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Healthcare Analytics Solutions | Predictive Analytics

Advance Healthcare Analytics Solutions for Better Clinical Performance


Advance Healthcare Analytics Solutions for Enhanced Patient Care


The common bottlenecks which might slow down your business growth


Data Security

Workflow should be optimized for the secured exchange of information & transactions allowing the providers to make real time updates



Fast Health Interoperable Resource (FHIR), better than existing standards like CDA & HL7 v3, can be implemented for an effective data sharing in real time


Data Format & Codes

Though CCD and CDA are the preferred data format & code used in healthcare, they are not specific enough. They should be constrained further.


Insufficient Technology

Inaccurate data can be resolved by intelligent analytics tool implementation which can list out the issues and troubleshoot them


Regulatory Issues

Though health information is HIPAA protected, opt-in opt-out model should also be implemented where patient consent is obtained for health data exchange


Domain Collaboration

Healthcare stakeholders—private-sector innovators, entrepreneurs and researchers need patient-centered outcomes by collecting and analyzing data.

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Healthcare Analytics Process

Leveraging the power of technology and expertise to empower healthcare with new data discoveries.

End-To-End Solutions Made for Healthcare

Agility combined with industry expertise, design-led engineering, and innovation to results in excellent healthcare software products.

Healthcare Electronic Data Warehouse (EDW)

EDW is a vital tool for effective management and clinical decision making. The need to monitor and prevent infection transmission provides an ideal case for sharing data between multiple facilities.

The advantage of sharing data among different providers include monitoring of antimicrobial resistance, measurement of antimicrobial use, detection of hospital-acquired bloodstream infections, measurement of treatment and detection of antibiotics prescribing errors. The quantity of time and money saved can be estimated well by using the data warehouse.

For instance, the hospital can utilize a data warehouse to provide a list of high-risk patients linked to the patient’s next scheduled visit. This enabled the targeted delivery of swine flu vaccine to high volume clinics. The next-generation health care providers can consolidate and manage information across the continuum of care. This involves building a warehouse of clinical and financial information that can be shared by health care professionals, regardless of the location.

Dashboard Solution

Healthcare dashboards are complex tools that can aggregate the data from multiple sources and provide an in-depth performance metrics view of the whole hospital management system. The main objective of those Healthcare dashboard tools is to eliminate inconsistent data, improve reporting and data analysis, and to provide deep insight.

Healthcare dashboard metrics allow them to track the performance of the hospital in terms of commercial efficiency and treatment success rates. Knowing the source of problems allows the healthcare team to make better decisions in improving the quality of care, optimizing the workloads and reducing the costs.

A single dashboard solution can track the effectiveness of treatments in different patients and compare the work of clinicians with their colleagues. This is a very good way to enhance clinician performance and patient satisfaction.

Healthcare Business Intelligence

While dealing with the livelihood of patients, healthcare professionals should remain on the safer side when it comes to introducing new technologies into their practice. Hence to improve the quality of care, Business Intelligence (BI) should be considered seriously.

Business intelligence allows healthcare providers to go through the patient-related data where one can estimate the ability of the patient to pay the bill. Thus the financial sections can proactively reach out to patients for payment collection.

In spite of the increased amount of data, BI software can easily combine data sources to increase efficiency. Data from sources such as Electronic Medical Records (EMR), lab data and financial data gets combined and correlated in a healthcare payment system in order to provide a 360-degree view of both patient and hospital insights.

AI-Driven Advanced Reporting

The patient care system analyses patients’ data to discover insights and suggest actions accordingly. AI allows hospital management to analyse clinical data and generate deep insight into patients with great accuracy. Personalized medications and real-time prescription errors can be monitored by AI Prescriptive analytics.

Early Diagnosis is possible to analyse chronic conditions by leveraging diagnostic/medical imaging results. New drugs can be found based on previous data and medical intelligence.

Healthcare management is easier where the optimal price for treatment and other services can be determined in advance. Easy automated operations like reporting are possible using an artificial intelligence software.

Cloud-Driven Big Data Analytics

Since digital healthcare revolution is going on with full fledge, data availability irrespective of the location of the patient and the clinician, has become the major factor for improved clinical outcomes. Cloud technologies play a role in this regard.

Healthcare functionality can be enhanced by cloud-based healthcare IT systems that has the potential for great interoperability and integration. Since healthcare cloud services are totally internet-based, interoperability is very simple.

Cloud services has remote access to applications and data. The Internet connectivity enables access at anytime from anywhere. Cloud computing has special features for physicians and patients which would reduce the burden of heavy investments and utilize the outsourced resources, software, hardware and automated computing. Thus there occurs improvement in patient care, analysis of physiological data along with optimization of the medical billing process with proper resource utilization.

Modern Data Management

Modern Data Management Solutions include data-driven strategies to improve commercial effectiveness in life sciences, including faster product launches, insights about patient engagement in healthcare and real-time data enrichment.

Details like CRM, sales order management, contracts and pricing and R&D were made accessible to everyone in the field of life sciences with the help of MDM.

A quick configuration, prototyping and user-friendly modern data interface enable pharma companies to build out new commercialization competences quickly, compared to traditional MDM solutions that may take several months to years.

Predictive Analytics

Healthcare at present is on the verge of drastic transformation which will be driven by an increased amount of electronic data. The use of predictive modeling method can successfully mine this data in order to improve patient care.

Patients at high risk for poor outcomes can also be identified easily in order to improve patient prognoses. The type of conditions in real-time can be predicted well in advance before the onset of any clinical symptoms

Physicians use predictive algorithms for more accurate diagnoses. The employers and hospital staff management will be provided with predictions concerning insurance and product costs. Pharmaceutical companies use predictive analytics to meet the needs of the public for medications in a better manner.

Embedding Intelligence & Automation

Embedding Intelligence & Automation is going to increase more in number especially in the healthcare domain. The major factor to use Embedding Intelligence in the healthcare domain is health monitoring by using biosensors and smart devices. These devices play a centric figure in the relationship between healthcare providers /payers and consumers/patients. There will be an increasingly growing popularity of healthcare wearables also, which would increase consumer health consciousness.

Blockchain integration including safe data storage, protected transactions, secure data exchanges between healthcare organizations will take place. Customer service and operations including request processing and appointment scheduling take place with the help of automation to facilitate the life of patients and doctors.

By 2020, the Real-time Health Systems (RTHS) will act as a vital area for Embedding Intelligence in the healthcare sector, since there is a hope of nearly 50% increase in the usage of robots to deliver medicines and supplies throughout the hospitals.

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Population Health Analytics

  • Data aggregation includes clinical applications, claims systems, health information exchange, remote monitoring devices, etc.
  • Tracking performance scores from prognosis, prevention and treatment to maintenance and wellness management.
  • Application of risk stratification algorithms to derive better and more targeted health management programs.
  • Assessing cost and quality metrics of population health programs to deliver return on investment scores.

Hospital Analytics

  • Real-time data analytics in a hospital enterprise can process and analyse huge amount of data quickly.
  • Variations like non-compliance or threats can be quickly identified and rapidly addressed.
  • Analysis of data collected from claims data, pharma and R&D data, EHR clinical data and patient behaviour data.
  • Patient waiting time, emergency waiting and treatment costs are the major enterprise-level key performance indicators.

Preventive & Prescriptive Analytics

  • Better performance of payers, providers and pharmaceutical companies by lessening their workloads.
  • Identification of intervention models for risk population by combining data from in-facility care and home based telehealth.
  • Capacity planning control operational data combined with population health trends.
  • Drug development is feasible by identifying patient cohorts that are most suitable for the clinical trials worldwide.
  • Analytics can predict the quantity of saved time and money while choosing patient cohort in a specific country.

MACRA Reporting

  • MACRA stands for Medicare Access and CHIP Reauthorization Act, that cancels the Sustainable Growth Rate (SGR).
  • MACRA replaces Physician Quality Reporting System (PQRS), and payment reporting with Merit-Based Incentive Payment System (MIPS).
  • MACRA has got 2 pathways namely Merit-Based Incentive Payment System (MIPS) and advanced Alternative Payment Model (APM) path.
  • MIPS relies on quality, improvement activities, advancing care information and cost.
  • APM relies upon primary care, oncology care model, ESDR Care model and MSSP NextGen ACO.

Payer Business Intelligence

  • Integration of HIPAA compliant multiple data sources for measuring business performance with best decision support system.
  • Business intelligence solutions enable payers to gain insights and focus on clinical outcomes through CRM implementation services.
  • Integration of Patient Engagement Portal with a system for communication and care management.
  • Features such as scheduling, video consultation, online real time availability and wearable are integrated within one application.
  • Streamline health benefits by reducing claims to support more intensive disease management.

Care Quality Measurement & Reporting

  • Healthcare organizations are facing high pressure to improve quality outcomes, utilization efficiency and financial performance.
  • In this condition, the ability to extract, analyse and manage enormous data is critical to provide actionable insights.
  • This integrated solution provides innovative big data technologies and cloud-based data-driven strategies to identify care gaps.
  • Cloud-based proprietary modules enable real-time analytics and secured big data integration and continuous monitoring.
  • Interoperability enables the automation of clinical data extraction and supports point-of-care gap insight.

Financial Analytics

  • Understanding profitability by service line to make investment decisions based on costs and profits for common services.
  • Enhanced medical billing process with better revenue cycle management.
  • A clear picture of profitability along with consolidated reporting to aggregate levels of service line.
  • Financial analytics solution is tied to the revenue cycle to ensure accurate and consistent reporting for easy data analysis.
  • Financial Solution includes physician dashboards and details for negotiations, including service line alternatives.

Data Aggregation and Warehousing

  • Building a reliable data asset involves acquiring, aggregating and refining data from multiple sources for a complete care.
  • Production of reliable, actionable healthcare analytics on quality and preparing for the evolving world of value-based care.
  • Data aggregation and warehousing begins with systematic data intake, processing, analysis and cleansing process.
  • Data is grouped and calculated to provide information on cost, quality, access and value.

Claims Analytics

  • Claims Analytics solution includes sophisticated data mining pinpoint payment issues both pre- and post-payment.
  • Reviews claims data against hundreds of industry-defined coding standards, including CMS, NCCI, AMA, and others.
  • The solution helps you to analyse paid claims data to identify overpayments and pursue opportunities for recovery.
  • Claim analytics include monitoring claims adjudication prior to payment and ensuring payment integrity.

Fraud & Risk Analytics

  • Fraud in healthcare sector include illegal medical billing and multiple claims filed by different providers for the same patient.
  • The effective way to prevent fraud and abuse is to identify it before claims are paid with the help of analytics.
  • Provides AI techniques, including modern statistical, machine learning, deep learning and text analytics algorithms.
  • It will continually mine data, identify emerging fraudulent patterns and create new “rules” for those as well.
  • It provides a visualization interface to go beyond transaction and analyse related activities with alert management.

Clinical Business Intelligence

  • Clinical business intelligence applies data analytics to patient medical records through various systems for proper care.
  • Data consolidation from varied sources and analysis helps to make better decisions about the patient treatment.
  • Clinical business intelligence track performance, measure progress towards goals and provide accountability for results.
  • Include patient registry analysis, priority conditions dashboards for alerts, performance reporting & waste reduction.

Clinical Lab Informatics

  • Clinical lab Informatics Systems can improve the effectiveness and efficiency of the lab operations, if managed properly.
  • Automated data integration to drive scientific decisions for the advanced R&D and manufacturing in an effective manner.
  • From strategy and implementation to science and lab systems application management, it provides insight that maximizes RoI.
  • Includes Lab Informatics Strategy, LIMS Implementation, Computer System Validation (CSV) and QA/QC Management.

Clinical Decision Support

  • CDS is a health information technology system designed to provide health professionals with clinical decision support.
  • CDS has 3-Ds: Device, Data, and Decisions. Data derived from an electronic device proceeded for clinical decisions. 
  • CDS optimise every aspect of healthcare such as individual patient, the acute care setting in a hospital.
  • CDS implementation is easier to attain its full potential, which should be embedded with the patient’s EHR. 
  • CDS provide international standards for improving healthcare outcomes across the healthcare delivery network.

Medical Reporting

  • Data is maintained in a secure repository via Electronic medical device reporting (eMDR), as per regulatory requirements.
  • This eMDR software include effective data collection, proper integration with document control solutions & mapping.
  • If a complaint form is filled out, an eMDR system can be electronically initiated based upon the triggers in complaint form.

Medical Device Data Analytics

  • Medical devices offer insight into not only the efficiency of devices themselves but also clinical operations. 
  • The status and performance of medical devices gain valuable insight into clinical operations followed by improvements.
  • Medical device analytics solutions identify markets where medical devices and diagnostics are needed during underutilization.
  • It can deliver data-driven, relevant and reliable value propositions that satisfy the buyer requirements.
  • Create strategies to demonstrate ROI which will support sales forces in existent settings.

IoT & Lifecycle Analytics

  • The need for quickness, speed, quality, and compliance is more important than ever in the medical device industry.
  • Device connectivity and highly personalized medical therapies are forcing changes at a rapid pace.
  • Hence, medical device companies control IoT to pair big data analytics with supply chains across the entire value chain.
  • Our IoT solutions combine big data with a virtual representation of medical devices, connected by digital threads.
  • Connection of virtual development and planning with real support and lifecycle production data using big data.

Product Intelligence

  • Product intelligence is defined as an automated system for analysis about the performance of a manufactured product.
  • The data is automatically fed back to the product managers to assist them in the development of next version.
  • This is accomplished by pushing product and test specification software to all the test stations in pipeline over a web connection.
  • The goal is to accelerate the rate of product innovation, thereby making the product and its owners more competitive.
  • The automatic enforcement of quality manufacturing processes to check if the products are manufactured correctly.

Drug Discovery Analytics

  • Enable scientists to source scientific findings and insights from external labs to start discovery & reduce cycle time.
  • Algorithms are developed to uncover sequencing patient record data, drug-protein interaction etc. for predicting side-effects.
  • These types of predictions can be used to identify the possible drug structures with diverse essential features.
  • Big data analytics also contributes to better drug efficiency and safety for regulators and pharmaceutical companies.

Supply Predictive Analytics

  • Built-in predictive models utilize internal and external data, to reduce unforeseen shortages in drug availability.
  • Volume-based short term forecasts for drugs can track performance, demand, and supply.
  • SKU-level analysis for strategic decisions and Inventory management for long-term forecasts would be integrated respectively.
  • Predict and manage supply events such as tender acquisition, new manufacturing units and price variations.

Product Analytics

  • Product analytics turn data into actionable insights that can improve operations management across the enterprise.
  • Includes enterprise-wide data model, extensible and flexible framework for easier customization.
  • Predefined data mapping to mitigate development efforts and hasten the implementation of a robust data warehouse.
  • Data architecture which maintains availability, reliability, scalability follow best data maintenance.
  • Advanced analytical data facilitate business intelligence strategy to improve the performance with high performance metrics.

Regulatory Compliance/Internal Reporting

  • The use of analytics helps organizations ensure regulatory compliance and justify the risk of non-compliance.
  • People, process & technology were placed well to manage regulations for internal reporting & tracking.
  • Regulatory compliance can be converted into high value adding analytics impacting the service delivery.
  • PPSA (Physician Payment Sunshine Act) reporting can track and predict marketing tool RoI easily.

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