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Healthcare Analytics Consulting
With 50+ projects in data analytics and 150+ projects in healthcare, ScienceSoft delivers comprehensive medical solutions for analysis of patient-generated health data, patient outcomes, care costs, and more.
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Healthcare analytics consulting is a way to get actionable insights from healthcare data and support fact-based decision-making. In data analytics since 1989 and in healthcare since 2005, ScienceSoft provides healthcare data management services to optimize customers' clinical and business processes.
Healthcare Data Analytics Market Overview
In 2022, the healthcare data analytics market was estimated at $37.15 billion worldwide. Analysts expect the market to reach $121 billion by 2030, increasing at a CAGR of 15.9%. Over half of the market is represented by solutions for life science companies, followed by solutions for healthcare providers. Among the key growth factors are the need to enhance medical staff performance, cut unnecessary costs, and improve health outcomes.
How the Healthcare Industry Utilizes Data Analytics
- 56% of leading healthcare organizations worldwide have adopted/started adopting some type of predictive analytics software, according to Statista.
- 85% of healthcare executives consider data analytics vital to achieving their business objectives.
- 51% of healthcare organizations say that data integration and interoperability are key barriers to implementing efficient data analytics.
- After hospital data analytics implementation, healthcare providers report that 70-80% of emergency readmissions are predicted and can be prevented by timely care interventions.
Healthcare Analytics Services We Offer
Healthcare data analytics consulting
What we do:
- Analyze your needs to identify how your org can benefit from data analytics.
- Design a detailed healthcare analytics strategy.
- Design a data analytics solution and plan an implementation roadmap.
- Support your org during the project design, implementation, and evolution.
Full healthcare analytics outsourcing
We take over:
- Needs analysis to design a tailored healthcare analytics solution.
- Selection of a healthcare data analytics platform and a tech stack.
- Implementation of a secure and compliant solution, development of analytics modules to complement the existing apps.
- Support, maintenance, and evolution of a cloud analytics solution (if required).
ScienceSoft, a Reliable Healthcare Analytics Company
- 35 years in data analytics and 11 years in image analysis and big data.
- 19 years in healthcare IT, data warehousing, and BI services.
- Expert at HIPAA, HITECH, GDPR, and more.
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A seasoned team of healthcare analytics consultants, developers, and solution architects with 12–27 years of experience.
- A trusted partner of Microsoft and an authorized AWS Solution Provider.
- Security management approach and quality management system for medical device software proven by ISO 27001 and ISO 13485 certifications.
- Health Tech Digital awarded ScienceSoft’s RPM solution with the Best Healthcare Technology Solution Award 2022.
- Featured among the leaders of the medical image analysis software market researches by MarketsandMarkets and Coherent Market Insights.
Healthcare Analytics Solutions ScienceSoft Offers
- Design of a healthcare data warehouse and data marts to structure healthcare data for analytical querying and reporting.
- Protected Health Information (PHI) storage.
- Integrated storage of EHR/EMR, ERP, HR management systems data, data from public medical databases, R&D results data, etc.
- Operational analytics (staff scheduling optimization, facility maintenance, claims management, etc.).
- Patient analytics (patient segmentation, patient journey analytics, treatment plans optimization, etc.).
- Financial analytics (financial fraud detection, financial risk management, etc.).
- Performance monitoring of the healthcare staff.
- Pharma data analytics (production cycle optimization, clinical trials data analysis).
Healthcare data visualization
- Real-time self-service operational dashboards (hospital admissions and load, EMR/EHR data, etc.).
- Strategic interactive dashboards (key performance indicators, patient trends, hospital performance, etc.).
Big data in healthcare
- Capture and storage of EHR/EMR data, medical device data, data from patient apps, portals, other patient-related data.
- Real-time medical device analytics.
- Remote patient monitoring, alerting on trends and patterns in a patient’s condition requiring a doctor’s attention.
- Personalized care plan recommendations.
Predictive analytics
- Healthcare supply chain optimization and planning (ML-based demand forecasting, supplier risk management, etc.).
- Disease management (ML-based prediction of patient readmissions, diagnosis validation, etc.).
- Health insurance management (defining optimal health care plans, potentially fraudulent claims or high-value losses, etc.).
AI-enabled medical imaging
- Establishing diagnosis.
- VR-enabled 3D imaging to provide interactive education for practitioners and medical students.
- AI-powered virtual assistants to provide patients with answers/recommendations based on their medical history, personal needs, etc.
The primary goal of any efficient analytics solution is to eliminate data silos and create a single point of truth. This can be especially challenging due to the number and the interconnection of possible data sources for the solution-to-be. For instance, clinical analytics will surely benefit from LIS and RIS data. Still, most of our customers already have these systems integrated with their EHR, which would make individual integration a waste of resources. That is why ScienceSoft usually starts with auditing data sources and eliciting the goals our customers want to achieve with their data.
Success Stories
9 results for:
Our Customers Say
bioAffinity Technologies hired ScienceSoft to help in the development of its automated data analysis software for detection of lung cancer using flow cytometry.
Our project required a large amount of industry specific methodology and algorithms to be implemented into our new software connected to EHR/LIS systems, which the team handled well. They are reliable, thorough, smart, available, extremely good communicators and very friendly.
During our cooperation, ScienceSoft proved to have vast expertise in the Healthcare and Life Science industries related to the development of desktop software connected to laboratory equipment, a mobile application, and a data analytics platform.
They bring top-quality talents and deep knowledge of IT technologies and approaches in accordance with ISO 13485 and IEC 62304 standards.
ScienceSoft designed and developed a native iOS app that offers a quantitative assessment of users' physical fitness.
I was impressed with the excellent level of responsibility, communication skills, and mobile competencies of both the management team and developers.
Data Analytics Capabilities for Healthcare
Analyzing patient progress indicators
to identify treatment anomalies and trends.
Sample reports:
- Variations of outcomes based on demographic factors (age, income level, lifestyle).
- Variations of outcomes based on a health organization’s internal factors (a specific physician, treatment, facility, and more).
Data sources:
- EHR / EMR.
- ADT.
- Department-specific systems (Cardiology, Dentistry, Neurology, etc.).
- Disease management/population health management.
Analyzing PGHD (patient-generated health data)
to enable constant monitoring of patients’ health status, identification of irregularities, treatment plan progress evaluation, early recognition of symptoms of complications, etc.
Sample reports:
- Blood glucose levels (for diabetes patients).
- SpO2 level changes (for COPD patients).
- Overall trends on patients’ nutrition, hydration, temperature, weight, blood pressure, etc.
Data sources:
Mobile patient apps and portals for:
- Chronic condition management.
- Appointment scheduling.
- Medication intake tracking.
- Post-surgery recovery support, etc.
Analyzing costs
to define the expenses for treating particular conditions or patients groups.
Sample reports:
- Variations of expenses for treating specific conditions.
- Relationships between patient outcomes and costs covering certain periods with insights on the results of changes in the care process.
Data sources:
- EHR / EMR.
- LIS.
- RIS.
- Department-specific systems.
- Accounting system / ERP.
Extended cash flow reporting
to enable financial planning and optimization.
Sample reports:
- Expected cash flow based on RCM data.
- Actual cash flow.
- Outstanding payments by specific payers (departments, facilities, diseases).
- Actual ROI by the types of investments (new facilities, medical equipment, such as CT or MRI, etc.).
Data sources:
- ERP or FMS.
- Revenue Cycle Management.
Comprehensive dashboards, charts, and diagrams
for transparent performance evaluation.
Sample reports:
- Bed utilization efficiency.
- Equipment utilization rates.
- Operating rooms utilization.
- Medication use.
- Clinical HR analytics (employee qualifications, certifications, hiring, and more).
Data sources:
- PMS.
- EHR.
- LIS.
- RIS.
- Workforce management system.
- Department-specific systems.
Analyzing operations
to calculate and track operational KPIs, identify process bottlenecks and their reasons, predict demand for services and assets.
Sample reports:
- Inventory turnover rate.
- Nurse-to-patient ratio.
- Supplier performance rate.
- Staff demand for the next week.
Data sources:
- EHR.
- HR software.
- Practice management software.
- Inventory management systems.
- SCM systems.
- Facility management systems.
Analyzing patient engagement
to track patient satisfaction, identify service and care gaps, and implement relevant improvements.
Sample reports:
- Patient portal engagement rate.
- Patient dropout rate.
- Engagement activity efficiency.
Data sources
- Patient portal.
- Telemedicine app.
- Patient surveys.
Providing smart treatment-related recommendations
to support clinical decision making with ML- and AI-generated output.
Possible output:
- AI-powered treatment medication dosage recommendation.
- Custom treatment plan.
- Alert on possible complications and health risks.
Data sources:
- EHR.
- LIS.
- RIS.
- Wearable devices.
Check the Examples of Insights You Can Get with Healthcare Data Analytics
Our Healthcare Data Analytics Tools & Techs
Our Supplementary Healthcare IT Services and Solutions
Healthcare software development services
We build HIPAA-compliant medical solutions with tailored functionality to provide enhanced medical care and increase patient and employee satisfaction.
Telemedicine apps
We construct custom and platform-based telehealth apps leveraging big data streaming for:
- Chronic disease management
- Primary care
- Acute care
- Nursing care
- Digital therapeutics
- Neurology, etc.
Medical mobile apps
We develop handy mobile apps for patients, healthcare providers, and medical professionals.
Internet of Medical Things (IoMT)
We provide consultancy, implementation, and support services to deliver reliable IoT solutions for:
- Hospital asset tracking.
- Patient and staff tracking.
- Medical devices.
- Smart hospital components, etc.
Virtual reality in healthcare
We create healthcare VR solutions with high and fast user adoption for:
- Medical education
- Medical training
- Surgery
- Pain management
- Rehabilitation, etc.
Healthcare CRM solutions
We build CRM solutions tailored to healthcare industry specifics based on Microsoft Dynamics 365 and Salesforce Health Cloud.
Frequent Questions About Healthcare Analytics, Answered
What types of data are used in healthcare data analytics?
Depending on the integrated sources, a healthcare data analytics solution can handle clinical, operational, and financial data, e.g.:
- Patient and treatment data: patient medical history, health outcomes, discharge summaries, medication records, lab results.
- Population health data: mortality rates, life expectancy, health-influencing behaviors and lifestyles, outcomes of public campaigns.
- Patient-generated data: logs of symptoms and vitals from patient apps and portals, real-time vitals data from sensors and wearables.
- Insurance, billing, and cost data: data on rejected claims and the reasons for denial, patient payment history, accounts receivable and accounts payable.
- Patient-hospital interaction data: patient satisfaction, no-shows, and post-visit surveys.
- Clinical trial and research data: the participants' demographics, outcomes, side effects, compliance with established protocols.
- HR data: employee performance, compensations, and turnover data.
- Internal processes data: patient flow, resources utilization, performance metrics.
- Pharma analytics data: genomic data, drug sales data.
How do you ensure the security and privacy of your customers' data?
As a seasoned healthcare data analytics company, we rely on our mature ISO 27001-certified security management system and leverage the experience of our in-house healthcare compliance experts. ScienceSoft develops healthcare analytics software (including advanced analytics solutions like SaMD) in complete alignment with all the required global and local standards, including US and EU regulations (HIPAA, GDPR, FDA, and MDR) and the requirements of the GCC countries' health authorities (e.g., MOH, ADHICS).
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