Data Analytics in Healthcare – Use Cases, Metrics, Techniques, Companies and More

Introduction

Healthcare data analytics is a form of systematic data analysis that enables healthcare workers to discover possibilities for improvement in health system administration, patient involvement, cost, and diagnosis. Its market is expected to grow by 28.3% CAGR by 2024, according to Markets and Market.

The sheer size and number of data sources in the healthcare industry have become quite overwhelming owing to their less interactive nature. The data relating to medical devices, insurance claims, administrative data, EHRs/EMRs are in bulk to provide 3600 overview of the patients, their medical history, and related determinants.

So, how to extract these data and further use them to derive relevant information? Do you think the use of AI-based technology can be of great help here?

Yes definitely, Data Analytics being based on AI and ML concepts is one such effective technique to model, mine, and evaluate the real-time data to make accurate predictions about the future. As a result, medical institutes are able to deliver high-quality care and efficient operations.

What is Data Analytics in Healthcare?

The robust concept of data analytics answers the question, “What are the possible future occurrences in the healthcare industry? “ An application of modeling and forecasting techniques helps to prepare the physicians, medical specialty, researchers, other stakeholders for the arising threats and opportunities. Data analytics is a perfect blend of traditional hierarchical linear models and advanced Artificial Intelligence & Machine Learning algorithms. As a result, the information so obtained is highly precise, relevant, and accurate.

Health data analytics provides statistics and predictions based on the medical history, environment, social risk factors, unique combinations, genetics, population average, and several other attributes. Moreover, it is potent to leverage benefit in three major dimensions of health care – Diagnosis (a quick study and immediate treatment), prognosis (detailed research, risk consideration, and intervening factors), and treatment (providing most effective course of treatment based on thorough learning of the case). Thus, the overall quality, cost efficiency, treatment process, and patient satisfaction improve gradually with the use of advanced data analysis tools.

Types of Analytics Applications in Healthcare

The outbreak of several health ailments, chronic diseases, environmental issues like global warming, and pollution have created the need to establish preventive healthcare measures. The development of these measures is based on the concept of data analytics where suitable descriptive predictions regarding the future outcomes of health events are made. As a result, several healthcare organizations across the world are bringing it to use for enjoying work and cost efficiency in delivering care. The diversified subject matter of healthcare gets its strength from precise and specific healthcare analytics type.

1. Clinical Analytics

The rapid technological growth has led to the emergence of clinical analytics in recent times. It is a field of data analytics where real-time medical data are used to generate useful insights, reduce errors, and ensure cost savings. One of the survey reports concluded that IT leaders of more than 1340 hospitals observe clinical analytics as their highest prioritized system. Also, termed as Clinical Decision Support (CDS), it helps to generate fact-based diagnostic and therapeutic decision-making along with capturing revenue. Moreover, clinical analytics is known for enhancing care coordination, improving patient wellness, minimizing fraud and overheads costs. Thus, it is the right time for healthcare organizations to embrace and incorporate clinical analytics into their system for efficient survival amidst the changing government regulations.

2. Business Analytics in Healthcare Industry

The inclusion of technical analytical skills in the healthcare industry helps to provide improved delivery of care and quality. Furthermore, healthcare operation experiences significant progress with the implementation of business analytics algorithms. Some of the major contributions of business analytics in healthcare organizations are as follows:

• By analyzing patterns in hospitals for emergency-room care the staffing strategies and operations are determined.
• The business analytics tool helps physicians to determine the customized treatment for the patients rather than considering the generic approach.
• With the use of business analytics healthcare community can improve their understanding of chronic diseases and their treatment alternatives.
• The data-driven approach also brings burden under control and leads to cost-saving in administrative operations.

3. Healthcare Financial Analytics

It refers to the healthcare solution which deals with planning, budgeting, service lines, usage analysis, and importantly the cost allocation aspect. Using financial analytics healthcare providers are able to generate reports, ascertain revenue growth, risk management, forecasting, etc. As a result, it facilitated organizations to shift their concern towards value-based care and risk-based reimbursement. The major components of health care financial analytics are –claim analytics, revenue cycle management, risk management analytics, etc. With the use of blockchain, artificial intelligence, and smart contracts in healthcare financial analytics, the claim adjudication, processing, the transactional cost has reduced by 20%. Moreover, the improvement in clinical documentation, medical billing, and insurance eligibility verification, electronic health record (EHR) has increased the demand for healthcare financial analytics.

4. Digital Health Analytics

It is the complete solution for healthcare data collection, transformation, and visualization needs. Digital health analytics consider operational, clinical, financial, and quality indicators of the healthcare institution. Using the essential digital metrics organizations are able to develop a holistic and personalized view of each patient, employee, and other member leading to effective customer relationship management (CRM). Moreover, in the case of health insurance digital analytics play a crucial role in detecting fraud and strengthening the entire ecosystem. Health analytics has revolutionized the customer service experience by establishing effective interaction mechanisms based on proper pattern study. The digital health analytics metrics help to streamline departmental operations of the hospital and other healthcare institutions at a greater pace.

5. Big Data & Data Mining in Healthcare

The application of big data mining in healthcare is immensely huge. Besides, supporting the administrative function, analytics help to support the decision-making process in the organization. With the assistance of big data and robust mining methods, the health care units can identify patients with a high risk of health ailments or diseases. As a result, it helps to map symptoms and solutions before the extremely widespread diseases. Moreover, big data allows early detection of epidemics or pandemics with constant surveillance which leads to effective patient monitoring. Big data mining has also brought huge changes in telecommunication by providing ongoing information even from remote operative areas. Also, the increasing healthcare fraud can be predicted with the use of big data analytics and their timely mining.

6. Healthcare Marketing Analytics

The healthcare industry is working furiously to make the best out of data analytics in its marketing strategies. Thus, healthcare marketing is a type of data analytics domain that deals with creating new communities and patient pooling. The inclusion of marketing analytics in the healthcare system helps to make informed marketing decisions based on synchronized pattern study. Moreover, the analytics help to leverage cost efficiency which leads to a better return on investment accelerates referrals and admissions rates across the system. According to Radford’s study, healthcare marketing analysts should focus on a single call to action to understand the customer journey and increase their engagement with the organization. Healthcare marketing analytics derive success only with the implementation of the right set of predictive and marketing tools.

7. Value Based Care Analytics

As the name suggests, value-based care analytics is largely focused on creating positive outcomes for the patients by delivering cost-efficient care. According to the report of Change Healthcare, the average medical cost savings increased by 5.6% after the used o value-based care analytics. Additionally, it allows reduction of errors, increased accuracy of the quality assessment, and creating room for innovation. Value-driven care analytics is popularly divided into five healthcare models.

• Medicare Quality Incentive Programs
• Accountable Care Organization
• Pay-for-Performance
• Patient-Centered Medical Home
• Bundled Payments

8. Advanced Analytics in Healthcare

Undoubtedly, healthcare industries go through crucial metamorphic changes of shifting from volume-based business to value-added business. As a result, healthcare providers are observed inclining much towards advanced analytics for delivering quality services. One such prominent example of advanced analytics is EMRs (Electronic Medical Records) which ensure high-quality care to the patients. Moreover, the predictive and prescriptive nature of advanced analytics helps to identify fraudulent activities, manage inventory, assess pharmacy needs, and understand patient’s behavioral patterns, leverage effective helpline service, coordinate staffing functions, evaluating the infrastructure requirements and financial performance too. Thus, advanced analytics is the comprehensive solution for boosting healthcare performance.

9. Patient Data Analytics

The entire healthcare system centers on patients, who are responsible for deriving revenue in the ecosystem. Ideally, it is not merely the reason to offer them profound services. The delivery of value-added services is the basic right every patient deserves to receive. As a result, improving patient care scenarios and creating a healthy population becomes possible with patient data analytics. The analytic facilitate tracking the waiting and appointment time, availability of proper rooms and equipment, observing patient feedback and complaints. As a result, it helps to aggregate healthcare data and use it for establishing work strategies. Using the patient data analytics organization can also predict risk factors and ways to combat such risks which are irregular blood pressure, cholesterol and glucose level, or some history of chronic disease, etc.

10. Descriptive Analytics in Healthcare

It is the most widely used and simplest analytics type which does not include any inferential analyses, correlations, and explorations between the variables. Instead, it is focused on influencing the healthcare decisions with its deep learning and machine learning algorithms As a result; it indicates hospital occupancy rates, length of stay, discharge, doctor’s schedule, availability of various equipment, etc. which play a key role in supporting the decision-making process at healthcare units. Furthermore, descriptive analytics is the retrospective approach that considers patterns, trends, habits, and processes to help to understand what is expected today and how it can impact the future of healthcare industries. For many healthcare units across the world descriptive analytics act as the core of effective management, planning, and budgeting.

11. Predictive Analytics in Healthcare using Big Data

Today, much of the medical science revolves around anticipated risk. As a result, every step is inclined towards reducing the risk factor. Thus, predictive analytics becomes the most organic choice of healthcare units as it helps them to make decisions with absolute certainty and prevent waste of time and resources. The patients at high risk can receive proper treatment at their homes with predictive-based virtual hospital settings for delivering acute care. Predictive Insights act as a valuable tool in the ICU where it helps to spot early signs of adverse events relating to a patient’s health. The patients in the ICU are monitored remotely through tele-ICU settings and wearable bio-sensors which are based on predictive analytics. The concept also facilitates early identification of equipment requirements like MRI scanners, oxygen cylinders, etc. As a result, it helps to ensure a seamless treatment and diagnosis process.

12. Predictive Modeling in Healthcare

The concept of predictive modeling considers statistical methods, game theory, and data mining processes to collect relevant data for a healthy medical establishment. The analyses are drawn on the basis of age, social and economic factors, individual autonomy, and patients’ susceptibility to chronic diseases. Thus, predictive models are used to find similar patterns in people’s behavior and responses for facilitating problem-solving and an opportunity-grabbing healthcare ecosystem, It plays a crucial role in calculating health insurance, medical imaging, palliative care, mental health, and ensuring effective pharmacy services. Moreover, the technology is known for providing several benefits which are as follows:

• Improved diagnostics
• High-cost effectiveness
• Enhanced operational efficiency
• Decreased readmission rates
• Personalized medical care

13. Healthcare Claims Analytics

The analytics claim data is focused on detailed health history which serves as the basis of the calculation claim amount. The claim snapshots consider diagnostic data, prescription consumed test results, and other related demographics detail. Besides these, blood pressure levels, weight, diet, respiration, and pulse rates are studied to gain an informative backdrop for identifying risky conditions. In the daily course, healthcare claim analytics is used to identify red flags based on peripheral details like ICD diagnostic codes for the ongoing diseases and disorders. Using the analytics and its metrics organization serving the Medicaid population save billions of dollars. As a result, a rich body of healthcare claim data is potent to transform the ecosystem. Moreover, the analytics can create multiple levels of metadata for every patient.

14. Population Health Analytics

Population health analytics is specialized in determining the optimal strategies for treating health issues within the group setting. It considers quantitative methods and technological advancements to develop suitable insight for the group. Being based on a machine learning algorithm, the analytics cover the actions and tools associated with relevant data compilation. In collaboration with standard analytical tools, population health analytics is used for identifying risk factors like kidney failure, vulnerability, etc. As a result, helps to incorporate proactive measures in the treatment process. Moreover, the use cases of population health analytics are fundamental to provide quality care and treatment.

Use Cases of Data Analytics in Healthcare

The prominent idea of data analytics is implemented in almost all phases of a patient’s journey from testing to treating. Thus, the use cases of health data analytics extend from micro to macro level and provide care to cost benefits in the most crucial industry. Reportedly, the 2019 survey of the Society of Actuaries says, “Of all the institutions using data analytics, 42% have experienced patient satisfaction wherein 39% of them have enjoyed cost benefits.”

So, let’s find out what exactly these use cases of health data analytics are:

Use Case	Description
Population Health Management	Using AI-based data analytics; physicians can fetch patient’s personal histories, existing health conditions, medications, predicted risks, and improvement possibilities. As a result, other similar cases within a population cohort can be traced and treated based on the patterns in EMR data.
Personal Preventive and Predictive Care	The historical use of medical alert services, electronic media records, helps to identify patients with high risk and lack of amenities. As a result, transform a reactive health care system into a proactive one by combining data from multiple sources.
Chronic Disease Management	The AL-based patient monitoring system facilitates healthy decision-making through threshold alerts and risk scoring. It provides a seamless experience to involved parties and helps mitigate outbreaks of chronic diseases by establishing requited infrastructure.
Operations Management	With the AI prediction a seamless workflow, effective schedule, and time management get established. Thus, it becomes easier for the administration to understand the clinical capacity, maximum booking slots, and availability of the specialist.
Equipment Maintenance	Healthcare equipment such as MRI scanners degrade over the span of time and require replacing. At the same time, estimating the replacement period has become quite difficult. Thus, the technical proactiveness of the devices can be ensured with data analytics and avoid workflow disruptions.

Metrics Used in Health Data Analytics

Today, the health care metrics are not merely focused on medicine and patient-driven approaches. It extends to a holistic data spectrum to provide the best care, manage cost and ensure sustainable hospital performance. As a result, the well-defined performance measurement factors are considered to monitor, analyze and optimize the healthcare processes.

Metric Category	Metrics Measured
Rates and Ratios	Bed Occupancy Rate to monitor the availability of hospital beds. Patient Room Turnover Rate to establish a balance between quality and speed. Patient Follow-up rate to ensure proper care of the patients over time. Hospital Readmission Rates provide a clear picture of patients coming back after discharge. Staff-to-patient ratio to deliver seamless experience and care to the patients.
Time Duration	Patient Wait Time shall be less to enhance patient satisfaction and avoid the risk of health deterioration. ER rush time can be well-managed by identifying the rush hours in the emergency room and making the arrangements accordingly.
Cost Involvement	Patient Drug cost per stay helps to improve the medication flow and cost management. Treatment Cost includes the reasonable cost of the equipment, staff, resources used in the process. Costs by payer helps to understand the insurance type of the patient (if any) and determine cost on the same basis.
Other KPIs	Canceled/Missed Appointments tracking helps to schedule appointments for the next patient in a row at the earliest. Patient Safety is of utmost importance and shall never be taken for granted. Average Hospital Stay helps to evaluate the admitted time of the patients at the hospital followed by cost evaluation. Equipment Utilization record keeping helps to maintain sufficient inventory in case of emergency.

Examples of Health Data Analytics

1. Sound Physicians-BPCI Advanced

Sound Physicians (USA), the largest hospitalist with a leader in physician performance and analytics has incorporated BPCI-A (Bundled Payment for Care Improvement- Advanced) program to enhance patients’ satisfaction and reduce cost. Under analytics-based program, data from Sound’s 3500+ clinics is used to analyze the probability of readmission, clinical workflows, and follow-up appointment, facilitate post-discharge telephone outreach, resource management, and other such targeted interventions of the department. Thus, as per the report of Sound, the application of data analytics reduced the number of risk population by 17.5%.

2. University of Montreal Hospital – Radiology

The Montreal University uses AI and ML models in its radiology processes to observe anatomical changes in each patient and identify the essential markets based on X-ray photographs. Also, the implementation of prognostic technology in lung screening and breast cancer diagnosis has created significant reduced the vulnerable patient categories.

3. MBSC – Bariatric Surgery Outcomes

The Blue Cross Blue Shield-funded Michigan Bariatric Surgery Collaborative (MBSC) uses an AI-based patient registry to improve bariatric surgery in Michigan. Such data analytics tools help comprehend preoperative data to feed MBSC predictive outcome calculator and understand the patient’s demographics like weight loss and other risk factors. As a result, MBSC observed a decline in the rate of venous thromboembolism (VTE) by 43% followed by a reduced post-surgical death rate by 67%.

4. Predict Patient’s Health Deterioration in the ICUs

Data analytics is an apt tool to detect early signs of patient-related risk and machinery deterioration. In the critical cases of Cancer and Traumas, it becomes more essential to consider predictive insights and deliver extensively careful treatment. Does it also help to determine after how long a pneumonia patient shall be readmitted to ICU if required?

The USA, the technically advanced nation made the best use of data analytics during the Covid-19 pandemic to continuously monitor and analyze the high probability of people requiring ICU in immediate 60 minutes. Moreover, it allowed the country to proactively analyze the patient’s probability of death and readmission. As a result, of a successful outcome, now the hospitals in several countries are equipped with tele-ICU Settings. The automated early warning scoring has reduced adverse events by 35% and cardiac arrest death risk by 86%.

5. Penn Medicine – Palliative Care

Emerging as the most reputed academic medical centre, Penn Medicine ensures the use of electronic health record (HER) to forecast health risk arising out of life-threatening diseases. Being named as Palliative connects; it is based on 30 factors to predict patient’s health status for the next month post admission in the hospital. As a result, Machine Learning algorithm in data analytics helps to reduce high risk of mortality too.

Main Techniques for Health Data Analytics

The healthcare data analytics system has gained immense acceptance owing to its profound techniques. These techniques provide data analysis of almost all sections of the health care industry and let derive notable outcomes.

Technique	How the Technique is Used?
Diagnostic Analytics:	It is an advanced analytics tool to examine data and content by drilling down, discovering, mining, and establishing correlations. Thus, it is the best-suited algorithm to diagnose patients with some specific illness and injury based on the thorough study of their symptoms. Additionally, it helps to prepare medical institutions for the upcoming seasonal diseases by examining the past case studies.
SVM (Support Vector Machine)	It is a complex Machine Learning algorithm used for classifying objects and data mining. As a result, it provides a self-reported questionnaire to predict the medical condition for heart failure, liver diseases, and diabetes, using demographic and behavioral data.
Decision Tree	With the tree-like graphs a relationship between the present and potential future consequences is established. Hence, it aids in making medical decisions and diagnosing heart patients owing to its evidence-based medicine approach.
Logistic Regression	A combination of conventional and modern statistics logistic regression is useful to draw predictions based on gender, age, BMI, cholesterol, heart rate, blood pressure, etc. Alongside, it leverages the mammography database to predict breast cancer.

Top Health Data Analytics Companies

The involvement of companies in the healthcare analytics market has improved the existing healthcare levels by making these services more accessible, affordable and innovative with AI-based technology.

Here is a list of companies that bring evolving technologies to elevate healthcare and actively provide economic and qualitative healthcare facilities to its employees and clients in the industry as well as contribute to social welfare:

Company	Progress in Healthcare Data Analytics
GIGACURE	The revolution of Gigacure is laudable for dedicating its Artificial Intelligence technology to rescuing the lives of pregnant women and their unborn children who often fall to death because of lack of healthcare facilities to avail at the right time. It aims to subjugate the mortality rates through its financial and administrative data, which are combined to provide healthcare analytics for hospitals and healthcare management. Regarded as one of the best data providers, the newly established company with a limited employee base has juggled through the hardships. It has progressed its technology and has even developed India’s No.1 BIS-certified health watch named Jeevan: G1.
APIXIO CENTENE (MERGER)	The computable platform of Apixio is known as a problem-solver of critical healthcare problems vis its data science. Its automated technology executes textual contexts as well as various other activities of administration, phenotype assembling for analysis, scouring physician notes, and other clinical documents for patient information. To assist key healthcare operations, they automate the execution of quality and risk measurements, payment or reimbursement regulations, and other operational procedures. Along with offering its contribution by handling the billions of healthcare files generated every year in the USA, the signing of Apixio and Centene will work to boost the Apixio platform via the additional data and AI tech provided.
SOURCE HEALTHCARE ANALYTICS	Information, analytics, and consulting services are provided by Source Healthcare Analytics, Inc. The Information Technology firm gathers and analyses data on pharmaceuticals and other elements of healthcare. Its contribution to the field of healthcare analytics dulate in the embodiment of a leading provider of physician-level pharmaceutical suited environment based on the strategic market analysis from various perspectives. It serves government agencies such as the DEA, FDA, and CDC, as well as departments of health, academic researchers, pharmaceutical businesses, biotechnology companies, and generic medicine companies. It also provides impartial and annotated similar services to the private firms.
CARE ANALYTICS	Care Analytics is a CoreQ (AHCA/NCAL) provider with national certification. Taking a more modern approach, it evaluates patient satisfaction and resolving concerns with our real-time tablet-based software, SMS (text) point of care survey, and communication platform for communicating critical messages easily. The Care Analytics Star Ratings Solution generates star ratings from the patient, resident, and family evaluation data and displays them on your website. This allows you to portray a genuine image of the care provided by your company. Care analytics uses AI processing to evaluate every comment from a patient, resident, or family member to decide which comments are obviously acceptable for publication and which comments require extra assessment by the customers.
VITALWARE HEALTH CATALYST	More than 1,000 hospitals have switched to VitalCDM, giving them the visibility and clarity they need to succeed in today’s fast-paced market. The company’s all-star revenue cycle team ensures that hospitals, clinics, and health systems are always up to date. Vitalware Health Catalyst proves its impeccability by deploying its powerful data analytics technology to various healthcare organisations. As healthcare standards change often, Vitalware gives constant access to the most up-to-date invoicing, charging, and code data. The experts will assist you in identifying and eliminating reimbursement and compliance issues that may arise as a result of missing (or misreading) regulatory changes.
LOGEX HEALTHCARE ANALYTICS	LOGEX is a pioneer in the field of healthcare analytics. By giving clarity to decisions that result in the greatest possible healthcare, they empower stakeholders at every level of the healthcare system. They place data as the main idea behind allocation, relocation, improvement, benchmark outcomes, with its strong data management system providing close-knit security. The company delivers advanced analytics tools that provide actionable insights for improving the value of care. LOGEX solutions identify opportunities to take focused action and ensure that resources are directed where they will have the greatest impact. All the decisions made for escalating the positive impacts within controlled costs are data-driven.
FHIR ANALYTICS	FHIR, headed by HL7 International, intends to alter that by allowing technology firms to share patient data rapidly and smoothly using a single, standards-based data and API format that prioritises security and privacy throughout the process. The API format technology can forecast trends, promote growth, and gain actionable insights by combining real-time and historical data analysis over the centralised database. To assist patient care efforts, this combination can enhance existing procedures as well as improve offerings. The addition of powerful embedded analytics tools directly into SaaS applications is now possible thanks to this new medical data standard.
PARA HEALTHCARE ANALYTICS	It provides services to medical offices, ambulatory care centres, skilled nursing institutions, and independent testing facilities and covers all areas of the healthcare revenue cycle. PARA is made up of people that have a lot of knowledge in specific areas of healthcare to help with the revenue cycle. A key part of PARA’s purpose is to provide revenue cycle solutions that are value-based, to be regarded as an industry leader when it comes to providing value and outcomes, and to lead the healthcare consulting market when it comes to enhancing financial management to support the delivery of care. Providing worthy value-based solutions, its contribution to healthcare runs on skilled consultancy services to hospitals.
INNOVACCER HEALTHCARE	They are the sole provider of a fully integrated, cloud-based technology stack that is specifically designed and optimised for the healthcare industry. Its cloud-native architecture is modular and extendable, allowing clients to use their existing technology investments while also introducing new digital tools and capabilities. The unified patient records developed is a component of the largest and safest ecosystem of health clouds in the USA. It believes that to help individuals attain and sustain excellent health requires a holistic approach based on exceptional data integrity and completeness. The company is born with a sense of urgency. Customers may do in weeks what would take them months with homegrown solutions or other providers.
DEERWALK ANALYTICS	They aim to provide the clients with technologies that facilitate data interoperability in healthcare decision-making. The goal is to increase the quality of treatment while also lowering costs by developing a healthcare environment that allows for the free flow of secured data without the use of data silos. Bundle and schedule report from a large library of financial, clinical, and executive-style dashboards with ease. Medicare cost comparisons, needless ER visits and admissions, generic vs brand name medications, and location of service savings opportunities are just a few of the areas where the system provided insights identifying areas of expenditure and possible savings.
KLAS HEALTHCARE ANALYTICS	KLAS’ aim is to enhance global healthcare by amplifying providers’ and payers’ voices. As technology gets more advanced, the scope of the company’s research is continuously growing to best meet consumer demands. It is known as the best Intelligence and Analytics provider of business; the healthcare contribution of KLAS relies on accurate insights. KLAS obtains difficult-to-find HIT data by cultivating strong connections with industry payers and providers. Around 900 items are actively tracked by KLAS. For each product, it aims to compile a comprehensive list of clients. When a vendor refuses to furnish a complete list, KLAS employs a variety of techniques to locate as many potential consumers as feasible.

Advances in Healthcare Analytics

Being a highly dynamic and vulnerable industry, healthcare requires profound infrastructure to mitigate the expected future contingencies. The diversified analytical techniques have ignited a huge revolution in the healthcare domain. Reportedly, 90% of business professionals and enterprises see data analytics as the key to business transformation and power house of the Data Analytics. Moreover, the global market of healthcare analytics is expected to surge from $14 billion in 2019 to 28.3% by 2024.

The sudden surge in the analytical use cases for the healthcare industry is a result of intelligent techniques being put to use. These tools and techniques are potent to drive significant change in the healthcare ecosystem. All it requires is sufficient infrastructure to get merit out of these proficient model setups. Thus, the strength of the healthcare model lies in its related tools and techniques without which it remains a dummy concept.

1. HIMSS Maturity Models

It stands for Healthcare Information and Management System Society which is a global thought leader and member association focused on bringing healthy transformation in the healthcare ecosystem. The model measures the functioning and performance of the Electronic Media Record to ensure optimized patient care at each of the eight (0-7) stages. Besides, the stage classification HIMSS maturity model is studied by bifurcating into several model sets.

• Adoption Model for Analytics Maturity (AMAM)
• Continuity of Care Maturity Model (CCMM)
• Clinically Integrated Supply Outcomes Model (CISOM)
• Digital Imaging Adoption Model (DIAM)
• Infrastructure Adoption Model (INFRAM)
• Outpatient Electronic Medical Record Adoption Model (O-EMRAM)
• Electronic Medical Record Adoption Model (EMRAM)

2. Healthcare Payer Analytics

Effective collaboration between care teams is extremely essential to eliminate unreliable communication and performance gaps. As a result, the healthcare payer analytics model plays a crucial role in promoting reliable information and analysis sharing. The core application of the technique extends to the healthcare payers like insurance carriers, employers, governmental organizations, etc. These payers rely on the analytics and its reporting softwares to allow the creation of better clinician networks. Recently, payers have started to adopt such analytics technology for some essential purposes.

• Approach transition from Fee-for-Service (FFS) to Value-based-Care( VBS)
• Pay-for-Reporting (P4R)
• Pay-for-Performance ( P4P)
• Risk-and-Revenue Sharing

3. EHR Data Analytics

In “Big Data” healthcare territory the involvement of business intelligence and analytics help to get an edge over mass information. Thus, EHR data analytics facilitates the efficient management of medical organizations leading to the establishment of better healthcare facilities. The 3V’s of the EHR system make it different from the traditional approach of the healthcare data recording process. These are extraordinarily high volume data recording, movement of data at higher Velocity, and the variable nature of the model. The basic functioning of such data analytics include:

• Facilitate timely generation of reports to address pressing matters at the earliest.
• Providing immense data security through controlled access adhering to the HIPPA regulations.
• Discovering patients’ behavioral trends to improve the healthcare ecosystem.

4. Health Catalyst DOS

The renowned health care analytics company, Health Catalyst introduced an engineering approach known as Health Catalyst Data Operating System. It is a combined result of data warehousing, health information exchanges, and clinical data repositories to ensure the effective use of healthcare data. Moreover, the DOS interface helps to customize patient care by eliminating the generic approach and developing decisive insights. . The main features of the health catalyst DOS are:

• Reusable business ad clinical logic.
• Existence of streaming data,
• Integrating platforms for structured and unstructured data.
• Incorporates EHR and machine learning algorithm.
• An agnostic data hub.
• Micro services architecture.

5. Healthcare Revenue Cycle Analytics

It is a web-based business intelligence technique to utilize standard revenue cycle transactions and health product data. As a result, it broadly focuses on billing efficiencies, payer performance, related KPIs, and reimbursements aspects of the healthcare organization. The revenue cycle analytics is a blend of several features which make it the prominent healthcare analytics technology.

• The informative dashboards depicting the drilled reports help to deliver comparative study and trending analysis.
• The model uses prominent keys like NAHAM and HFMA to serve the client base.
• It plays an essential role in linking account data with the revenue cycle.
• The technique revolves around pre-service, point-of-service, post-service, and denial metrics.
• It is an integrated algorithm of analytics with Experian health solutions.

6. Healthcare Data Analysis using Python

Being an open-source language, Python contributes immensely in giving shape to innovative healthcare solutions. The language complies with the HIPPA checklist to ensure complete medical data safety. Using Python, robust and dynamic apps can be created leading to the creation of a convenient ecosystem for healthcare stakeholders.

• Predictive analytics is the core benefit of Python programming which helps to predict diseases and identify vulnerable population sets.
• It facilitates image-based diagnostics to ensure accuracy in the process.
• With its holistic approach Python programming helps to create better patient management culture.

7. Healthcare Analytics Adoption Model

It is the sub-set of HIMMS model which is capable to improving the healthcare ecosystem beyond clinical care and decision. Moreover, Adoption Model for Analytics Maturity (AMAM) is a strong analytics tool to turn data into an actionable insights and leverages improvement in workforce dimensions of the digital health too. The ANAM is a 360 degree approach to improve healthcare infrastructure, customized customer experience, diagnosis process, and risk management and report generation aspects of the healthcare organization. It is studied and implemented in eight (0-7) stages which cover all the major healthcare aspects.

Stage	Stage Title
Stage 7	Personalized Medicines and Prescriptive Analytics
Stage 6	Clinical Risk Intervention
Stage 5	Understanding Economies of Care, Population Health and Quality of Care
Stage 4	Measuring Evidence-based Care and Waste Reduction
Stage 3	Agile, Consistent and Efficient Internal & External Report Production
Stage 2	Core Data Workout : Centralized Database Management with the support of an Analytics Competency Center
Stage 1	Foundation Building : Data Governance and Aggregation
Stage 0	Fragmented Point Solutions

Challenges for Data Analytics in HealthCare Industry

The big data analytics is certainly a perk which develop proactiveness in the health care system, but on contrary have some pitfalls to tackle with great diligence.

Challenge	Description
Lack of Data Sharing	The crucial data extracted from the relevant sources hoards at few places, wherein the other service providers act in a vacuum. It results in depriving health management and deteriorating value-based care. Therefore, it is essential to make use of emerging tools such as FHIR and public APIs to establish a healthy ecosystem for big data exchange.
Privacy Pressure	The health care data largely includes patient medical records which are highly personal and shall be preserved from any form of leakage. Many institutions are yet to ensure the data privacy, thus it is required to make the best use of cloud-based health IT Infrastructure and maintain strict data access protocols for the staff.
Inappropriate Data Recording	Ideally, the data capturing process should be clean, complete, and accurate which still remains the ongoing battle in the health sector. It is a result of poor EHR usability, convoluted workflows, partial understanding, etc. Thus, it is important to prioritize valuable data types for each specific project and develop clinical documentation improvement program.

Scope of Health Data Analytics

On the basis of a report published by Allied Market Research, the global market for health data analytics is expected to grow by 21.2% and creating a market of $8464 million by 2025. Evidently, the role of data analytics in healthcare is considerably gaining momentum in the global market. Additionally, the Society of Actuaries has clearly stated in their 2019 reports that 60% of respondents already brought AI-based KPIs in the use, and figure is expected to increase by 20% in a couple of years. Moreover, the global crisis of 2020 has broadened the scope of data analytics in the healthcare to avoid manual error and mitigate the outburst like Covid-19 in an organized manner.

Inarguably, amongst various health concerns, mental health is of great significance but sadly it is emerging as the ill-side of the healthcare sector. According to the study of WHO, in a year about 800,000 people end their lives by suicide, and 20 million attempts to make it a success. So, it is high time to help such individuals combat depression, chronic stress, and suicidal thoughts. Thus, it is essential to make the use of data analytics-based EHR to ensure timely mental health visits of the specific patient. Several nations across the globe have already taken an initiative to leverage healthy lifestyles to people through accurate predictive modeling backed up by data analytics technologies and solutions.

Final Words

The existence of data analytics in the healthcare sector is fully potent to challenge real-time health issues. Yet several people across the world still stand deprived of its benefits. Therefore, governance and legislation need to come into the picture and establish a balance between its potential benefit and existing shortcomings. Health data analytics has already set its mark for the future of healthcare delivery, thus earlier it’s adopted, the better it serves the health and other related sectors.

Topics in Data Analytics

• Advanced Data Analytics
• Clinical Analytics
• Credit Risk Analytics
• Cyber Risk Analytics
• Data Analytics for Customer Behavior and Customer Experience
• Data Analytics for Customer Journey
• Data Analytics for Fraud Detection
• Data Analytics for Human Resources
• Data Analytics for Logistics and Supply Management
• Data Analytics for Risk Management
• Data Analytics for Talent Acquisition and Management
• Data Analytics in Asset Management
• Data Analytics in Digital Marketing
• Data Analytics in Healthcare – Use Cases, Metrics, Techniques, Companies and More
• Data Analytics in Manufacturing
• Data Analytics in Pharmaceutical Industry
• Descriptive Analytics – Definition, Types, Examples, and More
• Digital Analytics
• Financial Data Analytics
• Financial Risk Analytics
• HealthCare Claim Analytics
• Insurance Risk Analytics
• Insurance Risk Analytics
• Population Health Analytics
• Portfolio Risk Analytics
• Revenue Cycle Analytics
• Risk Assessment Analytics

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