The Healthcare sector is booming at a faster rate and the necessity to manage patient care and innovate medicines has increased synonymously. With the rise in such needs, newer technologies are being adopted in the industry. One such major change that might take place in the future is the use of Big Data and Analytics in the Healthcare sector.

According to an International Data Corporation (IDC) report sponsored by Seagate Technology, it is found that big data is projected to grow faster in healthcare than in sectors like manufacturing, financial services or media. It is estimated that the healthcare data will experience a compound annual growth rate (CAGR) of 36 percent through 2025.

Market research have shown that the global big data in the healthcare market is expected to reach $34.27 billion by 2022 at a CAGR of 22.07%. Globally, the big data analytics segment are expected to be worth more than $68.03 billion by 2024, driven largely by continued North American investments in electronic health records, practice management tools, and workforce management solutions.

Here are 5 ways in which Big Data can help and change the entire scenario of the Healthcare sector.

1. Health Tracking

Big Data and Analytics along with the Internet of Things (IoT), is revolutionizing the way one can track various user statistics and vitals. Apart from the basic wearables that can detect the patient’s sleep, heart rate, exercise, distance walked, etc. there are new medical innovations that can monitor the patient’s blood pressure, pulse Oximeters, glucose monitors, and more. The continuous monitoring of the body vitals along with the sensor data collection will allow healthcare organizations to keep people out of the hospital since they can identify potential health issue and provide care before the situation goes worse.

2. Reducing Cost

Big Data can be a great way to save costs for hospitals that either over or under book staff members. Predictive analysis can help resolve this issue by predicting the admission rates and help with staff allocation. This will reduce the Rate of Investment incurred by hospitals and in fact help utilize their investment to the max. The insurance industry can save money by backing wearables and health trackers to ensure that patients do not spend time in the hospital. It can save wait times for patients since the hospital will have adequate staff and beds available as per the analysis all the time. Predictive analytics also helps cut costs by reducing the rate of hospital readmissions.

According to a recent report by the Society of Actuaries, 47% of healthcare organizations are already using predictive analytics. It is also noted that over 57% of healthcare sectors believe that predictive analytics will save organizations 25 percent or more in annual costs over the next five years.

Healthcare & Big Data Facts: McKinsey & Company report states that after 20 years of steady increases, healthcare expenses now represent 17.6% of GDP, ie. nearly $600 billion more than the expected benchmark for the U.S. size and wealth.

3. Assisting High-Risk Patients

If all the hospital records are digitized, it will be the perfect data that can be accessed to understand the pattern of many patients. It can identify the patients approaching the hospital repeatedly and identify their chronic issues. Such understanding will help in giving such patients better care and provide an insight into corrective measures to reduce their frequent visits. It is a great way to keep a list and check on high-risk patients and offer them customized care.

4. Preventing Human Errors

A lot many times it has been noted that the professionals tend to either prescribe a wrong medicine or dispatch a different medication by mistake. Such errors, in general, can be reduced since Big Data can be leveraged to analyze user data and the prescribed medication. It can corroborate the data and flag potential out of place prescription to reduce mistakes and save lives. Such software can be a great tool for physicians who cater to many patients in a day.

Healthcare & Big Data Facts: The Centers for Medicare and Medicaid Services prevented more than $210.7 million in healthcare fraud in one year using predictive analytics.

5. Advancement in Healthcare Sector

Apart from the current scenario, Big Data can be a great benefit for advancement in science and technology. For Healthcare, Artificial Intelligence, such as IBM’s Watson can be used to surf through numerous data within seconds to find solutions for various diseases. Such advancement is already in progress and will continue to grow with the amount of research collected by Big Data. It will not only be able to provide accurate solutions, but also offer customized solutions for unique problems. The availability of predictive analysis will assist patients traveling to a particular geographical location by studying similar patients in that area.

Healthcare & Big Data Facts: Effective use of big data could add $300 million per year to the healthcare industry.

Thus, to sum up, Big Data increases the ability of the healthcare sectors to:

• Predict Epidemics
• Cure Disease
• Improve Quality of Life
• Increase Preventable Care
• Begin Early Preventive Care
• Spot Warning Signs Sooner

Numerous studies and researches prove that technology has tremendously transformed the healthcare sectors. Professor and researcher Ronda Hughes too explains in her research how big data is improving health services.

Improving health outcomes with big data | Source : TEDxUofSC

Although most part of Big Data generated is not fully utilized currently due to limitations of the toolset and funds, it is definitely the future. Invest in the future and use Big Data Analytics to be a part of an evolving Healthcare Industry by seeking an experienced company such as ours to assist you.

Related Reading: Find out how big companies are using the power of Big Data to enhance customer experience. 

IoT and data remain intrinsically linked together. Data consumed and produced keeps growing at an ever expanding rate. This influx of data is fueling widespread IoT adoption as there will be nearly 30.73 billion IoT connected devices by 2020. The Internet of Things (IoT) is an interconnection of several devices, networks, technologies, and human resources to achieve a common goal. There are a variety of IoT-based applications being used in different sectors and have succeeded in providing huge benefits to the users.

The data generated from IoT devices turns out to be of value only if it gets subjected to analysis, which brings data analytics into the picture. Data Analytics (DA) is defined as a process, which is used to examine big and small data sets with varying data properties to extract meaningful conclusions and actionable insights. These conclusions are usually in the form of trends, patterns, and statistics that aid business organizations in proactively engaging with data to implement effective decision-making processes.

Merging Data Analytics and IoT will Positively Impact Businesses

Data Analytics has a significant role to play in the growth and success of IoT applications and investments. Analytics tools will allow the business units to make effective use of their datasets as explained in the points listed below.

• Volume: There are huge clusters of data sets that IoT applications make use of. The business organizations need to manage these large volumes of data and need to analyze the same for extracting relevant patterns. These datasets along with real-time data can be analyzed easily and efficiently with data analytics software.
• Structure: IoT applications involve data sets that may have a varied structure as unstructured, semi-structured and structured data sets. There may also be a significant difference in the data formats and types. Data analytics will allow the business executive to analyze all of these varying sets of data using automated tools and software.
• Driving Revenue: The use of data analytics in IoT investments will allow the business units to gain an insight into customer preferences and choices. This would lead to the development of services and offers as per the customer demands and expectations. This, in turn, will improve the revenues and profits earned by the organizations.

• Competitive Edge: IoT is a buzzword in the current era of technology and there are numerous IoT application developers and providers present in the market. The use of data analytics in IoT investments will provide a business unit to offer better services and will, therefore, provide the ability to gain a competitive edge in the market.

There are different types of data analytics that can be used and applied in the IoT investments to gain advantages. Some of these types have been listed and described below.

• Streaming Analytics: This form of data analytics is also referred as event stream processing and it analyzes huge in-motion data sets. Real-time data streams are analyzed in this process to detect urgent situations and immediate actions. IoT applications based on financial transactions, air fleet tracking, traffic analysis etc. can benefit from this method.
• Spatial Analytics: This is the data analytics method that is used to analyze geographic patterns to determine the spatial relationship between the physical objects. Location-based IoT applications, such as smart parking applications can benefit from this form of data analytics.
• Time Series Analytics: As the name suggests, this form of data analytics is based upon the time-based data which is analyzed to reveal associated trends and patterns. IoT applications, such as weather forecasting applications and health monitoring systems can benefit from this form of data analytics method.
• Prescriptive Analysis: This form of data analytics is the combination of descriptive and predictive analysis. It is applied to understand the best steps of action that can be taken in a particular situation. Commercial IoT applications can make use of this form of data analytics to gain better conclusions.

There have been scenarios wherein IoT investments have immensely benefitted from the application and the use of data analytics. With the change and advancement in technology, there are emerging areas in which data analytics can be applied in association with IoT. For instance, actionable marketing can be carried out by applying data analytics to the product usage. IoT analytics will also allow the increased safety and surveillance abilities through video sensors and application of data analytics methods.

Healthcare is one of the prime sectors of every country and the utilization of data analytics in IoT based healthcare applications can provide breakthroughs in this area. The reduction of the healthcare costs, enhancement of telehealth monitoring, and remote health services, increased diagnosis and treatment can be achieved using the same.

The utilization of data analytics shall, therefore, be promoted in the area of IoT to gain improved revenues, competitive gain, and customer engagement. By collaborating with the right strategy partner, businesses can couple data analytics with IoT to leverage data for gaining a competitive advantage.

Monetize IoT Data with Analytics [ Source : TEDx Talks]

If you are planning to select Business Intelligence (BI) tool for your Big Data solutions, it is important to evaluate which one is the best suited and not best rated for your company. Selecting a right visualization tool that can help you get the most out of Big Data and has well-defined functions, is an important criterion of the process. So you should ask the following questions before selecting the best tool for your company.

1. What are you visualizing?

It is important to first understand why you are looking for the tool in the first place. If you are planning to visualize the internal data such as marketing, finance, etc. you should look for a tool that is in alignment with your management system. For example, if you are using SAP ECC/Net Weaver system for handling internal data, an SAP-based BI will work better for easy implementation and cost reduction on training. Similarly, if you are going to use the tool for a client, it is better to use something that is compatible with what your client is using.

2. How is the tool’s interface?

It is imperative that the tool has an easy to use Graphical User Interface (GUI). Tools are meant to save time and make the task easy.  A well-designed tool that offers access to various options can be put in the pipeline with ease. Check if it has nice graphics capabilities in case you need to visualize decision trees and so on.

3. Does it have the essential support for visual discovery?

Tools should provide the most basic support for visual discovery and query processing. This might include something as simple as comma-separated values file, text, Excel, and XML support. Apart from these basic things, you might need to check what programming language it supports. Your decision will rest on what your internal team is expert at handling. Your team can get to support for various well-known programming languages such as C++, Python, Java, and Perl.

The other thing to check is whether or not the visualization tool you are planning to use is compatible with the operating system you use. In case of cloud implementation, ask the cloud provider for an OS that is compatible with your visualization tool. If you are catering to a client, ensure that the OS you select is compatible with their systems too.

4. Is the price right?

It is no surprise that price plays an important role in finalizing a lot of things in any company. BI projects cost a lot and the cost will largely depend on a number of criteria such as the level of in-house expertise and the ultimate goal to be achieved. Visualization tools should not be judged on the basis of their price alone but compared with how big is the need and what is being provided.

A good way to make a decision is to try a free trial version of the software to check whether it works for you or not. The tool provider should offer good technical support along with the documentation that covers all aspects of the tool.

5. How flexible is the tool?

Big Data is evolving at a phenomenal rate and so is the technology around it. Make sure that the visualization tool that you are seeking is flexible enough to adapt to these changes. Ask the provider how easy it is to upgrade the tool so that you do not hit a roadblock and require a complete overhaul in the near future.

Understanding these points will help you start zeroing on a list of visualization tools but seeking the support of an experienced tool provider will help you finalize it. Look for someone like us who have an expertise in understanding the requirements of the client and providing a complete solution.  

Cognitive computing in a broad sense refers to software mimicking the functioning of the human brain, to make better decisions.

Computers have caught on ever since its inception, owing to its ability to undertake lightning-fast calculations, much beyond the range of human capabilities. However, computing devices face a serious limitation in not being able to accomplish tasks humans take for granted, such as understanding the natural language or recognizing unique objects in an image. While artificial intelligence offers a start in this direction, cognitive computing represents the coming of age in this front.

Cognitive computing, in a sense, represents the third era of computing, with computers that could tabulate sums the in-thing in the 1900s, to programmable systems in the 1950s, and now cognitive systems.

Personal digital assistants such as Siri, already present in smartphones come close to cognitive computing but are not true cognitive systems. Such systems can only respond to a preset number of requests, whereas true cognitive applications give a thoughtful response, without being restrained to a preprogrammed response set.

How Do Cognitive Applications Work?

Cognitive computing aims to simulate human thought processes in a computerized model. To this end, cognitive applications use deep learning algorithms and neural networks and leverage the latest technological solutions such as data mining, natural language processing, and pattern recognition.

Cognitive applications draw on multiple sources of information, including structured and unstructured digital information, sensory inputs such as visual, gestural, auditory, information, sensor-provided information, and more. It then processes the gathered information by comparing it to the set of data it already knows. As such, the more data the system encounters, the more it learns, and the more accurate the system becomes, over time.

Cognitive computing applications integrate data analysis with adaptive page displays (AUI) to tailor content for the specific audience and specific situations.

IBM Watson, one of the earliest approaches to cognitive computing, offered a path-breaking combination of natural language processing, machine learning, and knowledge representation. Watson ingests questions or inputs in natural language mode, search its repository for information, develops and analyze hypotheses on its own, and generates answers, also in natural language mode. What made Watson successful was not just the combination of the multiple capabilities, but the seamless and powerful integration of such different capabilities in a way it influences each other.

Basic Characteristics of Cognitive Applications

Cognitive applications are a cut above ordinary applications, as evident from the following basic features or characteristics.

Adaptive: Cognitive applications are adaptive, capable of integrating information around its ecosystem, as it changes. These systems feed on dynamic data in real time, or near real-time, to master ambiguity and unpredictability. It adapts to the changing goals and requirements of the enterprise, which is common in today’s highly fluid business environment.

Interactive: Cognitive applications interact easily with users, and also with other processors, devices, and cloud services. Such seamless interactions allow users to make explicit their requirements comfortably, and the network ascertains the requirements automatically to some extent.

Iterative: Cognitive applications are iterative and stateful. These apps ask questions or find additional sources by itself when a problem statement is ambiguous or incomplete. It also remembers previous interactions, and pulls in suitable information relevant to the current context, from such corpus.

Contextual: Cognitive computing applications understand and identify contextual elements such as location, time, meaning, syntax, processes, regulations, user’s profile, and more, connected to its ecosystem. The apps act on the basis of such information, automatically.

Dark Data Compatibility: Cognitive computing systems have the capability to deal with “dark data.” Traditional business intelligence and analytics solutions are mostly unable to comprehend social media postings, electronic medical record notes, electronic fitness device readings, unstructured images, and the bulk of general data generated by users in normal day to day settings today. Cognitive Computing apps process such multi-structured and unstructured dark data, to pull out non-obvious insights and subject it to analytics. Combining such dark data with the readily available structured information such as customer records unearth patterns, relationships, and other contextual associations not discernable otherwise.

Cognitive Applications in Action: Use cases

While cognitive computing has been around for quite some time, it is only recently, with the advancements in technology giving it a boost, that it has come to the mainstream. Several practical use cases have already emerged.

Many businesses now use cognitive computing applications to connect with their customers and other stakeholders at a more personal level and offer highly relevant recommendations. Such apps modify the recommendations automatically as they understand more about the stakeholder, and as the situation unfolds. Furthermore, such apps pick up subtleties that traditional analytics would miss.

IBM Watson, one of the earliest manifestations of a cognitive computing platform, already finds widespread use in healthcare. The cognitive computing platform collates the entire gamut of knowledge around a medical condition, such as patient history, journal articles, best practices, diagnostic tools, and more. It then analyzes the information, and offer a recommendation in sync with the changing condition of the patient. It is virtually impossible for any human to possess such vast range of information, leave alone analyze it. Doctors may leverage such insights to adopt evidence-based treatment options considering all factors, including the individual patient’s presentation and history. This is a big upgrade from the present scenario where the doctor makes educated guesswork, based on grossly incomplete information, with the decision based on the doctor’s limited range of knowledge. The insights available through cognitive computing enable even fresher doctors to perform as effectively as experienced specialists.

Cognitive computing applications are also making its mark in a big way to improve consumer behavior analysis, facilitate personal shopping bots, in education, diagnostics, and other areas. A good real-life example is Hilton Hotel’s Connie, the first concierge robot, which helps visitors with regards to hotel information, local attractions, and more, with questions posed in natural language rather than computing language.

Cognitive computing delivers positive ROI. Enterprises have already succeeded in applying it to convert even traditional cost centers such as customer care to profit centers. For instance, a packaged goods company applying cognitive computing to resolve customer problems automatically, pre-empting the usual practice of customers raising a ticket, could achieve a 30% reduction in tickets. Considering the cost of each ticket was $24 to $160, the savings are substantial.

Enterprises adopting cognitive computing, however, need to develop purpose-built applications to address specific use cases relevant to their stakeholders. Success depends on not just technical competence, but the extent to which the cognitive computing technology is interwoven with the business or customer needs.