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Healthcare Data Analytics 

Software Solutions, Costs, Outcomes

With 35 years of experience in data analytics and 19 years in healthcare IT, ScienceSoft builds custom and platform-based analytics software that streamlines clinical decision-making, optimizes operations and costs, and enables care personalization.

Healthcare Data Analytics Overview - ScienceSoft
Healthcare Data Analytics Overview - ScienceSoft

Contributors

Gala Batsishcha

Healthcare IT Consultant, ScienceSoft

Alex Bekker
Alex Bekker

Head of Data Analytics Department, ScienceSoft

Healthcare Data Analytics: the Essence

Healthcare analytics is needed to consolidate and analyze multi-source data related to patient health, clinical processes, and healthcare business operations. Specialized healthcare analytics solutions help improve health outcomes, promote value-based care, support clinical decisioning, and achieve higher business process efficiency.

  • Integrations: EHR/EMR, healthcare CRM, patient portals and apps, remote patient monitoring software, medical image analysis software, healthcare asset tracking software, and more.
  • Implementation costs: $100,000–$1,250,000, depending on the number of integrated sources, data complexity, compliance requirements, AI/ML-powered analytics, and more.  You are welcome to use our cost calculator to get a tailored ballpark estimate for your project.
  • ROI: up to 350%.

Healthcare Analytics Software: Key Features

Below, ScienceSoft’s consultants list the key analytics software capabilities most requested by our customers in the healthcare domain.

General analytics features

Healthcare data processing & storage

  • Automated ingestion of structured and unstructured data from various sources (e.g., ERP, CRM, patient portals).
  • Cost-optimized storage of raw data in a data lake.
  • Batch and real-time healthcare data processing.
  • A healthcare data warehouse for analytics querying and reporting.
  • Automated data governance and data quality management.
  • Data storage, transfer, and access mechanisms compliant with the required regulatory standards (e.g., HIPAA, GDPR).
  • Voice and image recognition to streamline data input and interpretation.
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Healthcare data analytics

  • Data visualization via customizable dashboards and self-service reports.
  • Automated KPI calculation (e.g., HCAHPS, ALOS, readmission rate).
  • Automated data segmentation (e.g., by patient demographics, health outcomes).
  • Continuous KPI and patient state monitoring.
  • Instant notifications and alerts (e.g., on fraud detection, changes in patient vitals).
  • Identifying trends, dependencies, and issue root causes in the healthcare data.
  • Forecasting future health outcomes and trends.
  • Smart recommendations on improving business processes and treatment plans.
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Specific healthcare analytics features

Patient-generated health data (PGHD) analytics

  • Patient data analysis, including demographic data, clinical data, and patient history.
  • Continuous monitoring of PGHD collected from wearables, sensors, patient apps, daily rounds, etc.
  • Notifications & alerts on changes in a patient’s state (e.g., abnormal vitals).
  • Identifying trends and dependencies between treatment-related activities, lifestyle changes, and patients’ vital parameters.
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Health outcomes analytics

  • Automated calculation of health outcomes KPIs, including mortality rates, readmission rates, HRQoL, PROs, and more.
  • Automated segmentation of outcomes by demographic factors, physician, facility, condition, etc.
  • Identifying trends and dependencies between health outcomes and treatment types, medications, length of stay, and other possible variables.
  • Forecasting possible health outcomes (e.g., readmissions, patient volume, high-risk patients).
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Operational analytics

  • Automatic calculation of facilities and care KPIs (e.g., ER waiting time, bed occupancy rate, patient satisfaction scores).
  • Equipment KPIs (e.g., asset utilization rate, lifespan).
  • Pharmaceuticals KPIs (e.g., medication adherence rate, inventory turnover rate).
  • Personnel KPIs (e.g., nurse-to-patient ratio, patient load, turnover rate).
  • Supply chain management KPIs (e.g., supplier performance, stock-out rate, order accuracy).
  • Identification of operational bottlenecks (e.g., long patient wait time, delayed prescription processing) and root cause detection.
  • Prediction of demand for specific services and resources (e.g., equipment, surgical facilities, medications, staff).
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Costs and finance analytics

  • Continuous monitoring and analytics of the cash flow and treatment expenses, including care delivery and overhead costs.
  • Automated segmentation of costs (per episode, condition, patient group), outstanding payments (e.g., per department, facility), actual ROI by the type of investments.
  • Notifications & alerts on due and overdue payments, potential payment or insurance fraud.
  • Identifying trends and dependencies between costs and operational processes, reimbursement policies, and health outcomes.
  • Forecasting of future costs per period, expense type, etc.
  • Predictive modeling to identify the financial impact of planned actions (e.g., changes in reimbursement policies, supplier change).
  • Smart recommendations on cost-saving opportunities and pricing optimization without negatively affecting health outcomes.
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Clinical decision support systems

  • Alerts on potential health risks and complications (e.g., allergies, drug interactions, adverse effects).
  • Intelligent diagnostic assistance (clinical decision trees, differential rankings of potential diagnoses based on patient data).
  • Laboratory findings analysis.
  • Medical image analysis.
  • AI-powered treatment recommendations (e.g., medication dosage calculation, custom treatment plans based on patient history).
  • Clinical guidelines adherence checks and alerts.
  • CDS for medical specialties (tailored decision support for dermatology, ophthalmology, cardiology, etc.) and interdisciplinary collaboration support for complex cases.
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Patient engagement analytics

  • Automated calculation of patient engagement KPIs, including patient dropout and portal engagement rates, patient loyalty, etc.
  • Identifying trends and dependencies between engagement levels and various dimensions (e.g., facilities, therapeutic departments, disease statuses, age); engagement levels and engagement activities (e.g., follow-up calls, preventive screening reminders).
  • Smart recommendations on improving patient engagement rates.
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MD, Healthcare IT Consultant

Healthcare analytics solutions with advanced clinical decision support features may be classified as Software as a Medical Device (SaMD). For instance, this applies to software that enables remote control over wearable medical devices (e.g., an infusion pump or an implantable neuromuscular stimulator), provides AI-powered interpretation of medical images and test results, or sends alerts on patients’ states for potential clinical intervention. Since such software can directly affect patients and significantly influence treatment-related decisions, it must comply with IEC 62304:2006/Amd 1:2015 and ISO 13485 standards and requires FDA approval. To make sure our customers have flexibility in choosing their healthcare analytics features, ScienceSoft invests heavily in our expertise with the above regulations. We are ready to appoint relevant compliance experts to guarantee the future software adheres to all the requirements no matter how strict they are.

4 Types of Healthcare Analytics

When implementing healthcare analytics software, ScienceSoft differentiates between the following four types of analytics. Simpler systems may only enable one or two of them, while the most advanced solutions often combine all four.

Descriptive

Analyzes historical data to say what happened. For instance, to calculate the average ER wait time for the last month.

Diagnostic

Uses historical data and statistical analytics techniques to explain why things happened. For instance, why the average ER wait time was higher than usual last month.

Predictive

Applies AI/ML capabilities to historical data and builds what-if scenarios to predict what will happen. For instance, how high the average wait time will be next month.

Prescriptive

Uses AI/ML capabilities to recommend what to do to avoid an unfavorable prediction and achieve better results. For instance, how to reallocate stuff to avoid high ER wait times.

Check the Examples of Insights You Can Get with Healthcare Analytics

Monitor all the required patient vitals and instantly spot alarming tendencies in a single dashboard

Track patient satisfaction scores, easily identify bottlenecks, and improve patient experience

Evaluate your marketing effectiveness, identify the demand for provided services, and track patient satisfaction dynamics

Get full visibility into your inventory management and save time with automated replenishment

Avoid over- and understocking with accurate demand forecasts for supplies

Build a Reliable Healthcare Analytics Solution with Experts

ScienceSoft’s business analysts, solution architects, developers, and compliance experts are ready to assist you in implementing a comprehensive healthcare analytics system that perfectly fits your unique goals and processes.

Essential Integrations for a Healthcare Data Analytics Solution

Integrations for a Healthcare Data Analytics Solution - ScienceSoft

To support clinical decision-making (e.g., by facilitating diagnosing or suggesting the most effective treatment option).

  • To enable efficient patient segmentation and targeting.
  • To identify best practices and service gaps.

Note: Alternatively, your healthcare analytics solution can be integrated with call center software.

  • To enable continuous monitoring and analytics of the patients’ vitals with immediate alerts in case of abnormal readings.
  • To ensure personalized care or patient management depending on a particular condition or goal.
  • To optimize employee engagement and retention strategies and ensure proper staff credentialing.
  • To shape and improve initiatives for employee performance optimization.
  • To ensure smooth scheduling.
  • To enable billing and financial management optimization and increase revenue.
  • To ensure automatic detection of fraudulent claims.
  • To optimize asset utilization and minimize related losses (e.g., due to missed opportunities for reuse or incorrect storage conditions).
  • To improve inventory management.
  • To enable efficient medication distribution control and decrease medication shortage risks.
  • To plan timely equipment maintenance.

MD, Healthcare IT Consultant

Multiple EHR-adjacent software systems (e.g., practice management, LIS, HIE) can communicate with the analytics module via the integrated EHR or directly via APIs. In each specific case, we design a custom integration architecture based on our customers’ unique IT infrastructure to deliver the most efficient solution.

How ScienceSoft Drives Value of Healthcare Analytics Solutions

We follow our established practices for scoping, cost estimation, risk mitigation, and other project management aspects to reach project goals regardless of time, budget, and change request constraints.

Tailored functionality

With off-the-shelf software, you often have to settle for standard functions that don’t fit your processes, or pay for fancy features you don’t need. Focused on bringing value with our services, we plan and implement efficient software that answers the specific needs of our customers.

Quality-first approach

Holding ISO 9001 and ISO 13485 certifications, we can guarantee reliable delivery of fault-tolerant, secure, and high-performing software. We continuously grow our technology expertise to be able to offer robust software solutions to our clients in healthcare.

Mature Agile culture

Relying on mature Agile processes established over decades of practical experience, we can deliver an analytics system MVP in 2–6 months and gradually upgrade it to the fully-featured solution in stable 2–4-week iterations.

Cost optimization

To optimize software development costs, our developers use proven third-party components, open-source APIs, and microservices that enable significant code reuse. Our established DevOps practices, efficient CI/CD design, and feasible QA automation help us reduce the total development cost by up to 78%.

Focus on security and compliance

Proud of having zero security breaches in our 34-year history, we keep investing in our ISO 27001-certified security management system. Maintaining a team of in-house security engineers and regulatory experts, we can guarantee full software compliance with HIPAA, GDPR, FDA, and other regulatory requirements.

Advanced technologies

In data science since 1989, we enhance analytics solutions with AI capabilities that power intelligent automation and forecasting. We also apply a decade of experience in big data to ensure accurate and timely analytics across massive data sets.

Costs and Benefits of Healthcare Data Analytics Implementation

The cost of healthcare analytics may vary from $100,000 to $1,250,000, depending on data complexity and diversity, the number of integrations, the presence of AI/ML analytics capabilities, and more.

$100,000–$250,000

A basic solution that:

  • Integrates with 1-3 key data sources, like EHR or CRM.
  • Enables batch data processing (e.g., once every 24 hours).
  • Calculates the essential operational and financial KPIs.
  • Identifies trends and dependencies in health and operational data.

$250,000–$500,000

A solution of medium complexity that:

  • Integrates with multiple internal sources (e.g., RMS, asset tracking software, HR software).
  • Enables batch and real-time analytics.
  • Enables root-cause analysis, patient and outcomes segmentation, and forecasting.
  • Provides rule-based and ML-powered analytics.

$500,000–$1,250,000

An advanced solution that:

  • Integrates with any number of internal and external sources, including unlimited patient apps and IoT devices.
  • Integrates with several same-type sources, e.g., two CRMs of different divisions.
  • Enables batch and real-time analytics, including big data processing.
  • Enables advanced analytics, including AI-powered predictions and recommendations.

Want a more precise figure?

Our consultants will provide a tailored cost estimate for your data analytics initiative.

Get a quote

*Software license fees are not included.

87% of Organizations Report Measurable Business Value from Their Data Analytics Investments

The 2024 Data and AI Leadership Executive Survey is based on feedback from over 100 global industry leaders that have implemented data analytics solutions. The report features 10+ prominent organizations from the healthcare domain, including Pfizer, Mayo Clinic, UCLA Health, and more.

Market-Available vs. Custom Healthcare Analytics Software

When searching for out-of-the-box solutions, you’ll find several platforms that offer ready-made products for different types of analytics (e.g., for patient outcomes, revenue cycle tracking). Although these products can be customized or integrated into one with the help of native APIs, the capabilities of such software are still limited to a predefined set of features. If you are looking for a comprehensive system covering several analytics types at once (e.g., finance, operations, asset tracking) or a highly precise tool for a medical specialty (e.g., CDS for oncology), you’re likely to find OOTB solutions insufficient.

On the other hand, custom software means you’re getting a tailored solution that meets all your analytics needs and fits your unique IT environment. The downsides of custom software are related to substantial initial investments and relatively lengthy implementation. However, such solutions drive up to 350% ROI with an average payback period of 9 months, which makes the investments worthwhile in the long run.

The high ROI of custom solutions is driven by:

  • A bespoke feature set with any level of analytics complexity (e.g., multi-dimensional patient segmentation, financial analytics across different taxation systems).
  • Smooth integration with all the required systems: back-office and legacy software, third-party platforms (e.g., a clearinghouse), IoT devices, and more.
  • Compliance with the required global or local regulations (e.g., HIPAA, FDA, ADHICS), convenient functionality for compliance checks and reporting.
  • Guaranteed scalability in case of user and data volume increase.
  • Tailored interfaces for different user groups (e.g., hospital administrators, physicians, MLSs), leading to increased convenience and higher productivity.

Highlights of ScienceSoft’s Healthcare Analytics Portfolio

Build Your Healthcare Data Analytics Solution with Experts

Consulting on healthcare analytics

Whether you’re building a new analytics solution or upgrading your existing systems, we’re ready to provide strategic assistance. Our consultants will design a scalable architecture and choose an optimal tech stack for your initiative, ensure full solution compliance and security, and help avoid unnecessary risks when planning the project.

I’m interested

Implementation of healthcare analytics

We build secure, efficient, and user-friendly analytics solutions that are easy to support and upgrade with new features in the long run. We also provide comprehensive software documentation to streamline compliance checks and maintenance and can deliver long-term support services or training for your team.

I’m interested

About ScienceSoft

ScienceSoft is an IT consulting and software development company headquartered in McKinney, Texas. Since 2005, we help healthcare organizations leverage the potential of advanced data analytics to deliver better care, improve health outcomes, and achieve better operational efficiency. Being ISO 9001 and ISO 27001-certified, we can guarantee top software quality and complete security of our customers’ data.