDOH elements into the patient 360 approach helps improve treatment efficiency by empowering physicians to address the factors that influence healthcare outcomes. This can save time and resources, which can be used to provide better care for patients.

Enhanced patient engagement: Addressing SDOH factors helps enhance patient engagement by giving patients more awareness of their health data. This can lead to patients being more involved in their care management and being more likely to follow treatment plans.

Clinical notes to actionable insights: Physician notes record important patient medical histories, symptoms, demographics, and clinical data. These observations provide a holistic picture of the patient’s health. SDOH factors are important predictors of preventive care needs, which is why it is important to include them in patient records.

The integration of SDOH into patient 360 is a promising way to improve preventive care and achieve better health outcomes for all patients.

Manual SDOH data extraction: Typical challenges in the current system

Manually extracting social determinants of health (SDOH) elements, poses numerous challenges that can hinder the efficiency and accuracy of the process. SDOH data is often embedded in unstructured sources such as physician notes, medical records, or social service assessments, making it laborious and time-consuming for healthcare professionals to extract relevant information. Here are some of the difficulties associated with manual data extraction for SDOH:

Unstructured data: SDOH elements are often scattered throughout free-text narratives, that lack a standardized format.

Human error: Human analysts are susceptible to making errors during data extraction, leading to inaccuracies in the collected information.

Incomplete data capture: Due to the sheer volume of information, manually extracting SDOH elements from various sources may result in incomplete data capture.

Limited scalability: As healthcare organizations grow and data volumes increase, manual data extraction becomes less scalable and impractical.

Cracking the code of health: Indium’s SDOH machine learning model 

Indium’s expertise in developing the SDOH ML model is based on two pillars: NLP technology and a deep understanding of the healthcare landscape. With a team of experts in data science, engineering, and healthcare, Indium is at the forefront of using AI to transform preventive care.

Indium’s journey began with a recognition of the importance of social factors in determining health outcomes. The company’s ML model is designed to identify and address these factors, which can help improve the health of individuals and communities. Recognizing that manually extracting these factors from unstructured physician notes is labor-intensive and prone to errors, Indium sought to create an efficient and accurate solution. Leveraging Natural Language Processing (NLP) techniques, the team precisely crafted a robust ML model that swiftly identifies key social determinants hidden within vast amounts of textual data.

The success of Indium’s SDOH ML model lies in its ability to provide healthcare providers and payers with invaluable insights. By seamlessly integrating social determinants into preventive care, the model empowers stakeholders to offer personalized preventive interventions, optimize patient care, and drive cost savings within the healthcare ecosystem.

SDOH ML model

ML techniques can be used to identify and extract SDOH from physician notes. These techniques can identify patterns in text, such as the presence of certain words or phrases that are associated with SDOH. For example, the phrase “food insecurity” might be associated with the SDOH of food insecurity. By using the SDOH ML model, healthcare providers can make right interventions to help improve healthcare outcomes and reduce costs.

Once SDOH have been identified and extracted from physician notes, they can be integrated into preventive care management. This information can be used to provide a more comprehensive understanding of the patient’s overall well-being and to develop a more personalized treatment plan.

The power of precision: Partner with Indium

As a leading healthcare service provider and a leader in the digital engineering space, Indium has developed the SDOH machine learning model. Understanding the profound influence that social factors have on health outcomes, and recognizing the value of this information is crucial to bring transformative advancements in patient care, the SDOH model is trained to accurately extract social factors from patient records. Beyond improving patient care, the integration of social determinants also serves as a strategic tool in reducing healthcare costs by proactively addressing health issues. Unlike the traditional method, our model is 90% accurate and can identify SDOH attributes from thousands of patient records in a matter of seconds.

The post How the SDOH machine learning model improves patients’ health and your bottom line appeared first on Indium.

Having the right solution will also help with garnering insights from data stored in electronic medical records (EMR). These insights will help with the following: 

• Improve patient treatment decisions and deliver better healthcare outcomes.
• Reduce the need to duplicate testing.
• Better diagnosis
• Minimize prescription errors.
• Overall, deliver patient success at lower costs.

This will require a health information exchange that provides healthcare professionals with access to clinical data, public health information, and data on healthcare spending and activity from multiple sources. As patients become the epicentre of the healthcare ecosystem, it is important to have the right engagement apps that help patients have an end-to-end experience on their journey. 

Patient 360 – The app built by Indium is precisely focused on that paradigm.

The solution offers a reusable UI/UX model that can jumpstart any patient engagement mobile app.

It can be connected to any aggregated data store that encompasses data from multiple EMR systems.

Patient 360 is a native cloud app that supports both iOS and Android.

It helps patient on:   To enrol with clinical systems, practise management systems, and an aggregated clinical data store.   Understand the body’s vital parameters based on the last recorded details from the EMR.   Gain insights on the diagnosis:   It includes the disease type, symptoms, and physician observations.   Report Navigation & Delivery Queue.   Department/Test Wise Investigation Collection.   Find my Doctor/PCP:   Find your PCP easily: Search & locate trusted primary care doctors based on specialty, network, and distance.   Verified credentials: Access provider credentialing details to ensure your PCP’s qualifications and expertise.   Learn about Health Plan and Claims   Information on the health plan and benefits, along with cost-sharing arrangements and details about OOP expenses.   Details on claims processed, payment status, and clear segmentation on what plan Vs members paid.   Schematic representation of how the patient is performing against the goals set by the physicians or the care management team.   Medication Details: Provides a comprehensive list of all medications prescribed based on the disease type   Member notification channels allow patients to access the educational videos and blogs posted by the care management team for the specific disease type.

Additionally, check out this interesting Success Story on the Decision to Launch the Drug Lifecycle App.

Drive Healthcare Data & Analytics Innovation with Indium Software

Indium Software has been working with clients across the healthcare spectrum, including health-tech firms, ISVs, payers, providers, home health service providers, diagnostics companies, pharmaceutical companies, and biotech companies, for more than 20 years. The healthcare technology services vertical at Indium is one of the fastest-growing practises. It includes: 

• Delivering complex, techno-domain projects such as TeleICU applications
• Building data analytics solutions such as patient intelligence from EMR dumps
• Automating the conversion of telehealth interactions between physicians and patients into a claims-submissible medical record

Indium is currently working with several large players across the healthcare ecosystem to enable digital technology enablement such as app modernization, cloud enablement, mobile development, etc. We also deliver core business services such as benefit configuration, claims testing, and Medicare and Medicaid enrolment platform support. 

Our capabilities include developing domain-specific solutions leveraging our rich knowledge and experience with the following IP-based solutions: 

• iDAF – (Indium Data Assurance Framework) is an all-inclusive solution that accelerates validation of heterogeneous target and source datasets for data quality, completeness, integrity, and consistency. 
• AI/NLP platform – report extraction from EMR data and PII masking

We have proven experience in healthcare and are the partner of choice for leading healthcare organisations in their product modernization journeys. We have a formidable team of 250+ techno-domain leaders and SMEs and partnerships with technology leaders such as AWS, GCP, MS Azure, Mendix, and Striim to fast-track your organization’s technology transformation programme. 

We work with industry-recognised, in-house solutions to accelerate value delivery, including our home-grown solutions like teX.ai, Gravity, uphoriX, and iBrix, making us highly competitive. 

Some of our solutions include:

• Remote Patient Monitoring: Indium developed a telehealth and remote patient monitoring portal and mobile app with a comprehensive clinical workflow, videoconferencing, and a real-time collaborative channel between physicians, carers, and ICU staff. 
• Security and Compliance: Healthcare is a highly regulated industry, and there is a strong need to protect patient data. Indium develops secure solutions for permission-based access that meet regulatory and compliance needs. 

FAQs

The post Patient 360: Why Healthcare Enterprises Need an Intelligent Patient Management System appeared first on Indium.

To create policies and procedures that promote the greatest possible patient outcomes, quality assurance (QA) teams at healthcare facilities work across the system. That entails making sure that a wide range of rules, guidelines, and laws at the federal, state, and local levels are followed, as well as devising internal strategies to promote the provision of high-quality healthcare and the general wellbeing of the community the organization serves.

What is healthcare administration ecosystem?

The management and direction of healthcare organizations, such as hospitals, clinics, and other forms of healthcare institutions, are referred to as healthcare administration. To provide healthcare services to the public, a variety of various organizations and stakeholders collaborate within the healthcare administration ecosystem.

These stakeholders include the administrative employees who manage the daily operations of healthcare institutions as well as healthcare providers like doctors and nurses. The insurance industry, governmental entities, and regulatory bodies are all part of the healthcare administration ecosystem, and they all contribute to the development and smooth operation of the healthcare system.

How do organizations use healthcare administration ecosystem

The healthcare administration ecosystem is used by healthcare organizations to plan and control the provision of healthcare services. This involves overseeing the different divisions and jobs performed by a healthcare facility, including clinical care, accounting, human resources, and information technology. Healthcare administrators also make sure that the facility is meeting the needs of the community and patients, as well as all applicable laws, regulations, and standards.

Additionally, healthcare organizations can work with insurance firms, regulatory bodies, and other healthcare organizations to enhance the effectiveness and quality of care by utilizing the ecosystem for healthcare management. For instance, a hospital might collaborate with an insurance provider to create a patient payment schedule or with a regulatory body to make sure the institution complies with all relevant safety and quality standards.

Overall, the delivery of healthcare services depends heavily on the complex and interrelated healthcare administration environment. Healthcare providers can provide better care to their clients and the public at large by cooperating with one another.

What are the issues with healthcare administration ecosystem based on legacy technologies?

When healthcare companies rely on old technologies inside the healthcare administration ecosystem, several problems may occur.

One problem is that older technologies might not be able to keep up with the healthcare system’s evolving needs and rising demand. For instance, the volume of data and information produced by contemporary healthcare facilities may be too much for older systems to handle, causing bottlenecks and inefficiencies. Additionally, it may be challenging to interact with or exchange data with other businesses within the healthcare administration ecosystem if legacy technologies are incompatible with more modern systems or technology.

Read Our bi-weekly publication feature findings

The fact that outdated technologies might not be safe or in compliance with present laws and standards is another problem. Data breaches and cyberattacks are more likely as the healthcare sector grows more digital, endangering patient privacy and endangering the business. Legacy technologies might not be protected against these attacks by the proper security measures, which could result in regulatory penalties and reputational harm.

Finally, maintaining and upgrading legacy technologies can be costly because they may need specialized resources and expertise that are hard to come by. It may be difficult for the companies to invest in more cutting-edge and efficient technologies as a result of this drain on resources.

What are the needs of a modern-day healthcare administration ecosystem using QA and a data segregation framework?

Using a quality assurance (QA) programme with data segregation architecture can help meet several needs of the contemporary healthcare administration ecosystem, including the following:

A QA program can assist healthcare organizations in locating and addressing areas where patient care delivery can be improved. This could entail gathering and examining data on patient outcomes, running audits and reviews, and putting right any problems that are found. Sensitive patient data can be protected by healthcare organizations by being divided into several categories or “segments” according to the level of sensitivity. This can lessen the likelihood of data breaches and help prevent unauthorized access to sensitive information.

Healthcare organizations can comply with the requirements of numerous laws, regulations, and standards, such as the Health Insurance Portability and Accountability Act, by using both QA programmes and data segregation frameworks (HIPAA). These programmes can assist healthcare businesses in operating more effectively and cost-effectively by detecting and correcting inefficiencies and waste. 

Overall, the QA programmes and data segregation frameworks can assist contemporary healthcare administration ecosystems in providing patients with care that is higher-quality, more effective, and more cost-effective while also addressing the needs of all stakeholders in the healthcare ecosystem.

The Success Story

The client is a leading global provider of advisory solutions for health plans and a range of value-added services in the healthcare technology sector. Utilizing technology to provide solutions that ease business administration procedures, healthcare partner management, and automated medical care is the key to their services.

What were the issues with our client’s administration ecosystem?

• The client’s business-admin management portal was updated from the legacy systems, which had problems with data operations in siloed databases for distinct healthcare partners and their associated members.
• The system was upgraded with a multi-tenancy structure and segregated databases that demonstrate efficient querying and analysis.
• The Data Integrity and Security were in jeopardy during the system upgrade process and necessitated extensive Validations.

What are the business requirements?

• To confirm that the Data Segregation Framework is suitable for mapping access privileges and possible data breach escalations.
• Verify the data integrity of all aspects of the business flow, including on boarding, profiles, health plans, and the business rules that govern them.

How we did it

• To make sure there are no gaps in test coverage, a strong test strategy was defined and then carried out in accordance with the business flow.
• Developed a reliable test design requires in-depth knowledge of the Data Segregation Framework at the functional and database levels.
• In-depth business rules were included in the workflow’s on boarding process and customizations based on plan choices, including setting up benefit amounts and caps, restricting shipments, activating emergency plans, etc.
• Logical QA checks on the application of “security filters” for Partner group access. This QA criteria was fulfilled by performing user hierarchy-based tests; only the right partners and their members shall have access to information that is relevant to them.
• Information Look-up Integrity has been confirmed for the correct fetch from the data tables for all information request permutations.
• Conducted responsiveness and data-manipulation tests.

What are the results we have delivered?

• Since data segregation is a process-intensive exercise, knowing the data orchestration in and the numerous business rules in the end-to-end workflow were part of Indium’s test strategy.
• Beyond just data validations, Indium’s CoE evaluation of the entire project resulted in functional improvements that the client effectively adopted and applied.
• Demonstrated domain expertise and infused the right flavor of data security into the design of test cases.

If you are unable to find the perfect solution, we are happy to assist you in utilizing technology in your company. Contact us at (888) 207 5969 or (800) 123 1191 for more information or send an email to info@www.indiumsoftware.com.

The post How to Enhance Healthcare Applications with QA in Data Segregation Frameworks: Indiums’ Expertise appeared first on Indium.

To provide these services, healthcare providers such as doctors, nurses, medical administrators, insurance companies, government agencies, medical equipment manufacturers, and pharmaceutical companies must work together. Keeping track of all these areas and getting results when needed is a challenge for today’s healthcare providers.

Diagnostics Management Applications play an important role in assisting healthcare providers and patients to get the reports on time. Let us see how by reading the blog below.

What is Diagnostics Management Application

DMA – A Diagnostic Management Application is a software solution that aids healthcare professionals in managing the diagnostic testing procedure for their patients. DMAs are frequently used to aid in problem identification and troubleshooting, performance optimization, and downtime prevention in sectors like manufacturing, automotive, aerospace, and healthcare.

Users can often plan and monitor diagnostic tests, gather and analyze data, and produce reports and warnings using DMAs. Additionally, they might have capabilities for predictive maintenance, root cause analysis, and error tracking. To give a complete picture of the machinery and procedures, DMAs are frequently combined with other tools and systems, such as enterprise resource planning (ERP) systems and maintenance management systems.

Through proactive maintenance and the early detection of issues, a DMA’s major objective is to increase efficiency and reliability. Employing a DMA enables businesses to lower the costs related to unscheduled downtime while also enhancing the overall efficiency of their machinery and procedures.

How do organizations use Diagnostics Management Application?

To increase productivity and dependability, organizations use diagnostics management applications (DMAs) in a variety of ways, including the following:

DMAs gather data from diagnostic tests and give tools for analyzing that data to discover faults and possible difficulties. They also let users to schedule and track diagnostic tests for equipment, systems, or processes, ensuring that all relevant tests are conducted on a regular basis.

DMAs can provide reports and warnings based on the findings and analysis, assisting users in identifying and prioritizing problems. The ability to track errors and assist users in determining the source of difficulties allows companies to take corrective action and avert more problems.

Employing a DMA enables businesses to increase performance overall while enhancing the effectiveness and dependability of their machinery and operational procedures.

What are the issues with Diagnostics Management Application Based on Legacy Technologies

Modern diagnostic management software may contain more features than legacy systems, which can limit the usefulness of the system and the standard of care given to patients. It may be challenging to share patient data and coordinate care when legacy systems are unable to communicate with other systems in an efficient manner.

The user interfaces of legacy systems may frustrate healthcare personnel, which could result in overall decline in the quality of patient treatment.

Legacy systems might be more susceptible to cyber assaults due to security flaws, which could expose private patient information. It could be more challenging to manage and maintain, needing more effort and resources to keep them up and running. Higher expenses for healthcare providers may result from this.

Read this informative blog on: Top 5 Predictive Analytics Applications in Healthcare

What are the needs of a modern-day Diagnostics Management Application using Mendix?

To be effective and efficient, a modern diagnostics management application (DMA) employing Mendix a low code platform may need to adhere to several standards. A Mendix-based contemporary DMA might require the following things:

DMAs may need to be flexible and configurable to fulfil the unique needs and specifications of various businesses and sectors. They must be able to adjust their scale up or down as necessary to consider changes in the quantity of diagnostic tests being run or the size of the company.

DMAs may need to be integrated with other systems, like enterprise resource planning (ERP) systems or maintenance management systems, to provide a comprehensive perspective of equipment and operations. They should have an easy-to-use interface that is understandable even to users with limited technical skills.

To secure sensitive information and adhere to applicable regulations, the applications should be designed with data security and privacy in mind and be easily accessible from a variety of devices and allow users to access and utilize the system from anywhere.

Read on blog on: The Best Low Code Development Solution for Startups & SMBs

The Success Story

The client is a well-known expert in MRI diagnostics and offers image diagnosis services across several US locations.

What were the issues with our client’s Diagnostics Management Application?

The client’s system has a significant amount of patient data as well as other pertinent information because it is one of the busiest diagnosis centers.

This greatly complicated the process of tracking payments and delivering reports, etc. A platform is required to make talks between medical facilities and the lawyers who represent the patients easier. To manage all approvals, billing, delivery reports, and payment structure, the client wanted to create an application.

What are the business requirements?

The client required an application to achieve the following corporate objectives while facilitating simplicity and automation inside the current system.

• Develop an application that integrates delivery report, billing, and approvals and improve the case management.
• Assistance with HL7 talks for the attorney with other medical facilities.
• Scalable architecture that can accommodate the portal’s future growth and association.

How we did it

Utilizing Mendix, we created an application based on a business need that enabled the following features

• An innovative system to improve end-user usability with a strong emphasis on streamlining the approvals and invoicing process was created.
• Interaction between the application and EXA to convert HL7 files into regular files.
• Developed HL7 engine integration.
• Lowered the need for manual intervention in the approvals and denials processes, which also decreased the need for communication via FAX and email.

What are the results we have delivered?

• After the installation procedure was complete, encrypted data transfer boosted data security by 93%.
• The efficiency and productivity of the stakeholders increased as the approval and rejection processes were made simpler.
• Automated billing conversion reduced manual involvement by up to 60% when switching from paper billing to electronic billing.
• Attorneys were able to monitor and track pending payments in all the billing centers.

If you can’t find the ideal answer, we are pleased to help you make technology work for your business. For further information, contact us by email at info@www.indiumsoftware.com. or by phone at (888) 207 5969 or (800) 123 1191.

The post Developing a Diagnostics Management Application with Improved Data Security Using Mendix Solution appeared first on Indium.

The secret to this magnificent growth is the use of technologies that are guided by AI and workflow digitization in the health industry.  We now have multiple healthcare tools, thanks to AI, that provide faster and more efficient medical solutions.

As a medical professional, incorporating AI into your healthcare business will give you the following benefits and more:

• Enhanced efficiency
• Easy access to medical services
• Better data security
• Increased productivity
• Profit maximization and cost reduction

In this article, we will explore the top AI trends that have the most impact on the healthcare industry, explaining the different ways in which they have impacted or changed how medical professionals operate.

Top 7 AI Trends that Transform the Healthcare Industry

1. Robotic Surgeries

Investment in medical devices that use robots has increased greatly with the introduction of AI in the medical industry. The success rate of this trend is so good that it has become the new idea for the future of surgery and is expected to have incredible adoption rates in the coming years.

Surgical robots help identify critical insights and state-of-the-art practices by browsing through millions of data sets with the help of ML techniques. It allows the surgeons to focus on the complex aspects of the surgery.

AI has also been helping surgeons in preoperative planning and intraoperative guidance.

2. Remote Health Monitoring/Telehealth

Telehealth is the process of using technology to get remote health care and help people manage their ailments better without having to go to the hospital. Aside from the innovations for surgery, AI is set to revolutionize how we monitor our health, especially from home. 

AI in Telehealth helps doctors to make data driven decisions, it gives access to real-time data which eventually improves patient experience and health outcomes.  

3. Administrative Workflow Automation

When most people think about healthcare, they only consider the act of treating patients. However, administrative duties are equally important as they determine many productivity factors in any industry or company, irrespective of its niche.

A lot of work is involved in keeping a medical facility running without problems – from getting insurance authorization to following up with patients about medical bills to ensuring useful data is collected and recorded properly. AI helps automate the administrative workflow process and resources to make the system work as efficiently as possible.

4. Digital Therapeutics/Primary Care

Digital therapeutics, otherwise known as DTx, is a proof-based treatment based on patients’ behaviors with the aid of software. Digital therapeutics is expected to improve the healthcare industry when it comes to treatment effectiveness and accessibility. The program observes the feeding, sugar level in the blood, blood pressure, exercise, and medication to improve the treatment of patients.

Medical practitioners and patients can trust the recommendations given through digital therapeutics, which guide the treatment of a couple of common illnesses by using simulations to enhance the changes in behavior. It uses different trackers to change the care of patients without needing professional help from medical practitioners. 

5. Disease Diagnosis and Treatment

AI has been instrumental in the industry, improving the diagnostic and treatment decisions, while reducing medical errors. Integrating AI will give secure access to patient records and data, and it can help detect or define the risks of someone getting a disease.

This will reduce the workload of healthcare providers and help them focus on prevention and treatment for the patients.

6. Drug Discovery

The process, from researching new drugs to getting patients as a viable means of treatment, is long and expensive.

Research of drugs and discovery is another AI trend changing the medical industry rapidly when finding new drugs that can help patients combat infections. The health industry uses the latest in AI development to improve the drug repurposing and discovery process in a way that drastically shortens the time it takes for new drugs to get to the market and also reduces the cost of getting the drugs.

7. Value-Based Care

AI can help drive efficiency of value-based care thus improving the quality of patient care and enhancing patient outcomes.  It helps in organizing and analyzing healthcare data, enhancing diagnostic procedures and predictions, improving informed clinical decision-making and so on.

Medical companies can have significant cost savings utilizing AI.  AI systems process multiple medical records at once, reducing the need for extra workforce and the money they cost. It also helps healthcare professionals make better, well-informed decisions more accurately than before, making it possible for them to give more accurate treatment with reduced risk and the best patient results.

Conclusion

AI has moved the healthcare industry in multiple ways, enhancing the treatment of patients and workflow of professionals.

It is a given that AI will play an even more important role in the medical field as we continue moving toward the future. Healthcare practitioners will continue to integrate modern technologies to make things work better than they already do.

Indium has been helping several healthcare organizations, provide patient-centric business models through seamless digital engineering of advanced technologies that drives superior customer experience and operational efficiencies.

To know more about the services we offer, please click here.

The post Top AI Trends Transforming Healthcare Industry appeared first on Indium.

How are healthcare organizations leveraging predictive analytics to derive actionable insights from their ever-growing datasets? We find out here.

Staying ahead of Patient Health Deterioration

It is the most essential application of predictive analytics in healthcare.

It helps caregivers react quickly to any change in a patient’s vitals and gather foresight into possible deterioration before symptoms are evident.

A 2017 study demonstrates this: at the University of Pennsylvania, a predictive analytics tool using machine learning and EHR data helped identify patients vulnerable to severe sepsis or septic shock a full 12 hours before the onset of the illness.

Predictive insights are particularly valuable in the intensive care unit (ICU), where timely intervention can help save someone’s life and prevent patient health deterioration.

The increased adoption of wearable biosensors offers manifold benefits, too, for care providers. They enable remote health monitoring and help detect early symptoms of health deterioration.

Preventing Patient self-harm

Early identification of individuals likely to self-harm will help provide the essential mental healthcare to avoid potentially serious or fatal events.

According to the World Health Organization, almost 800,000 people die of suicide each year, which is one person every 40 seconds.

Studies have showed that predictive analytics, using electronic health record (EHR) data and depression questionnaire, helps identify individuals at higher risk of committing suicides or other forms of self-harm.

In a study led by Kaiser Permanente (a leading American healthcare provider) and conducted together with Mental Health Research Network, EHR data combined with a depression questionnaire helped accurately detect those with a higher risk of attempting suicide.

Another study, featured on the American Journal of Psychiatry, aimed to build and validate predictive models with the help of electronic health records to predict suicide attempts and suicide deaths after an outpatient visit.

Based on predictors such as prior suicide attempts, mental health substance diagnoses, mental health and more, it was found that within 90 days of a mental health visit, suicide attempts and suicide deaths among individuals in the upper one percent of predicted risk were 200 times more common than those in the bottom half of the predicted risk scale.

Predicting patterns in patient utilization

Predictive analytics helps healthcare organizations ensure adequate staffing levels for busier clinic hours, minimize wait times and improve patient satisfaction.

With the help of big data visualization tools and analytics strategies to model patient flow patterns, healthcare centers can ensure the inpatient department has adequate beds available for patient admission, that the outpatient and physician offices have enough resources to reduce patient wait times and manage workflow and scheduling adjustments accordingly.

Scheduling changes help nurses and doctors cope with the increased patient flow while reducing the burden on them, thus ensuring they provide timely care and improve patient satisfaction.

Data Security

Predictive analytics and artificial intelligence (AI) play a key role in boosting cybersecurity, with the sophistication of cyberattacks (involving malware, phishing and more) rapidly on the rise.

Confidential patient information worth big money, a vast network of connected medical devices, outdated technology, among other factors, make the healthcare industry a constant target of cyberattacks.

Predictive analytics tools and machine learning help calculate real-time risk scores for different transactions and requests, making the system respond differently based on how the event is scored.

David McNeely from the Institute for Critical Infrastructure Technology says: “Once the risk score has been determined in real-time, the system can use this during a login event to either grant the access for a low-risk event or to challenge for Multi Factor Authentication [MFA] or possibly block the access for high-risk events.”

Create risk scores for chronic diseases

Early identification of individuals with a higher risk of developing chronic illnesses is essential for two reasons. It gives care providers and patients the best chance of preventing long-term health issues. It also helps mitigate the potential cost and complexities of the treatment.

By creating a risk score—from examining patients with identical characteristics, gathering lifestyle and clinical data and using algorithms to understand how various factors effect patient outcomes—healthcare providers gain insight into the type of therapy and wellness activities which can benefit their patients.  

Summary

As far as health management is concerned, prediction is the foundation for prevention and treatment. Predictive analytics helps healthcare providers in different ways. In addition to those mentioned above, the technology helps identify individuals likely to miss a clinical appointment and send timely reminders, manage supply chain to enhance efficiency and cut down on unnecessary costs, develop effective therapies and new medication, improve patient engagement and more.

Given its manifold benefits, it’s no wonder that, according to a 2017 study by the society of actuaries, 89 percent of healthcare providers were then either already using predictive analytics in their organizations or planned to in the next five years.

The post Top 5 use cases of Predictive Analytics in Healthcare appeared first on Indium.