{"id":57825,"date":"2026-08-21T04:54:32","date_gmt":"2026-08-21T04:54:32","guid":{"rendered":"https:\/\/www.bridge-global.com\/blog\/?p=57825"},"modified":"2026-08-24T06:58:23","modified_gmt":"2026-08-24T06:58:23","slug":"a-digital-health-product-engineering","status":"publish","type":"post","link":"https:\/\/www.bridge-global.com\/blog\/a-digital-health-product-engineering\/","title":{"rendered":"Digital Health Product Engineering: A Complete Guide"},"content":{"rendered":"

Your product team has a clinical workflow that works in a pilot, a board asking for a faster release, and a compliance review that keeps uncovering assumptions nobody documented. At the same time, a clinician wants fewer clicks, an integration vendor has changed an endpoint, and the model that looked reliable during validation is producing different results in live conditions.<\/p>\n

That situation is normal in digital health. The difficult work isn't only building an app, connecting an EHR, or adding an AI assistant. Digital health product engineering is the discipline of keeping a health product safe, interoperable, useful, and auditable as its users, integrations, regulations, and models change. The product isn't finished at launch. Launch is where the most demanding part of the lifecycle begins.<\/p>\n

The Pressure Every Healthtech Team Feels Before They Write a Line of Code<\/h2>\n

A founder walks into a product meeting with three urgent requests. Investors want a demonstrable release before the next funding conversation. The security lead wants a credible answer for how protected health information will move through the system. A clinical advisor says the proposed workflow adds too much interruption to an already overloaded consultation.<\/p>\n

None of those requests is unreasonable. The problem starts when the team treats them as separate priorities and asks engineering to \u201cmove fast\u201d before defining the product's clinical purpose, data boundaries, or operational owner.<\/p>\n

A rushed decision at this stage travels further than you expect. A shortcut in identity management becomes a rewrite when enterprise buyers require stronger access controls. A vague clinical requirement becomes a traceability gap when the product enters a regulated pathway. A direct EHR connection built for a single pilot becomes integration debt when the team must support another health system with different terminology, permissions, and workflow conventions.<\/p>\n

\n

Practical rule:<\/strong> In healthtech, the fastest architecture is usually the one that makes future evidence, change control, and integration behavior visible from the beginning.<\/p>\n<\/blockquote>\n

The tension isn't a simple triangle of speed, safety, and scalability. It's a stack. Product discovery supports regulatory classification. Classification influences architecture and verification. Architecture determines how easily the team can capture evidence. Workflow design determines whether clinicians will use the product. Post-market ownership determines whether the product remains trustworthy after deployment.<\/p>\n

That's why digital health product engineering demands a different discipline from general product delivery. A consumer application can often correct a poor assumption through a rapid interface update. A health product may need to assess patient impact, preserve an audit trail, repeat validation, update documentation, and coordinate with clinical stakeholders before making the same change.<\/p>\n

Teams that build with this lifecycle in mind can still move quickly. They move quickly through explicit gates rather than borrowing time from the future.<\/p>\n

What Digital Health Product Engineering Actually Means<\/h2>\n

Digital health product engineering combines product development, clinical context, regulatory controls, security, interoperability, and operational monitoring into one delivery system. It isn't healthtech consulting that ends with recommendations, and it isn't medical IT work limited to configuration or support. The outcome is a shipped product with a defensible architecture, documented behavior, and an operating model for continued change.<\/p>\n

The foundation is conventional software engineering. Teams still need domain-driven design, version control, automated testing, observability, cloud operations, release management, and clear ownership. On top of that foundation sits the regulated healthtech layer, which can include HIPAA, GDPR, medical-device classification, FDA Software as a Medical Device considerations, IEC 62304 practices, and quality-system evidence.<\/p>\n

The third layer is clinical workflow integration. The product must fit how a patient, clinician, administrator, payer, or laboratory worker completes a task. A technically correct interface can still fail if it forces context switching, duplicates documentation, produces poorly timed alerts, or makes the user reconcile conflicting records manually.<\/p>\n

The three stacks engineers must design together<\/h3>\n

The software stack<\/strong> handles reliability, maintainability, performance, deployment, and recovery. It gives the team a stable place to implement product behavior.<\/p>\n

The regulated-product stack<\/strong> defines intended use, risk controls, data handling, verification, validation, change control, and evidence. It determines what the team must prove, not just what it must build.<\/p>\n

The workflow and interoperability stack<\/strong> connects the product to EHRs, laboratories, devices, payer systems, and human routines. HL7 FHIR is described as the dominant modern data exchange standard, while SMART on FHIR supports third-party application access to EHR environments in the digital health frameworks and standards overview<\/a>.<\/p>\n

The hard parts appear at the boundaries. PHI must remain protected across services and logs. Audit trails must explain who did what, when, and under which authorization. FHIR resources must preserve semantic meaning rather than merely satisfy an endpoint contract. AI outputs must be validated, reviewed, and monitored after release.<\/p>\n

The market context reinforces the engineering burden. One estimate valued the global digital health market at USD 347.4 billion in 2025 and projected USD 1,830.4 billion by 2033, with a 23.4% CAGR from 2026 to 2033. Software represented 45.7% of revenue in 2025, according to Grand View Research's digital health market analysis<\/a>. Product engineering therefore isn't a peripheral delivery function. It sits at the center of the value being created.<\/p>\n

\"A<\/figure>\n<\/p>\n

A product team should treat the lifecycle as continuous. Discovery doesn't stop when the backlog is approved, compliance doesn't stop when the product passes a review, and engineering doesn't stop when production traffic begins.<\/p>\n

The End-to-End Lifecycle From Discovery to Post-Market Monitoring<\/h2>\n

A digital health product can pass launch review and still become unsafe later. Clinical workflows change, integrations age, AI models drift, and compliance evidence loses relevance as the system evolves. Treat the lifecycle as eight connected gates. Each gate creates evidence for the next decision, while post-market monitoring tests whether earlier assumptions still hold.<\/p>\n

1. Discovery and clinical problem validation<\/h3>\n

Start with the clinical problem, intended users, affected workflows, and measurable safety concerns. Produce a User Requirements Specification, or URS, that separates user needs from proposed features. Interviews, workflow observation, service blueprints, and prototype reviews should establish whether the product removes work or moves it elsewhere.<\/p>\n

A rushed discovery phase can produce technically impressive software for a problem clinicians do not experience as urgent.<\/p>\n

2. Regulatory classification<\/h3>\n

Define intended purpose before selecting a regulatory route. Classification affects risk controls, quality records, validation depth, labeling, and post-market responsibilities. Product claims must match the evidence plan, especially when the software influences diagnosis, triage, treatment, or clinical decisions.<\/p>\n

An unclear classification creates compliance debt because later artifacts inherit an uncertain product boundary.<\/p>\n

3. Architecture and threat modeling<\/h3>\n

Create the Software Design Specification, threat model, trust boundaries, data-flow diagrams, identity model, and recovery strategy. Decide which services may process PHI, how secrets and encryption keys are managed, how privileged access is approved, and how the platform behaves during a dependency outage.<\/p>\n

The architecture should show how failures are contained and how evidence is collected, not merely present a modern diagram.<\/p>\n

4. FHIR and EHR integration design<\/h3>\n

Map workflows and data semantics before writing connectors. Define resource profiles, terminology mappings, synchronization rules, error handling, consent behavior, and reconciliation procedures. A FHIR endpoint can exchange syntactically valid resources while still failing if the receiving workflow interprets the information differently.<\/p>\n

Specify the degraded mode for EHR outages. Queueing, retry behavior, reconciliation, and user-visible status are safer than silent data loss.<\/p>\n

5. Iterative development with evidence capture<\/h3>\n

Build vertical slices that include the user journey, authorization path, audit events, integration behavior, and test evidence. Maintain a traceability matrix linking requirements to risks, controls, implementation, and verification. Where the quality system requires it, store design decisions, code reviews, test results, and change records in the Design History File.<\/p>\n

A sprint that produces code without evidence can create later work to reconstruct why the code is safe and how it was verified.<\/p>\n

6. Verification and validation<\/h3>\n

Verification asks whether the product was built according to its specifications. Validation asks whether it supports the intended clinical use in its operating context. Test plans should cover functional behavior, security, usability, interoperability, performance, accessibility, abnormal inputs, and recovery.<\/p>\n

A passing unit-test suite cannot show that a clinician can safely interpret an alert or that an EHR mapping preserves the intended meaning.<\/p>\n

7. Controlled deployment<\/h3>\n

Release with feature flags, environment separation, rollback procedures, migration checks, release notes, and an incident pathway. Begin with a bounded deployment and observe workflow behavior before expanding exposure. This approach lets the team identify unsafe assumptions without turning every customer into a test environment.<\/p>\n

Deployment evidence should include what changed, who approved it, which users received it, and how the team would reverse it.<\/p>\n

8. Post-market monitoring<\/h3>\n

Create the post-market plan before launch. Monitor incidents, complaints, integration failures, access anomalies, workflow friction, model performance, and changes in external systems. Assign owners for triage, corrective action, customer communication, evidence updates, and release approval.<\/p>\n

The eight-step roadmap published in PubMed<\/a> connects intended purpose, medical-device classification, regulatory approval, evidence generation, fairness and generalisability, interoperability, information governance, and post-market surveillance. Those concerns apply beyond AI. Post-market work is not maintenance after the lifecycle. It is half of the lifecycle.<\/p>\n

The operating model also needs a practical way to connect discovery, engineering, quality records, deployment, and monitoring. See our guide to healthcare product lifecycle engineering<\/a> for that broader treatment.<\/p>\n

\"A<\/figure>\n<\/p>\n

Compliance, Security, and Interoperability as One Architectural Concern<\/h2>\n

Treating compliance, security, and interoperability as separate workstreams creates gaps between them. A consent decision affects data access. Data access affects API behavior. API behavior affects audit records and downstream interoperability. The architecture must preserve those relationships instead of asking separate teams to reconcile them before launch.<\/p>\n

At the identity layer, use role and attribute-based access controls that reflect the user's organization, care relationship, purpose, and location where appropriate. Break-glass access should be explicit, time-bound, reason-coded, highly visible, and reviewed after the event. The product shouldn't hide emergency access inside a generic administrator role.<\/p>\n

At the API edge, a web application firewall can block known attack patterns before traffic reaches application services. The gateway should also enforce authentication, authorization, rate policies, schema validation, and consistent correlation identifiers. Services still need their own authorization checks because perimeter controls can't understand every clinical decision.<\/p>\n

Within the data layer, encryption at rest and in transit is only the starting point. Key management belongs in a controlled secrets and key-management system, PHI tokenization can reduce exposure in non-clinical processing paths, and consent metadata should travel with the data or remain resolvable wherever the data moves. A downstream analytics job shouldn't receive a record without the policy context needed to determine whether that use is permitted.<\/p>\n

Layered Concerns Where Compliance Security and Interoperability Meet<\/h3>\n\n\n
\n\n\n\n\n\n
Architecture Layer<\/th>\nCompliance Control<\/th>\nSecurity Control<\/th>\nInteroperability Control<\/th>\n<\/tr>\n
Identity and access<\/td>\nPurpose, role, consent, and emergency-access records<\/td>\nMFA, least privilege, ABAC, session controls<\/td>\nSMART on FHIR scopes and organization context<\/td>\n<\/tr>\n
API gateway<\/td>\nRequest traceability and policy enforcement<\/td>\nWAF rules, token validation, throttling, schema checks<\/td>\nFHIR capability negotiation and version handling<\/td>\n<\/tr>\n
Service and domain layer<\/td>\nRisk controls, validation status, change records<\/td>\nService authorization and segmentation<\/td>\nCanonical mappings and workflow-specific resource handling<\/td>\n<\/tr>\n
Data layer<\/td>\nRetention, residency, provenance, and deletion policy<\/td>\nEncryption, key separation, tokenization<\/td>\nFHIR profiles, terminology, identifiers, and consent context<\/td>\n<\/tr>\n
Operations and delivery<\/td>\nAudit evidence, SBOMs, release records, CAPA inputs<\/td>\nMonitoring, alerting, backup, and recovery<\/td>\nIntegration health, message replay, and reconciliation logs<\/td>\n<\/tr>\n<\/table><\/figure>\n\n\n

FHIR design requires its own discipline. Model resources around clinical meaning and workflow use, not only around the fields a vendor currently exposes. Version profiles and terminology mappings deliberately. A national interoperability program in Australia reached 75% of planned actions completed in 2026 after two additional actions were completed, as reported by the Australian Digital Health Agency progress update<\/a>. That progress reflects the direction of the market, where connected systems matter as much as standalone interfaces.<\/p>\n

For practical implementation patterns, the HIPAA-compliant software development guide<\/a> provides a useful companion reference. The key engineering decision is to make logs readable to a reviewer, not merely exportable after an incident. Record actor, action, resource, purpose, authorization context, outcome, and correlation path in a form that supports investigation.<\/p>\n

AI Integration Is a Lifecycle Commitment, Not a Feature<\/h2>\n

A model can be connected in a sprint. A dependable clinical AI capability requires ownership across design, validation, release, monitoring, and retirement.<\/p>\n

Start by defining the decision the system supports. Specify the intended user, permitted output, prohibited use, escalation route, and person accountable for the final decision. Document training-data provenance, inclusion criteria, labeling assumptions, missingness, subgroup behavior, and the gap between retrospective performance and clinical usefulness.<\/p>\n

Historical performance does not guarantee safe behavior after deployment. Documentation patterns, patient populations, devices, and workflows change. A recent benchmark framework found that FHIR Specificity scored 1.3 out of 2.0, while AI Validation scored 0.3 out of 2.0, across 10 representative sources. It also found no prospective external validation and Governance Readiness at or below 1.0 in every category, according to the preprint benchmark framework<\/a>. The engineering implication is direct: FHIR conformance does not make the clinical AI built on top of an interface safe.<\/p>\n

Design the evidence path before the inference path<\/h3>\n

Maintain a model card that records intended use, training data, evaluation conditions, limitations, subgroup results, approval status, and change history. Version inference endpoints and lock them when predictable behavior is required. A feature store can standardize clinical feature definitions, but it also requires lineage, access controls, freshness rules, and validation.<\/p>\n

Before broad release, run the model in shadow mode so it produces outputs without influencing care. Compare those outputs with clinician decisions, investigate failure modes, and set safety thresholds. Canary releases can restrict exposure. Human review queues give clinicians a defined place to inspect uncertain or high-impact cases.<\/p>\n

Prompt-based systems require the same controls. An LLM that drafts a clinical note or summarizes a record needs hallucination logging, source visibility, escalation paths, clinician override telemetry, and a policy for unsupported output. Calling a system \u201cassistive\u201d does not remove its effect on workflow or patient decisions.<\/p>\n

Deployment planning must cover fairness, generalisability, interoperability, information governance, and post-market surveillance, alongside classification and evidence generation. Regulated AI, including software that may fall under FDA SaMD or medical-device frameworks such as EU MDR, carries a more formal evidence burden than an internal productivity assistant. Both need named owners after release.<\/p>\n

\n

Ownership test:<\/strong> If nobody owns drift review, model-card updates, incident triage, and rollback approval, the AI feature is not production-ready.<\/p>\n<\/blockquote>\n

Team and Partner Models That Fit Each Growth Stage<\/h2>\n

The right delivery model depends on product stage, clinical risk, and integration depth. Budget matters, but it shouldn't be the deciding variable. A low-cost team that lacks quality ownership can become expensive once the product enters procurement, validation, or multi-site deployment.<\/p>\n

An in-house squad makes sense when the company has stable product demand, recurring clinical feedback, and enough post-market work to support dedicated quality, security, and platform roles. It offers strong context retention, but hiring a complete regulated-product team too early can consume capital before the product has validated its market.<\/p>\n

A fractional CTO with specialist contractors can help a founder establish direction and reach an early prototype. The model breaks down when no one owns the complete chain from clinical requirements to verification evidence. Staff augmentation has a similar limitation. Engineers add capacity, but the client must provide architecture, domain context, quality management, and delivery coordination.<\/p>\n

A dedicated delivery pod is stronger when the product needs product management, UX, engineering, QA, DevOps, and healthcare integration knowledge working together. A full-service healthtech software development partner<\/a> earns its role when the product has critical compliance requirements, and the client needs one accountable group to absorb architecture, delivery, evidence, and post-market support.<\/p>\n\n\n

\n\n\n\n\n\n
Operating Model<\/th>\nBest Stage<\/th>\nCompliance Accountability<\/th>\nTime to First Shipped Feature<\/th>\nPost-Market Capacity<\/th>\n<\/tr>\n
In-house product squad<\/td>\nEstablished product and repeatable demand<\/td>\nStrong internal ownership<\/td>\nDeliberate, depends on hiring<\/td>\nHigh once the team is staffed<\/td>\n<\/tr>\n
Fractional CTO plus contractors<\/td>\nEarly discovery and technical direction<\/td>\nUsually shared and founder-dependent<\/td>\nFast for narrow prototypes<\/td>\nLimited without retained specialists<\/td>\n<\/tr>\n
Staff augmentation<\/td>\nKnown architecture with temporary capacity gaps<\/td>\nClient-owned<\/td>\nFast when onboarding is simple<\/td>\nVariable and often task-specific<\/td>\n<\/tr>\n
Dedicated delivery pod<\/td>\nMVP through scale-up<\/td>\nShared through explicit quality ownership<\/td>\nFast for integrated vertical slices<\/td>\nStrong if the pod is retained<\/td>\n<\/tr>\n
Full-service regulated partner<\/td>\nHigh-risk product or complex integration program<\/td>\nExplicit partner and client governance<\/td>\nFast after discovery and controls<\/td>\nDesigned into the engagement<\/td>\n<\/tr>\n<\/table><\/figure>\n\n\n

Context switching carries a hidden cost. Engineers may need to translate between clinical stakeholders, FDA consultants, security reviewers, EHR vendors, and commercial buyers. A generalist team can write code, but a domain-aware pod can keep those conversations connected to the product's requirements and evidence.<\/p>\n

For broader delivery choices, compare software development service models<\/a> against the product's actual risk profile. The model should make post-market work someone's responsibility, not an optional extension.<\/p>\n

How to Choose an Engineering Partner Without Regretting It<\/h2>\n

A credible partner can explain difficult decisions made on products already in production. A sales presentation is not evidence. Request a redacted architecture diagram, a sample traceability structure, an example of change control, and references willing to describe the team's conduct during a production incident.<\/p>\n

Score candidates from 1 to 5 against documented evidence, not confidence. Ask every vendor the same questions, then record the assumptions behind each score. Pay particular attention to what happens after launch, when integrations change, models drift, and compliance records need updating.<\/p>\n

Engineering Partner Evaluation Scorecard<\/h3>\n\n\n
\n\n\n\n\n\n
Criterion<\/th>\nWeight<\/th>\nWhat to look for<\/th>\nRed flag<\/th>\n<\/tr>\n
Regulated domain depth<\/td>\nHigh<\/td>\nNamed quality ownership, risk-management practice, relevant delivery evidence<\/td>\nVague references to HIPAA<\/td>\n<\/tr>\n
Architectural ownership<\/td>\nHigh<\/td>\nDelivered diagrams, trade-off records, migration and recovery decisions<\/td>\nThe vendor only supplies developers<\/td>\n<\/tr>\n
AI governance experience<\/td>\nHigh for AI products<\/td>\nModel cards, validation plans, monitoring, rollback, human oversight<\/td>\nAI described only as a feature<\/td>\n<\/tr>\n
Interoperability fluency<\/td>\nHigh for connected products<\/td>\nFHIR profiles, SMART on FHIR, terminology mapping, reconciliation<\/td>\n\u201cWe can integrate with anything\u201d without examples<\/td>\n<\/tr>\n
Post-market support<\/td>\nHigh<\/td>\nIncident response, monitoring, corrective action, release governance<\/td>\nSupport ends at deployment<\/td>\n<\/tr>\n<\/table><\/figure>\n\n\n

Build the RFP around real scenarios. Ask how the team would approach a Class II SaMD product requiring IEC 62304 evidence, an EHR integration using FHIR R4 and SMART on FHIR launch, and an AI feature with a locked model card and recurring drift review. The response should identify discovery work, assumptions, evidence, dependencies, acceptance gates, and the people accountable for each decision. It should also explain how those records will be maintained after release.<\/p>\n

Treat a fixed-price bid cautiously when intended use, classification, integrations, or model behavior remains uncertain. The statement of work should define change control, assumption logs, knowledge-transfer obligations, exit clauses, and ownership of post-market activities. Clarify who handles incidents, integration version changes, model review, corrective actions, and compliance evidence refreshes.<\/p>\n

A partner that challenges unsafe scope and contributes clinical or quality expertise is more useful than one that accepts every requirement without qualification. Ask what the team will refuse to ship, and what evidence would change that decision.<\/p>\n

Use the key questions to evaluate a potential software development partner<\/a> to structure the first evaluation conversation. Then request two references for the criteria most important to your risk profile, rather than collecting generic testimonials.<\/p>\n

Bringing It All Together: Your Engineering Decision<\/h2>\n

Durable digital health products come from a sequence of decisions, not a heroic launch. Start by mapping the current product against the eight lifecycle stages. Mark every gate without a named owner, current artifact, acceptance condition, and escalation path.<\/p>\n

Next, audit every AI capability. Look for a model card, data lineage, baseline evaluation, drift approach, retraining or review policy, rollback procedure, and an accountable clinician or product owner. If those artifacts don’t exist, treat the AI as an unmanaged production risk rather than a finished feature.<\/p>\n

Then run a combined compliance and interoperability review. Cover HIPAA and GDPR obligations, IEC 62304 where applicable, identity and audit controls, FHIR R4 coverage, terminology mappings, consent behavior, and failure recovery for each external integration. For analytics-heavy products, separate transactional FHIR retrieval from analytical processing. A benchmark of FHIR data engines found query times that reached 1 hour 26 minutes 27 seconds for 100,000 records in one tested configuration and 4 hours 22 minutes 49 seconds in another, while Blaze delivered subsecond count queries and Trino performed best for the largest record counts on a complex hemoglobin query, as reported in the comparative FHIR data-engine benchmark<\/a>. Architecture should match workload.<\/p>\n

Finally, select a partner using evidence and commit to a phased engagement with explicit knowledge transfer. Custom healthcare software development<\/a>, AI development services<\/a>, and healthcare integrations<\/a> should be evaluated as connected capabilities, not isolated service labels.<\/p>\n

\"A<\/figure>\n

Start the audit this week, before a vendor proposal fixes the wrong scope. Bridge Global can help healthtech teams with discovery, compliant product engineering, healthcare integrations, AI governance, and ongoing delivery support. Visit Bridge Global<\/a> to discuss your product gaps and define a phased engineering plan that stays accountable after launch.<\/p>","protected":false},"excerpt":{"rendered":"

Your product team has a clinical workflow that works in a pilot, a board asking for a faster release, and a compliance review that keeps uncovering assumptions nobody documented. At the same time, a clinician wants fewer clicks, an integration …<\/p>\n","protected":false},"author":165,"featured_media":57824,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1015],"tags":[1872,953,1032,1405,1637],"class_list":["post-57825","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-healthcare","tag-digital-health-product-engineering","tag-ai-in-healthcare","tag-hipaa-compliant-software","tag-fhir-integration","tag-healthtech-engineering"],"featured_image_src":"https:\/\/www.bridge-global.com\/blog\/wp-content\/uploads\/2026\/08\/digital-health-product-engineering-medical-hologram.jpg","author_info":{"display_name":"Upendra Jith","author_link":"https:\/\/www.bridge-global.com\/blog\/author\/upendrajith\/"},"_links":{"self":[{"href":"https:\/\/www.bridge-global.com\/blog\/wp-json\/wp\/v2\/posts\/57825","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.bridge-global.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bridge-global.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bridge-global.com\/blog\/wp-json\/wp\/v2\/users\/165"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bridge-global.com\/blog\/wp-json\/wp\/v2\/comments?post=57825"}],"version-history":[{"count":2,"href":"https:\/\/www.bridge-global.com\/blog\/wp-json\/wp\/v2\/posts\/57825\/revisions"}],"predecessor-version":[{"id":57831,"href":"https:\/\/www.bridge-global.com\/blog\/wp-json\/wp\/v2\/posts\/57825\/revisions\/57831"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.bridge-global.com\/blog\/wp-json\/wp\/v2\/media\/57824"}],"wp:attachment":[{"href":"https:\/\/www.bridge-global.com\/blog\/wp-json\/wp\/v2\/media?parent=57825"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bridge-global.com\/blog\/wp-json\/wp\/v2\/categories?post=57825"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bridge-global.com\/blog\/wp-json\/wp\/v2\/tags?post=57825"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}