The Definitive Legal Practitioner’s Handbook for Automated Medical Chronologies

Medical chronology creation has evolved from a time-intensive manual process into a streamlined, AI-powered workflow that delivers measurable value to legal practitioners. A medical chronology is a date-ordered summary of a patient’s medical history, highlighting key events such as diagnoses, treatments, procedures, and healthcare provider notes to support legal case preparation. For attorneys, paralegals, and claims professionals handling personal injury, medical malpractice, workers’ compensation, and insurance matters, automated chronology tools now reduce review time by up to 50% while improving accuracy to over 98%. This handbook provides a comprehensive roadmap for legal teams seeking to leverage automation for faster case preparation, stronger legal arguments, and defensible documentation.

Understanding Medical Chronologies in Legal Practice

Medical chronologies serve as the backbone of medical-legal case preparation, transforming scattered health records into coherent timelines that reveal patterns, gaps, and causation. These structured summaries organize hospital admissions, progress notes, physician observations, laboratory results, imaging studies, and treatment records into a single, navigable document. Each entry typically links back to its original source, enabling rapid verification and supporting the legal narrative with auditable evidence.

In personal injury litigation, a well-constructed chronology establishes the sequence of events from incident to injury to treatment, making it easier to demonstrate causation and quantify damages. Medical malpractice cases rely on chronologies to identify deviations from the standard of care, missed diagnoses, or delayed interventions. Workers’ compensation claims use these timelines to connect workplace incidents with subsequent medical treatment and disability. Insurance professionals depend on chronologies to assess claim validity, identify pre-existing conditions, and support settlement negotiations.

Properly organized medical chronologies can reduce manual organization time by 50%, freeing practitioners to focus on legal strategy instead of paperwork. Rather than spending days sorting through hundreds or thousands of pages, legal teams using automated tools can quickly locate critical events, spot inconsistencies, and prepare for depositions or trial. The chronology becomes a shared reference point for attorneys, medical experts, and opposing counsel, streamlining discovery and facilitating more informed settlement discussions.

Key elements typically included in comprehensive medical chronologies encompass hospital admissions and discharge summaries, emergency department visits, physician office notes, surgical reports, diagnostic imaging interpretations, laboratory test results, medication records, therapy session notes, and specialist consultations. Each entry should include the date, provider, facility, clinical finding or intervention, and a direct reference to the source document. This level of detail transforms the chronology from a simple list into a powerful analytical tool that supports every phase of case development.

Key Benefits of Automated Medical Chronologies

Automation through artificial intelligence transforms medical chronology creation from a labor-intensive bottleneck into a competitive advantage for legal teams. The shift from manual review to AI-powered processing delivers measurable improvements across multiple dimensions:

• Time savings progress from days or weeks to minutes, enabling legal teams to handle higher case volumes without proportional staffing increases

• Cost reductions lower the expense of manual labor, with automation typically halving the hours required for chronology assembly

• Enhanced accuracy reaches 98% or higher in date and event identification, surpassing typical manual review rates

• Scalable workflows accommodate growing caseloads without degrading quality or extending turnaround times

One health-claims firm documented review time reductions of approximately 40% and accuracy rates reaching 99.5% after implementing AI-powered chronology tools. These improvements translate directly into better case outcomes, as AI-generated chronologies systematically highlight causation links, standard of care breaches, and missed clinical interventions that might escape notice during rushed manual reviews.

Automated medical chronology is the process of using AI-powered software to extract, organize, and verify clinical events from health records, delivering an audit-ready timeline linked to original documentation. This definition captures the essential transformation: from unstructured medical records to structured legal intelligence. The automation handles the tedious work of date extraction, event classification, and chronological ordering, while legal professionals apply their expertise to interpretation, strategy, and case theory development.

The impact extends beyond individual cases to firm-wide operations. Standardized chronology formats improve collaboration among attorneys, paralegals, and medical experts. Faster turnaround supports earlier case evaluation and more strategic settlement timing. Reduced manual effort allows paralegals to focus on higher-value tasks like witness preparation and discovery coordination. For insurance carriers and third-party administrators, automation enables consistent claim evaluation across large portfolios, supporting more accurate reserving and fraud detection.

Step-by-Step Guide to Generating Medical Chronologies from Records

Creating defensible, comprehensive medical chronologies through automation requires a systematic approach that balances technology capabilities with human oversight. This sequential roadmap guides legal teams through each phase, from initial record collection to final export.

Gathering and Organizing Medical Records

Comprehensive record collection forms the foundation of accurate chronology generation. Legal teams should obtain all relevant medical documentation, including hospital records, electronic medical records from physician practices, scanned PDF documents, handwritten clinical notes, laboratory reports, radiology studies, physical therapy records, and pharmacy medication histories. Incomplete record sets produce incomplete chronologies, potentially missing critical events that support or undermine case theories.

Arranging documents in strict chronological order before processing improves both manual and AI-driven reviews. Cross-referencing collected records against claim events, treatment timelines, and known provider visits helps identify gaps early. Using checklists to verify document completeness before upload prevents the need for later reprocessing and ensures that the resulting chronology represents the full clinical picture. This preparatory work pays dividends throughout the case lifecycle, as attorneys can rely on the chronology’s completeness when preparing for depositions, expert consultations, and trial.

Selecting and Using Automated Tools

Choosing the right automated chronology platform requires evaluating several critical dimensions. HIPAA compliance and SOC 2 certification ensure that the platform meets healthcare data security standards. Auditability features provide the documentation trail needed for court admissibility. PDF and EMR compatibility determine whether the platform can process the diverse file formats typical in medical-legal cases. The ability to link chronology entries to source documents enables rapid verification and supports cross-examination preparation.

Natural language processing interprets clinical terminology, abbreviations, and context to extract meaningful events from narrative notes. Optical character recognition is software that converts scanned or handwritten document images into searchable text, enabling automation even with older or paper-based records. NLP is AI technology that interprets clinical language to extract relevant medical events, dates, and provider information. Together, these technologies enable comprehensive processing of diverse record types.

Platforms built specifically for legal and medical purposes with proper certifications offer significant advantages over general AI tools. Generic AI systems lack the security controls, auditability features, and regulatory compliance frameworks essential for handling protected health information in legal contexts. Legal-grade platforms provide signed business associate agreements, encrypted data handling, role-based access controls, and audit logs that document every action taken with sensitive medical information.

Uploading and Processing Records with AI

Modern AI platforms streamline the ingestion and processing of complex medical record sets. Legal teams upload complete collections of scanned documents, electronic medical records, and handwritten notes into the chosen platform. AI processing engines then build organized chronologies in minutes rather than the days or weeks required for manual review.

The automated workflow progresses through distinct stages:

Step	Example Action
Ingest	Upload PDFs/EMRs
Parse	AI extracts events, dates, diagnoses
Chronologize	Events placed in strict date order
Link	Timeline entries connect to source documents

Advanced platforms like InQuery, MedChron, and LawPro.ai offer side-by-side views that display chronology summaries alongside original source documents. This dual-pane interface enables immediate verification, allowing reviewers to confirm that extracted information accurately reflects the underlying medical record. The combination of automated extraction and instant verification dramatically accelerates the review process while maintaining the accuracy standards required for legal proceedings.

Reviewing and Finalizing the Chronology

Human oversight remains essential for defensible, ready-to-use chronologies. The review process should follow a systematic approach: examine the AI-generated output for missing events or misattributed data, edit entries to correct any inaccuracies, confirm that all key clinical events relevant to the legal issues are captured, and utilize platform verification tools to check source document links. This human-in-the-loop approach blends the speed of automation with the judgment and contextual understanding that only experienced legal professionals can provide.

Reviewers should pay particular attention to dates near critical case events, ensuring that the chronology captures pre-existing conditions, the incident or alleged malpractice, immediate post-incident treatment, and subsequent complications or improvements. Adding custom notes, legal observations, or flags for follow-up questions enhances the chronology’s utility for case strategy discussions and expert consultations. The goal is not simply to verify the AI’s work but to transform the automated output into a strategic document that advances the legal team’s case theory.

Exporting and Integrating for Legal Use

Once finalized, chronologies must be formatted and exported in ways that support legal workflows. Most platforms offer multiple export formats including PDF for court filings and client communications, Excel for data analysis and custom sorting, and Word for editing and annotation. Professional chronology reports maintain hyperlinks connecting each timeline entry to its source document, supporting both internal review and external audit.

Integration with the firm’s document management system or case management platform ensures that chronologies remain accessible to all team members and automatically update as new records arrive. This centralized approach prevents version control issues and ensures that everyone works from the same factual foundation. Export options should preserve formatting, maintain source links, and include any custom notes or observations added during review. The final product serves attorneys during settlement negotiations, supports expert witness preparation, and provides a clear roadmap for trial presentation.

Overview of Automated Medical Chronology Tools and Platforms

The market for automated medical chronology solutions has matured significantly, offering legal professionals multiple platforms with varying feature sets, pricing models, and integration capabilities. Understanding the landscape helps legal teams select tools aligned with their specific needs, case volumes, and security requirements.

Leading platforms share common core capabilities: AI-powered event extraction, chronological organization, source document linking, and export functionality. However, they differ in processing speed, accuracy rates, user interface design, security certifications, and pricing structures. Enterprise-grade security, seamless integration with existing legal technology stacks, comprehensive auditability, and responsive technical support represent essential requirements for compliance-adjacent legal and insurance users.

InQuery: AI-Powered Legal Medical Chronologies

InQuery distinguishes itself as a purpose-built platform for legal and insurance organizations requiring scalable, secure, and fully auditable medical chronology workflows. The platform combines AI-driven timeline generation with human-in-the-loop verification, ensuring that automation speed never compromises accuracy or defensibility. Every chronology entry automatically links to its source document, supporting rapid verification and creating the audit trail essential for court admissibility.

InQuery’s architecture emphasizes measurable ROI through documented turnaround speed improvements and cost reductions. The platform maintains strict adherence to HIPAA, SOC 2, and other regulatory frameworks governing protected health information. Enterprise customers benefit from dedicated implementation support, custom workflow configuration, and integration capabilities that connect InQuery with existing document management and case management systems. Legal teams seeking to transform medical chronology creation from a bottleneck into a competitive advantage can book a demo or start a free trial to experience the platform’s capabilities firsthand.

MedChron by Filevine

MedChron represents Filevine’s entry into AI-powered chronology generation, offering legal teams an integrated solution within the broader Filevine case management ecosystem. The platform excels at classifying diverse document types, parsing clinical content, and hyperlinking every extracted event to its corresponding source document. This source-cited approach accelerates review while maintaining the documentation trail required for legal defense.

MedChron processes typed documents, handwritten notes, and scanned images with equal facility, leveraging advanced OCR and NLP technologies. Outputs include editable summaries that legal teams can customize with annotations, legal observations, and strategic notes. The platform supports Bates numbering and maintains comprehensive audit trails documenting every action taken during chronology creation. Integration with Filevine’s case management features enables seamless workflow from record receipt through chronology generation to case strategy development.

LawPro.ai

LawPro.ai focuses on delivering citation-backed, real-time chronologies that update automatically as new medical records arrive. The platform’s event extraction engine identifies dates, diagnoses, procedures, treating providers, and clinical findings using machine learning models trained on legal-medical datasets. This specialization enables nuanced understanding of clinical terminology and accurate classification of events by legal relevance.

Real-time updating capabilities prove particularly valuable in ongoing treatment scenarios common to personal injury and workers’ compensation cases. As new records arrive, LawPro.ai automatically integrates them into the existing chronology, maintaining chronological order and updating summary statistics. Export-ready reports include professional formatting, comprehensive source citations, and customizable templates that match firm branding and preferred chronology structures.

EvenUp

EvenUp takes a guided approach that blends automated assembly with structured human review. The platform provides templates for common case types, step-by-step workflows that walk reviewers through verification processes, and inline validation tools that flag potential discrepancies for human attention. This scaffolded approach helps newer paralegals and legal assistants produce high-quality chronologies while learning medical-legal review skills.

The platform’s template library covers personal injury, medical malpractice, workers’ compensation, and other common practice areas. Each template defines the types of events most relevant to that case type, ensuring that chronologies capture legally significant information while filtering routine or irrelevant clinical notes. Inline validation compares extracted dates and events against expected patterns, alerting reviewers to potential OCR errors, date inconsistencies, or missing documentation.

Additional Notable Solutions

InPractice AI offers immediate duplicate detection, predictive analytics that identify potential case issues, and cloud-based delivery that enables remote team collaboration. The platform’s duplicate detection prevents chronologies from becoming cluttered with repeated entries from multiple record sources, while predictive analytics highlight patterns that may indicate causation, pre-existing conditions, or treatment gaps.

Supio emphasizes high-volume processing capabilities designed for insurance carriers and third-party administrators managing large claim portfolios. The platform’s batch processing features enable simultaneous chronology generation for dozens or hundreds of cases, with automated quality checks and standardized output formats that support consistent claim evaluation across portfolios. Both platforms maintain enterprise-level security and compliance certifications essential for legal-industry adoption.

Comparing AI Medical Timeline Platforms

Informed platform selection requires systematic evaluation across dimensions that directly impact legal workflow efficiency, case quality, and regulatory compliance. Legal and insurance organizations should assess candidates through the lens of their specific operational needs, case volumes, and risk tolerance.

Processing Speed and Efficiency

Processing speed directly impacts case throughput and attorney productivity. Leading AI platforms generate initial chronologies in minutes even for complex cases involving thousands of pages of medical records. One legal team documented the ability to build comprehensive chronologies in under 30 minutes compared to the 80-plus hours previously required for manual review. This 90%+ time reduction enables the same staff to handle significantly larger caseloads or redirect saved hours toward higher-value legal analysis.

Typical time-to-output varies by platform and case complexity:

Method	Typical Time
Manual	8–80+ hours
AI Platform	5–30 minutes

Speed advantages compound across high-volume practices. A firm handling 50 new medical-legal cases monthly saves approximately 400 hours of paralegal time monthly by automating chronology creation. These savings translate directly to reduced labor costs, faster case evaluation, and earlier identification of settlement opportunities or case weaknesses.

Accuracy and Quality Control

High-fidelity extraction and organization represent non-negotiable requirements for legal applications. Modern AI platforms achieve over 98% accuracy in date extraction and event identification, exceeding typical manual review accuracy rates. Some platforms document overall accuracy rates reaching 99.5% when combining AI extraction with human verification workflows.

Accuracy alone proves insufficient without defensibility. Legal-grade platforms maintain comprehensive audit trails linking each chronology entry to its source document. This linkage enables rapid verification during internal review, supports responses to opposing counsel’s challenges, and provides the documentation foundation for court admissibility. Quality control features should include side-by-side source document viewing, automated consistency checks that flag date conflicts or missing information, version control tracking all edits and annotations, and export options that preserve source citations in final deliverables.

Security and Compliance

Platforms handling protected health information in legal contexts must meet stringent security and regulatory standards. HIPAA compliance ensures that the platform implements required safeguards for electronic protected health information, including encryption, access controls, and breach notification procedures. HIPAA, the Health Insurance Portability and Accountability Act, establishes federal standards for protecting patient health information privacy and security, requiring covered entities and their business associates to implement administrative, physical, and technical safeguards.

SOC 2 certification demonstrates that the platform has undergone independent audit of its security controls, availability, processing integrity, confidentiality, and privacy practices. SOC 2, developed by the American Institute of CPAs, provides a framework for evaluating service organizations’ information security practices and controls, with Type II reports documenting the effectiveness of those controls over time.

Legal teams should never use generic AI tools lacking these certifications for medical chronology creation. Platforms lacking proper security controls and compliance frameworks present significant risks. Vendor selection should prioritize platforms like InQuery that offer signed business associate agreements, end-to-end encryption for data in transit and at rest, role-based access controls limiting data exposure, comprehensive activity logging for audit purposes, and regular third-party security assessments. Learn more about what it takes to build a secure, HIPAA-compliant system.

Cost Structures and Value

Automated chronology platforms employ various pricing models including monthly per-user subscriptions, tiered enterprise licenses based on case volume, and pay-per-case or pay-per-page usage fees. Subscription models typically range from $100 to $500 per user monthly, with enterprise pricing negotiated based on firm size and expected usage. Per-case pricing varies from $50 to $300 depending on record volume and complexity.

Cost evaluation should extend beyond subscription fees to encompass total cost of ownership. Automation typically reduces manual chronology costs by 50% or more when accounting for paralegal time savings. A firm paying $200 monthly per user for chronology software but saving 20 paralegal hours monthly realizes net savings of approximately $800 monthly per user, assuming a $50 hourly paralegal rate. Additional value derives from faster case evaluation, earlier settlement opportunities, and improved case outcomes resulting from more comprehensive chronologies. View InQuery’s pricing for transparent cost structures.

Hidden costs or savings emerge from support quality, training requirements, integration complexity, and compliance features. Platforms offering comprehensive onboarding, responsive technical support, and pre-built integrations reduce implementation time and ongoing administrative burden. Robust compliance features prevent costly data breaches and regulatory violations that could dwarf software subscription costs.

Integration and Features

Platform capabilities beyond core chronology generation significantly impact workflow efficiency and user adoption. Mid-to-large legal teams should prioritize platforms offering direct document management system integration enabling automatic record import and chronology storage, case management system connectivity for seamless workflow across legal tools, editable timelines supporting custom annotations and legal observations, professional-grade export formats maintaining source citations and formatting, and collaborative features enabling multiple reviewers and expert consultations.

API availability enables custom integrations with proprietary systems or specialized legal technology tools. Advanced features like predictive analytics, duplicate detection, and automated quality checks add value for high-volume practices. User interface design impacts adoption rates and training requirements, with intuitive platforms reducing onboarding time and support needs.

Cost Considerations for Medical Chronology Software

Understanding the full economic impact of automated chronology software requires examining both direct costs and operational savings. Pricing structures vary significantly across vendors, making systematic evaluation essential for informed decision-making.

Typical software pricing follows several common models. Monthly per-user subscriptions charge a recurring fee for each attorney or paralegal accessing the platform, usually ranging from $100 to $500 monthly depending on feature sets and support levels. Enterprise tiered pricing offers volume discounts for larger firms, with annual contracts typically reducing per-user costs by 10% to 20%. Pay-per-case or pay-per-record models charge based on actual usage, with fees varying from $50 to $300 per case depending on record volume and complexity.

Cost efficiency analysis should compare software expenses against traditional manual chronology costs. Automation reduces manual chronology assembly time by up to 50% for many firms, translating directly to labor cost savings. A paralegal spending 20 hours monthly on chronology creation at $50 hourly represents $1,000 in monthly labor cost. Automation reducing that to 10 hours saves $500 monthly, easily justifying software costs of $200 to $300 monthly while still delivering net savings.

Hidden costs emerge from implementation, training, technical support, and integration work. Platforms requiring extensive customization or lacking pre-built integrations increase IT support costs. Conversely, platforms offering comprehensive onboarding, intuitive interfaces, and responsive customer support reduce ongoing administrative burden. Compliance features preventing data breaches avoid potentially catastrophic costs from regulatory fines, notification requirements, and reputational damage.

Long-term value extends beyond direct cost savings to strategic advantages. Faster case evaluation enables earlier settlement discussions when appropriate, reducing overall case costs. More comprehensive chronologies support stronger legal arguments and better expert witness preparation. Standardized workflows improve consistency across cases and team members, reducing quality variations and supporting scalability as practices grow.

Choosing Between Medical Chronology Services and Software Platforms

Legal and insurance professionals face a fundamental choice between outsourced chronology services and self-managed software platforms. Understanding the trade-offs helps organizations select the approach best aligned with their case volumes, timelines, and operational preferences.

Medical chronology services employ external consultants or specialized vendors who build chronologies manually or with partial automation. These services receive medical records from the legal team, assign trained reviewers to extract and organize relevant events, and deliver completed chronologies according to agreed timelines. Software platforms provide in-house, AI-driven, self-service tools that legal teams operate directly, maintaining full control over the chronology creation process.

Services offer several advantages: minimal setup requirements with no software installation or training, expert validation from reviewers experienced in medical-legal documentation, and flexibility to handle occasional cases without ongoing subscription costs. However, services typically involve slower turnaround measured in days or weeks rather than minutes, higher per-case costs ranging from $300 to $1,000 or more, limited control over review priorities and timeline, and dependency on external vendor availability and quality.

Software platforms provide contrasting benefits: dramatically faster processing generating chronologies in minutes, scalable workflows handling growing caseloads without proportional cost increases, complete control over review timing and priorities, and lower per-case costs after accounting for subscription fees. Trade-offs include upfront investment in software selection and implementation, staff training requirements to achieve proficiency, and ongoing responsibility for quality assurance and verification.

For larger firms and insurance organizations seeking efficiency, compliance, and auditability at scale, AI platforms represent the superior choice. The combination of speed, cost-effectiveness, and control enables these organizations to transform medical chronology creation from an outsourced commodity service into a strategic capability supporting faster case resolution and better outcomes. Smaller firms with occasional medical-legal cases may find services more practical, avoiding the overhead of software licensing and training for limited usage.

Best Practices for Organizing Medical Records Chronologically

Preparing medical records properly before automated processing maximizes accuracy and minimizes post-processing cleanup. Systematic organization transforms raw documentation into structured inputs that AI platforms can process efficiently.

Legal teams should follow a methodical preparation workflow:

• Sort all records by date or event type, grouping hospital admissions, physician visits, diagnostic tests, and treatment notes into logical categories

• Standardize file naming using consistent conventions such as Bates numbers, patient name, provider, and date to enable quick identification and retrieval

• Eliminate duplicate records before upload, as duplicates clutter chronologies and complicate review without adding information

• Document a cross-reference table mapping case events to expected medical records, identifying gaps requiring additional record requests

• Verify document completeness by checking that all known providers, facilities, and treatment dates have corresponding documentation

• Ensure document quality by confirming that scanned records are legible, properly oriented, and complete without missing pages

Well-organized input produces cleaner, more accurate automated outputs. AI platforms processing systematically organized records generate fewer errors, require less post-processing correction, and deliver chronologies that legal teams can use immediately. The time invested in preparation pays dividends throughout the case lifecycle, as organized records support not only chronology creation but also discovery responses, expert consultations, and trial preparation.

Additional best practices include maintaining original records in unmodified form, creating working copies for annotation and review, tracking record sources and receipt dates for audit purposes, and documenting any known gaps or missing information. These practices ensure that chronologies rest on a defensible foundation of complete, authentic medical documentation. Missing records cost the industry $2B annually—learn how to identify and resolve gaps in medical documentation.

How Long Does It Take to Create a Medical Chronology with AI?

Processing time represents a primary concern for legal teams evaluating automation options. Modern AI platforms generate initial chronologies in minutes even for complex cases involving extensive medical records. A case with 500 pages of documentation might require 10 to 20 minutes for complete processing, while simpler cases with 100 pages may finish in 5 minutes or less.

The contrast with manual methods proves dramatic:

Method	Typical Time
Manual	8–80+ hours
AI Platform	5–30 minutes

These timeframes reflect initial chronology generation. Review and finalization require additional time proportional to case complexity and the legal team’s quality standards. A straightforward personal injury case might need 30 to 60 minutes of review to verify accuracy and add legal annotations. Complex medical malpractice cases involving multiple specialties, extended treatment periods, and detailed standard-of-care analysis may require several hours of attorney and paralegal review even after AI processing.

Total turnaround from record receipt to finalized chronology typically ranges from same-day completion for urgent matters to two to three business days for complex cases requiring thorough review. This represents a substantial improvement over the one to two weeks often required for manual chronology creation or outsourced services. Faster turnaround enables earlier case evaluation, more timely settlement discussions, and better resource allocation across the firm’s caseload.

Ready to see how AI can transform your medical chronology workflow? Schedule a demo to process up to 1,000 pages free and experience automated chronology generation firsthand.

Frequently Asked Questions

What is a medical chronology and why is it important in legal cases?

A medical chronology is a timeline of medical events organized by date, providing legal teams with a clear, structured overview that supports case analysis and strengthens arguments in court.

How does AI improve the accuracy and speed of medical chronology creation?

AI rapidly extracts dates and key events from medical records, organizing them with accuracy that often exceeds 98%, and can reduce review time from days to minutes.

What types of medical records can be used with automated chronology tools?

Automated chronology platforms process scanned PDFs, electronic medical records, handwritten notes, lab results, and imaging files to capture a comprehensive view of medical events.

Are automated medical chronologies admissible and reliable in court?

Chronologies created by AI platforms designed for legal use are admissible and reliable, especially when each entry is linked to its original source document and thoroughly reviewed.

How can legal professionals ensure data security and compliance when using AI tools?

Legal teams should select AI platforms like InQuery that are HIPAA-compliant, SOC 2 certified, and provide encrypted data handling with signed business associate agreements to ensure confidentiality and regulatory compliance.

Where can I get a free medical chronology template?

You can download a free medical chronology template from InQuery, which includes pre-formatted fields for legal case chronologies. For more template options and guidance, see our complete guide to medical chronology templates.