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Analytics for Medical Labs

Features, Integrations, and Costs

In data analytics since 1989 and in healthcare IT since 2005, ScienceSoft builds secure and compliant analytics solutions to help clinical, R&D, public health, and other medical labs get data-driven insights into test results, operational and financial planning, quality control, and more.

Medical Laboratory Analytics - ScienceSoft
Medical Laboratory Analytics - ScienceSoft

Contributors

Alex Bekker
Alex Bekker

Head of Data Analytics Department, ScienceSoft

Gala Batsishcha
Gala Batsishcha

Healthcare IT Consultant, ScienceSoft

Laboratory data analytics provides a consolidated view of disparate data from LIMS/LIS, EHR/EMR, ERP or financial systems, inventory management solutions, and other systems used by laboratories. It helps to optimize testing protocols, inventory management, staffing, and other aspects of lab management. Advanced lab analytics systems, including custom solutions, feature capabilities for ML/AI-powered medical image analysis, test result analysis, and scenario modeling (e.g., to simulate patterns of workload distribution).

  • Implementation time: 26 months for an MVP.
  • Costs: $30,000$500,000, depending on the solution's complexity. Use our free online calculator to get a ballpark estimate for your case.
  • Core integrations: a laboratory information management system (LIMS) or a laboratory information system (LIS), an EHR or EMR, a laboratory inventory management system, medical lab billing software, an ERP or financial management software, asset management software.

Data Analytics is Among Top Three IT Priorities for Laboratories

According to 2024 State of the Industry Report by Medical Laboratory Observer, data analytics optimization to support lab management is among the major IT investments for labs alongside infrastructure development and revenue cycle management optimization.

Analytics for Laboratories: High-Demand Features 

Lab operations analytics

  • Monitoring operational KPIs (e.g. turnaround time, cost per test, volume of unnecessary tests, billable tests versus performed tests, staff productivity).
  • Asset utilization analytics (e.g., tracking equipment-related metrics like accuracy and lifespan; recommendations on optimizing instrument usage).
  • Predictive equipment maintenance.
  • Environmental analytics (e.g., tracking lab’s temperature and humidity indicators, providing insights into waste production and disposal practices).
  • Tracking safety incidents to prevent their recurrence.
  • Predictive analytics (e.g., for test utilization, staffing needs).
  • What-if models for efficient operational planning (e.g., simulation of staff workload and responsibility allocation models).
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Quality control analytics

  • Monitoring quality control samples (e.g., mean and standard deviation of QC results, out-of-control events).
  • Sample error segmentation by time, type, patient location, and other parameters to identify error root causes (e.g., improper equipment calibration).
  • Benchmarking test results against internal and external quality control standards.
  • Insights into sample processing efficiency (e.g., how sample volumes and types, test complexity, and equipment availability affect turnaround time).
  • At-risk sample alerts.
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Diagnostic analytics

E.g., for clinical, public health labs

  • Monitoring clinical KPIs (e.g., clinical correlation rates, interpretation rates).
  • Automated test result interpretation (e.g., hematology analytics, microbiology analytics, genetics analytics).
  • Benchmarking test results against the established parameters (e.g., individual results vs. the established reference intervals).
  • Identifying patterns in historical data (e.g., age- and sex-specific diagnostics thresholds, indicators of flu outbreaks in epidemiological data).
  • Matching patient-specific test results with relevant medical history data (e.g., medications intake, medical images) for more accurate diagnosing.
  • Medical image analysis (e.g., histopathology slides, IHC images, X-rays, CT scans).
  • Predictive analytics (e.g., to forecast disease progression based on test results and demographics data).
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Research analytics

E.g., for clinical trial and R&D labs, CROs

  • Choosing patients by the required criteria (e.g., demographics, disease characteristics) to assist study sponsors in determining patient eligibility for clinical trials.
  • Preclinical data analytics to evaluate safety and efficacy in animal models before advancing to human trials.
  • Tracking trial parameters (e.g., pharmacokinetic metrics like Cmax and Tmax).
  • Comparing results across different groups (e.g., high IP dosage vs. low dosage, medication vs. placebo).
  • Patterns identification (e.g., dose-response relationships).
  • Alerts on adverse events and root cause detection.
  • Predictive analytics (e.g., to forecast side effects).
  • What-if simulations (e.g., for ADME modeling).
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Laboratory inventory analytics

  • Real-time tracking of inventory levels with automated reordering and shortage alerts.
  • Tracking expiration dates of reagents and consumables with notifications on soon-to-expire items.
  • Identifying inventory management bottlenecks and their root causes (e.g., protocol inefficiencies that lead to reagent waste).
  • Comparing suppliers by performance and other required factors.
  • Inventory demand forecasting.
  • Scenario modeling (e.g., to estimate inventory needs in case of certain protocol changes).
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  • Real-time tracking of delivered sample location and condition (e.g., temperature, humidity) with alerts on delays and state changes.
  • Insights into package quality (e.g., tracking contamination rates during transportation).
  • Monitoring transportation KPIs (e.g., on-time performance, delivery costs, fuel efficiency rates).
  • Real-time suggestions on optimal delivery routes and locations (e.g., pinpointing the most suitable lab in terms of distance and required testing equipment).
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  • Tracking financial KPIs (e.g., revenue and cost per test, cash flow from operations, ROI).
  • Cost segmentation (e.g., by category, time period) to identify cost drivers.
  • Benchmarking financial performance against historical results and industry peers.
  • Profitability analysis (e.g., break-even analysis, profitability analysis for different service lines and test types).
  • Budget variance analysis.
  • Grant and fund management analytics (e.g., funding score tracking, grant budget monitoring).
  • Forecasting and financial modeling (e.g., to predict revenue; to perform sensitivity analysis under different variables like changes in supply costs or test volume).
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  • Monitoring customer KPIs (e.g., customer acquisition cost, customer lifetime value, churn rate).
  • Customer segmentation (e.g., by organization type and location for B2B customers like healthcare providers and pharmaceutical companies; by demographics and disease type for B2C customers).
  • Customer journey mapping.
  • Identifying cross-selling and upselling opportunities.
  • Satisfaction analytics based on data from surveys, feedback, and reviews.
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Workforce analytics

  • Tracking employee performance metrics (e.g., error rates, turnaround time, output volume).
  • Comparative analytics (e.g., employee performance vs. laboratory goals, performance vs. compensation).
  • Recommendations on employee-specific workload optimization.
  • Analyzing the effectiveness of recruitment channels and strategies.
  • Employee satisfaction and attrition analytics.
  • Identifying skill gaps and suggesting relevant training programs.
  • Analyzing the effectiveness of practices for promoting inclusion and diversity.
  • Forecasting staffing requirements based on current projects, trends, and seasonal variations.
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Data visualization and reporting

  • User-friendly dashboards with clear visuals and capabilities for drilling up and down, slicing and dicing.
  • Scheduled and ad hoc reports creation with automated submission to the relevant parties.
  • Dashboards adapted to role-specific needs (e.g., lab managers, researchers).
  • Automated creation of reports for regulatory bodies in accordance with the relevant guidelines (e.g., quality control, synoptic, environmental, safety reports).
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Planning to Implement These or Other Analytics Capabilities?

Our data analytics consultants are ready to offer tailored strategies and techs to implement or improve an analytics solution for your laboratory. Just describe your needs briefly, and our experts will get in touch.

Essential System Integrations for Lab Analytics

According to the 2024 State of the Industry report by Medical Laboratory Observer, 65% of labs utilize laboratory information management systems (LIMSs) that are integrated with other enterprise software (e.g., EHR/EMR or medical billing software), while 33% use standalone LIMSs. 78% of respondents integrate their LIMSs with an analytics solution.

Below, ScienceSoft’s consultants provide a sample integration pattern that can be used in most labs, where a LIMS/LIS consolidates data from enterprise systems and serves as a major data source for a laboratory analytics solution.

Integrations for lab analytics

  • EHR or EMR + PACS — to get insights for context-driven interpretation of test results; to identify patterns specific to certain patient groups and support clinical decision-making.
  • Laboratory inventory management software — to optimize inventory levels, avoid over/under-stocking, and prevent reagent and consumables waste; to forecast inventory needs; to optimize logistics operations.
  • ERP or financial management software (e.g., medical lab billing software, revenue cycle management software) – to support informed budget planning; to find cost-optimization opportunities.
  • Laboratory information management system (LIMS) or laboratory information system — to get insights for optimizing testing protocols and increasing sample processing efficiency; to balance personnel workload; to forecast staffing needs and analyze employee efficiency.
  • Asset management software — to enable predictive equipment maintenance.

Lab Analytics Development: Key Steps and Industry Best Practices

Laboratory analytics development is the process of delivering tailored or highly specific software capabilities that are not offered by OOTB solutions (e.g., role-centric data views, integrations with custom and legacy systems, ML/AI-powered features). Drawing on 35 years of experience in implementing custom analytics solutions, ScienceSoft summarizes the key steps and best practices to develop analytics for labs. 

1.

Business analysis and requirement engineering

At this stage, business analysts conduct interviews with lab managers, clinical staff, IT team representatives, and other stakeholders to elicit the organization’s business goals and understand data- and business-related processes (e.g., types of data and data sources; operational specifics). This information is then translated into business and software requirements. The business analyst and a compliance consultant or an SME on the laboratory’s side determine the applicable regulations and compliance requirements for the solution-to-be (e.g., HIPAA, GDPR, FDA regulations, ISO 15189).

ScienceSoft

ScienceSoft

2.

Technical design

At this step, a solution architect decides on the integrations, architecture components, and techs to support lab data analysis.

Depending on the case, the architect may choose one of the available platforms to optimize development time and costs. For example, Azure Synapse Analytics features a flexible integration tool (Azure Data Factory), which makes the platform a good choice for labs that need to integrate data from multiple disparate sources (e.g., when performing clinical trials). Labs that conduct genomic research are likely to benefit from Amazon Redshift due to its columnar storage and advanced compression mechanisms. It offers an optimal cost-to-benefit ratio for storing vast data sets and processing complex analytics queries.

Working with healthcare data, we pay special attention to building data processing pipelines that ensure standardized representation of different nomenclatures in accordance with the required coding systems like SNOMED CT, LOINC, ICD-10, CPT, CDT. Consistent names and codes result in more accurate analysis and dependable insights. This is especially important for labs as the same test can have different names depending on the system it arrives from (e.g., variations such as CBC vs. complete blood count vs. hemogram).

Head of Data Analytics Department, ScienceSoft

3.

UX/UI design

UX/UI designers create user personas and user journeys to tailor the UI according to specific user roles. For example, researchers and lab technicians may need dashboards with clickable visuals and multi-parameter data segmentation. On the other hand, lab administrators are likely to need more static interfaces, e.g., templates for creating weekly performance reports.

When implementing an analytics solution, user adoption often turns out to be a major challenge. To facilitate the user adoption of a new solution, we build convenient UIs that ensure a short learning curve and smooth navigation. For example, we study the interface of software the client already uses and replicate elements like colors, widgets, and tools in the new solution.

ScienceSoft

ScienceSoft

4.

Development, QA, and deployment

At ScienceSoft, we typically recommend conducting testing in parallel with production to ensure efficient collaboration between development and QA teams and prevent issues at early development stages. We also advise implementing best practices for optimizing development time and costs. That includes the implementation of CI/CD pipelines and DevOps practices, utilizing ready-made third-party components (e.g., for data integration, workflow automation), and going for feasible QA automation. According to our experience, this approach helps cut development costs by up to 78%.

ScienceSoft

ScienceSoft

What makes ScienceSoft different

We achieve project success no matter what

ScienceSoft does not pass off mere project administration for project management, which, unfortunately, often happens on the market. We practice real project management, achieving project success for our clients no matter what.

See how we do that

How Much Does It Cost to Develop a Laboratory Analytics Solution?

The cost of laboratory analytics development may range from $30,000 to $500,000. The major cost factors include the range of operations a lab wants to analyze (e.g., inventory management and pathology will require dedicated data sources and data models) and the complexity of analytics features (e.g., rule-based or ML/AI-powered forecasts, real-time analytics driven by big data).

Basic solution

Medium-complexity solution

Advanced solution

The number of business areas to be covered by analytics
?

For example, pathology, inventory management, finance, and quality assurance.

1 business area

2–3 business areas

4+ business areas

Integration complexity
?

More data sources lead to greater data format diversity and additional data standardization efforts. Legacy and custom-built systems may require custom API development.

Integration with 1–2 core systems (e.g., a standalone LIMS/LIS, inventory management software).

Up to 7 data sources, including several LIMS/LIS.

Integration with all the required internal and external systems, including same-type systems of different organization divisions.

Data complexity

Structured (e.g., relational databases, CSV, Parquet files).

Structured and semi-structured (e.g., XML, JSON, ORC files).

Structured, semi-structured, and unstructured (e.g., DICOM, PDF, HTML files).

Data processing frequency

Batch (e.g., every 24 hours).

Batch and real-time.

Batch and real-time.

Complexity of analytics features

KPI calculation, data segmentation, rule-based forecasting (e.g., forecasting sample processing time based on the previously calculated average time).

  • ML/AI-powered forecasting based on historical and real-time data (e.g., predicting sample processing time using multiple factors like test type and staff workload).
  • What-if modeling (e.g., to simulate workload scenarios).
  • Predictive maintenance and usage optimization for lab equipment.
  • Intelligent recommendations (e.g., on optimal delivery routes).
  • AI-powered test analysis (e.g., hematology analytics, microbiology analytics) and medical image analysis.
Reporting and visualization

Via market-available tools like Power BI, Tableau, Looker.

Via market-available tools like Power BI, Tableau, Looker.

  • Via market-available tools like Power BI, Tableau, Looker.
  • Custom complex visuals, e.g., Sankey diagram to illustrate the flow of sample processing from collection to reporting.
Costs

$30,000 – $70,000

$70,000 – $150,000

$150,000 – $500,000

Get a Ballpark Cost Estimate for Your Case

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Achieve Cost-Optimized Operations and Boost Profitability With Data-Driven Insights

Since 1989 in data analytics and 150+ successful healthcare IT projects, ScienceSoft has the necessary expertise to build an efficient lab analytics solution. Holding ISO 9001- and ISO 27001 certifications, we can guarantee top software quality and complete security of your data.