Healthcare Data Analytics
Software Solutions, Costs, Outcomes
With 34 years of experience in data analytics and 18 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: 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.
- 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.
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.
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.
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).
- 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).
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.
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.
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.
Healthcare analytics solutions with advanced clinical decision support features are classified as Software as a Medical Device (SaMD). Since such software can 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.
Analyzes historical data to say what happened. For instance, to calculate the average ER wait time for the last month.
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.
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.
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
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Essential Integrations for a Healthcare Data Analytics Solution
To support clinical decision-making (e.g., by facilitating diagnosing or suggesting the most effective treatment option).
- 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.
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
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.
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.
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.
Costs and Benefits of Healthcare Data Analytics Implementation
The cost of healthcare analytics may vary from $100,000 up to $1,250,000, depending on data complexity and diversity, the number of integrations, the presence of AI/ML analytics capabilities, and more.
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.
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.
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.
*Software license fees are not included.
92% of Organizations Report Measurable Business Value from Their Data Analytics Investments
The 2023 Data and Analytics Leadership Executive Survey is based on feedback from over 100 global industry leaders that have implemented data analytics solutions. The report features 20 prominent organizations from the healthcare domain, including Pfizer, Astra Zeneca, Novartis, and Mayo Clinic.
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
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.