Evolving Telemedicine Healthcare with ZIF™

Ashish Joseph

Overview

Telemedicine is a powerful tool that was introduced in the 1950s to make healthcare more accessible and cost effective for the general public. It had helped patients especially in rural areas to virtually consult physicians and get prompt treatment for their illnesses.  

Telemedicine empowers healthcare professionals to gain access to patient information and remotely monitor their vitals in real time.

In layman terms, Telemedicine is the virtual manifestation of the remote delivery of healthcare services. Today, we have 3 types of telemedicine services;

  • Virtual Consultation: Allowing patients and doctors to communicate in real time while adhering to HIPAA compliance
  • EHR Handling:  Empowering providers to legally share patient information with healthcare professionals
  • Remote Patient Monitoring: Enabling doctors to monitor patient vitals remotely using mobile medical devices to read and transmit data.

The demand from a technology embracing population has brought in a higher rate of its adoption today.

Telemedicine can be operated in numerous ways. The standard format is by using a video or voice-enabled call with a HIPAA compliant tool based on the country of operation. There are also other ways in which portable telemedicine kits with computers and medical devices are used for patient monitoring enabled with video.

patient experience health management

Need of the Hour

The COVID-19 pandemic has forced healthcare systems and providers to adapt the situation by adopting telemedicine services to protect both the doctors and patients from the virus. This has entirely changed the scenario of how we will look at healthcare and consultation services going forward. This adoption of the modern telemedicine services has proven to bring in more convenience, cost saving and new intelligent features that enhance the doctor and patient experience and engagement significantly.

The continuous advancements and innovation in technology and healthcare practices significantly improve the usability and adoption of telemedicine across the industry. In the next couple of years, the industry is to see a massive integration of telemedicine services across practices in the country.

healthcare it support service offerings

A paper titled, “Telehealth transformation: Covid19 and the rise of Virtual Care” from the journal of the American Medical Informatics Association, analyzes the adoption of telemedicine in different phases during the pandemic.

During the initial phase of the pandemic when the lockdown was enforced, telemedicine found the opportunity to scale as per the situation. It dramatically decreased the proportion of in-person care and clinical visits to reduce the community spread of the virus.

As the causalities from the pandemic intensified, there was a peak in demand of inpatient consultations with the help of TeleICUs. This was perfectly suited to meet the demands of inpatient care while reducing the virus spread, expanding human and technical resources, and protecting the healthcare professionals.

With the pandemic infection rates stabilizing, telemedicine was proactive in engaging with patients and effectively managing the contingencies. As restrictions relaxed with declining infection rates, the systems will see a shift from a crisis mode to a sustainable and secure system that preserve data security and patient privacy.

The Future of Telemedicine

With the pandemic economy serving as an opportunity to scale, telemedicine has evolved to a cost effective and sustainable system. The rapid advances in technology enable telemedicine to evolve faster.

The future of Telemedicine revolves around Augmented reality with the virtual interactions simulated in the same user plane. Both Apple and Facebook are experimenting with their AR technology and are expected to make a launch soon.

Now Telemedicine platforms are evolving like service desks, to measure efficiency and productivity. This helps to track the value realizations contributed to the patients and the organization.

The ZIF™ Empowerment

ZIF™ helps customers scale their telemedicine system to be more effective and efficient. It empowers the organization to manage healthcare professionals and customer operations in a New Age Digital Service Desk platform. ZIF™ is a HIPAA compliant platform and leverages the power of AI led automation to optimize costs, automate workflows and bring in an overall productivity efficiency.

ZIF™ keeps people, processes and technology in sync for operational efficiency. Rather than focusing on traditional SLAs to measure performance, the tool focuses more on end user experience and results with the help of insights to improve each performance parameter.

Here are some of the features that can evolve your existing telemedicine services.

AIOps based Predictive and Prescriptive Analytics Platform

Patient engagements can be assisted with consultation recommendations with their treatment histories. The operations can be streamlined with higher productivity with quicker decision making and resolutions. A unified dashboard helps to track performance metrics and sentiment analytics of the patients.

AI based Voice Assistants and Chatbots

Provide consistent patient experience and reduce the workload of healthcare professionals with responses and task automations.

Social Media Integration

Omnichannel engagement and integration of different channels for healthcare professionals to interact with their patients across social media networks and instant messaging platforms.

Automation

ZIF™ bots can help organizations automate their workflow processes through intuitive activity-based tools. The tool offers over 200+ plug-and-play workflows for consultation requests and incident management.

Virtual Supervisor

The Native machine learning algorithms aid in initial triaging of patient consultation requests to the right healthcare professional with its priority assignment and auto rerouting tickets to the appropriate healthcare professional groups.

ZIF™ empowers healthcare organizations to transform and scale to the changing market scenarios. If you are looking for customized solutions for your telemedicine services with the help of ZIF™, feel free to schedule a Demo with us today.

https://zif.ai/

About the Author –

Ashish Joseph is a Lead Consultant at GAVS working for a healthcare client in the Product Management space. His areas of expertise lie in branding and outbound product management.

He runs two independent series called BizPective & The Inside World, focusing on breaking down contemporary business trends and Growth strategies for independent artists on his website www.ashishjoseph.biz

Outside work, he is very passionate about basketball, music, and food.

Anomaly Detection in AIOps

Vimalraj Subash

Before we get into anomalies, let us understand what is AIOps and what is its role in IT Operations. Artificial Intelligence for IT operations is nothing but monitoring and analyzing larger volumes of data generated by IT Platforms using Artificial Intelligence and Machine Learning. These help enterprises in event correlation and root cause analysis to enable faster resolution. Anomalies or issues are probably inevitable, and this is where we need enough experience and talent to take it to closure.

Let us simplify the significance of anomalies and how they can be identified, flagged, and resolved.

What are anomalies?

Anomalies are instances when performance metrics deviate from normal, expected behavior. There are several ways in which this occur. However, we’ll be focusing on identifying such anomalies using thresholds.

How are they flagged?

With current monitoring systems, anomalies are flagged based on static thresholds. They are constant values that provide the upper limits of a normal behavior. For example, CPU usage is considered anomalous when the value is set to be above 85%. When anomalies are detected, alerts are sent out to the operations team to inspect.

Why is it important?

Monitoring the health of servers are necessary to ensure the efficient allocation of resources. Unexpected spikes or drop in performance such as CPU usage might be the sign of a resource constraint. These problems need to be addressed by the operations team timely, failing to do so may result in applications associated with the servers failing.

So, what are thresholds, how are they significant?

Thresholds are the limits of acceptable performance. Any value that breaches the threshold are indicated in the form of alerts and hence subjected to a cautionary resolution at the earliest. It is to be noted that thresholds are set only at the tool level, hence that way if something is breached, an alert will be generated. These thresholds, if manual, can be adjusted accordingly based on the demand.

There are 2 types of thresholds;

  1. Static monitoring thresholds: These thresholds are fixed values indicating the limits of acceptable performance.
  2. Dynamic monitoring thresholds: These thresholds are dynamic in nature. This is what an intelligent IT monitoring tool does. They learn the normal range for both a high and low threshold, at each point in a day, week, month, and so on. For instance, a dynamic system will know that a high CPU utilization is normal during backup, and the same is abnormal on utilizations occurring on other days.

Are there no disadvantages in the threshold way of identifying alerts?

This is definitely not the case. Like most things in life, it has its fair share of problems. Routing from philosophy back to our article, there are disadvantages in the Static Threshold way of doing things, although the ones with a dynamic threshold are minimal. We should also understand that with the appropriate domain knowledge, there are many ways to overcome these.

Consider this scenario. Imagine a CPU threshold set at 85%. We know anything that breaches this, is anomalies generated in the form of alerts. Now consider the same threshold percentage as normal behavior in a Virtual Machine (VM). This time, the monitoring tool will generate alerts continuously until it reaches a value below the threshold. If this is left unattended, it will be a mess as there might be a lot of false alerts which in turn may cause the team to fail to identify the actual issue. It will be a chain of false positives that occur. This can disrupt the entire IT platform and cause an unnecessary workload for the team. Once an IT platform is down, it leads to downtime and loss for our clients.

As mentioned, there are ways to overcome this with domain knowledge. Every organization have their own trade secrets to prevent it from happening. With the right knowledge, this behaviour can be modified and swiftly resolved.

What do we do now? Should anomalies be resolved?

Of course, anomalies should be resolved at the earliest to prevent the platform from being jeopardized. There are a lot of methods and machine learning techniques to get over this. Before we get into it, we know that there are two major machine learning techniques – Supervised Learning and Unsupervised Learning. There are many articles on the internet one can go through to have an idea of these techniques. Likewise, there are a variety of factors that could be categorized into these. However, in this article, we’ll discuss an unsupervised learning technique – Isolation Forest amongst others.

Isolation Forest

The algorithm isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.

The way that the algorithm constructs the separation is by first creating isolation trees, or random decision trees. Then, the score is calculated as the path length to isolate the observation. The following example shows how easy it is to separate an anomaly-based observation:

Best AI Auto Discovery Tools

 

In the above image, the blue points denote the anomalous points whereas the brown ones denote the normal points. Anomaly detection allows you to detect abnormal patterns and take appropriate actions. One can use anomaly-detection tools to monitor any data source and identify unusual behaviors quickly. It is a good practice to research methods to determine the best organizational fit. One way of doing this is to ideally check with the clients, understand their requirements, tune algorithms, and hit the sweet spot in developing an everlasting relationship between organizations and clients.

Zero Incident FrameworkTM, as the name suggests, focuses on trending organization towards zero incidents. With knowledge we’ve accumulated over the years, Anomaly Detection is made as robust as possible resulting in exponential outcomes.

References

About the Author –

Vimalraj is a seasoned Data Scientist working with vast data sets to break down information, gather relevant points, and solve advanced business problems. He has over 8 years of experience in the Analytics domain, and currently a lead consultant at GAVS.

Healthcare Transformation Using Cognitive Services

Srinivasan Sundararajan

Democratization of AI in Healthcare

Organizations are becoming increasingly digital and Artificial Intelligence is being deployed in many of them. Often small tech-savvy start-ups and large firms with huge funds, like those in technology and finance businesses, are deploying sophisticated forms of AI.

But several other companies are being left behind. They may not know how or where to deploy AI, or they may not have the resources to create their own AI. Cloud technologies are filling this gap. With options from Google, AWS, Microsoft, and plenty of other vendors, companies can begin exploring how AI can help them. The more that AI becomes accessible, the more companies – and users – can leverage it for their benefit. 

Healthcare is often cited as an area that AI can help immensely. The democratization of AI in healthcare, which is being driven by cloud technologies, is leading to greater access and more predictive work in patient monitoring and smarter reactive responses to health issues. 

ML and AI have traditionally been perceived as the domain of experts and specialists with PhDs. While democratization of AI is viewed differently by different organizations, a common theme has been to make AI adoption simpler.

The following are a few democratized AI services available as part of cloud providers (most of the examples are from Microsoft Eco System as a reference, however other providers also have similar services).

Handwriting Recognition

ai-led operations consulting firm in healthcare

With Windows Ink, you can provide your doctors with the digital equivalent of almost any pen-and-paper experience imaginable, from quick, handwritten notes and annotations to whiteboard demos.

The Windows Ink platform, together with a pen device, helps create digital handwritten notes, drawings, and annotations. The platform supports capturing digitizer input as ink data, generating ink data, managing ink data, rendering ink data as ink strokes on the output device, and converting ink to text through handwriting recognition.

There are equivalent options in other platforms like iOS and Android which can be used for making similar applications for doctors.

Optical Character Recognition

ai-led operations management services in healthcare

Azure’s latest OCR technology Computer Vision Read API extracts printed text (in several languages), handwritten text (English only), digits, and currency symbols from images and multi-page PDF documents. It can extract text from text-heavy images and multi-page PDF documents with mixed languages and detect both printed and handwritten text in the same image or document.

Most hospitals have to deal with lot of documents, especially when it involves external parties like insurance companies. Healthcare organizations can increase productivity and cut down on costs by investing in OCR for managing medical documents.

Emotion APIs

The Azure Face service provides AI algorithms that can detect, recognize, and analyze human faces in images. Facial recognition software has varied applications like in security, natural user interface, image content analysis and management, mobile apps, and others.

Using this API, we can detect perceived facial expressions such as anger, contempt, disgust, fear, happiness, neutral, sadness, and more. It is important to note that facial expressions alone do not represent the internal states of people.

Speech Translation

ai-led product engineering services in healthcare

The number of people in the U.S. who speak a language other than English is large and growing. Language barriers have been found to impede access to care, compromise quality, and increase the risk of adverse outcomes. When friends and family interpret, they are prone to omit, add, and substitute information.

The Azure Speech Translation API can translate incoming speech into more than 60 languages. This API enables real-time, multi-language speech-to-speech and speech-to-text translation of audio streams. With the Speech SDK, your applications, tools, and devices have access to source transcriptions and translation outputs for provided audio. Interim transcription and translation results are returned as speech is detected, and results can be converted into synthesized speech.

Health BOTs

AI for Health Cloud Enablement Services

  • Providers have built health bot instances that triage patient issues with a symptom checker, help patients find appropriate care, and look up nearby doctors.
  • Insurers have built health bot instances that give their customers an easy way to look up the status of a claim and ask questions about benefits and services.

Azure Health Bot empowers developers in healthcare organizations to build and deploy AI-powered, compliant, conversational healthcare experiences at scale. Combining a built-in medical database with natural language capabilities to understand clinical terminology, it can be easily customized for various clinical use cases. The service ensures alignment with industry compliance requirements and is privacy protected to HIPAA standards.

Conversational intelligence also adapts dynamically as the health bot instance learns from previous interactions.

Text Analytics for Health

The healthcare industry is overwhelmed with data. They face an incredible challenge in trying to identify and draw insights from all that information. Unlocking insights from this data has massive potential for improving healthcare services and patient outcomes.

The Key Phrase Extraction API evaluates unstructured text, and for each JSON document, returns a list of key phrases.

AI/ML Led Solutions for Life Sciences

The Text Analytics API lets you take unstructured text and returns a list of disambiguated entities, with links to more information on the web. The API supports both Named Entity Recognition (NER) for several entity categories and entity linking.

Text Analytics for health performs NER, relation extraction, entity negation, and entity linking on English-language text to uncover insights in unstructured clinical and biomedical text.

Reinforcement Learning

AI Tools for Digital Transformation in Healthcare Industry

Medical diagnoses essentially involve mapping patients’ medical history, current symptoms, and other information to the correct disease profile. It can be an incredibly complex task representing an enormous burden (in both time and cognitive energy required) for busy clinicians.

Personalizer API uses reinforcement learning to select the single best action, known as reward action ID. Azure Personalizer is a cloud-based service that helps your applications choose the best content item to show your users.

Personalizer currently uses Vowpal Wabbit as the foundation for the machine learning. This framework allows for maximum throughput and lowest latency when making personalization ranks and training the model with all events.

Anomaly Detection

digital transformation in healthcare it consulting

Anomaly detection in medical treatment can be used to discover deviations from regular patterns and determine whether the patient management is unusual. Detecting an anomaly from medical images including mammograms, CT, or PET images is still an ongoing research problem drawing a lot of attention with applications in medical diagnosis.

The Anomaly Detector API enables you to monitor and detect abnormalities in your time series data without having to know machine learning. Using your time-series data, the API determines boundaries for anomaly detection, expected values, and which data points are anomalies.

Detect anomalies in your streaming data by using previously seen data points to determine if your latest one is an anomaly. This operation generates a model using the data points you send and determines if the target point is an anomaly.

Conclusion

Healthcare transformation requires a great amount of AI integration and implementation.

However, most healthcare organizations don’t have enough resources and bandwidth to work on AI development and deployment. Also, AI Involvement by its very nature is iterative and more concentration is required on involving the stakeholders and arriving at a consensus. Remember, the success of AI depends on the richness of data which is the main responsibility of healthcare organizations, while implementation of AI can be taken care of by these cognitive services.

About the Author –

Srini is the Technology Advisor for GAVS. He is currently focused on Healthcare Data Management Solutions for the post-pandemic Healthcare era, using the combination of Multi-Modal databases, Blockchain, and Data Mining. The solutions aim at Patient data sharing within Hospitals as well as across Hospitals (Healthcare Interoprability), while bringing more trust and transparency into the healthcare process using patient consent management, credentialing and zero-knowledge proofs.

Why is AIOps an Industrial Benchmark for Organizations to Scale in this Economy?

Ashish Joseph

Business Environment Overview

In this pandemic economy, the topmost priorities for most companies are to make sure the operations costs and business processes are optimized and streamlined. Organizations must be more proactive than ever and identify gaps that need to be acted upon at the earliest.

The industry has been striving towards efficiency and effectivity in its operations day in and day out. As a reliability check to ensure operational standards, many organizations consider the following levers:

  1. High Application Availability & Reliability
  2. Optimized Performance Tuning & Monitoring
  3. Operational gains & Cost Optimization
  4. Generation of Actionable Insights for Efficiency
  5. Workforce Productivity Improvement

Organizations that have prioritized the above levers in their daily operations require dedicated teams to analyze different silos and implement solutions that provide the result. Running projects of this complexity affects the scalability and monitoring of these systems. This is where AIOps platforms come in to provide customized solutions for the growing needs of all organizations, regardless of the size.

Deep Dive into AIOps

Artificial Intelligence for IT Operations (AIOps) is a platform that provides multilayers of functionalities that leverage machine learning and analytics.  Gartner defines AIOps as a combination of big data and machine learning functionalities that empower IT functions, enabling scalability and robustness of its entire ecosystem.

These systems transform the existing landscape to analyze and correlate historical and real-time data to provide actionable intelligence in an automated fashion.

Data Center Migration Planning Tools

AIOps platforms are designed to handle large volumes of data. The tools offer various data collection methods, integration of multiple data sources, and generate visual analytical intelligence. These tools are centralized and flexible across directly and indirectly coupled IT operations for data insights.

The platform aims to bring an organization’s infrastructure monitoring, application performance monitoring, and IT systems management process under a single roof to enable big data analytics that give correlation and causality insights across all domains. These functionalities open different avenues for system engineers to proactively determine how to optimize application performance, quickly find the potential root causes, and design preventive steps to avoid issues from ever happening.

AIOps has transformed the culture of IT war rooms from reactive to proactive firefighting.

Industrial Inclination to Transformation

The pandemic economy has challenged the traditional way companies choose their transformational strategies. Machine learning-powered automations for creating an autonomous IT environment is no longer a luxury. The usage of mathematical and logical algorithms to derive solutions and forecasts for issues have a direct correlation with the overall customer experience. In this pandemic economy, customer attrition has a serious impact on the annual recurring revenue. Hence, organizations must reposition their strategies to be more customer-centric in everything they do. Thus, providing customers with the best-in-class service coupled with continuous availability and enhanced reliability has become an industry standard.

As reliability and scalability are crucial factors for any company’s growth, cloud technologies have seen a growing demand. This shift of demand for cloud premises for core businesses has made AIOps platforms more accessible and easier to integrate. With the handshake between analytics and automation, AIOps has become a transformative technology investment that any organization can make.

As organizations scale in size, so does the workforce and the complexity of the processes. The increase in size often burdens organizations with time-pressed teams having high pressure on delivery and reactive housekeeping strategies. An organization must be ready to meet the present and future demands with systems and processes that scale seamlessly. This why AIOps platforms serve as a multilayered functional solution that integrates the existing systems to manage and automate tasks with efficiency and effectivity. When scaling results in process complexity, AIOps platforms convert the complexity to effort savings and productivity enhancements.

Across the industry, many organizations have implemented AIOps platforms as transformative solutions to help them embrace their present and future demand. Various studies have been conducted by different research groups that have quantified the effort savings and productivity improvements.

The AIOps Organizational Vision

As the digital transformation race has been in full throttle during the pandemic, AIOps platforms have also evolved. The industry did venture upon traditional event correlation and operations analytical tools that helped organizations reduce incidents and the overall MTTR. AIOps has been relatively new in the market as Gartner had coined the phrase in 2016.  Today, AIOps has attracted a lot of attention from multiple industries to analyze its feasibility of implementation and the return of investment from the overall transformation. Google trends show a significant increase in user search results for AIOps during the last couple of years.

Data Center Consolidation Initiative Services

While taking a well-informed decision to include AIOps into the organization’s vision of growth, we must analyze the following:

  1. Understanding the feasibility and concerns for its future adoption
  2. Classification of business processes and use cases for AIOps intervention
  3. Quantification of operational gains from incident management using the functional AIOps tools

AIOps is truly visioned to provide tools that transform system engineers to reliability engineers to bring a system that trends towards zero incidents.

Because above all, Zero is the New Normal.

About the Author –

Ashish Joseph is a Lead Consultant at GAVS working for a healthcare client in the Product Management space. His areas of expertise lie in branding and outbound product management. He runs a series called #BizPective on LinkedIn and Instagram focusing on contemporary business trends from a different perspective. Outside work, he is very passionate about basketball, music, and food.