Healthcare Software Development: The Complete Guide

Healthcare software development is the design, engineering and ongoing maintenance of applications that create, store, exchange or act on health information, including clinical records, patient communication, medical device data, claims and operational data. It differs from general software development because the data is protected by law, the users work in clinical workflows and the cost of an error is measured in patient outcomes.

The category covers everything from a small clinic’s appointment app to a national claims platform. What the products share is a set of constraints that never go away: protected health information must be handled under HIPAA, GDPR or an equivalent law; systems must exchange data with electronic health records and devices built by other vendors; and any function that influences a clinical decision may be regulated as a medical device.

What makes healthcare different

Three things set the work apart. First, regulation is an input to architecture, not a checklist at the end: where data lives, how it is encrypted and who can read it are design decisions. Second, interoperability is unavoidable, because a product that cannot read from or write to the EHR will not be adopted; ONC reports that 70 percent of US non-federal acute care hospitals already enable FHIR-based app access to patient data. Third, clinical safety has its own discipline, with hazard analysis, risk classes and documented evidence, and in the UK it is a legal precondition for sale to the NHS.

Who builds it and who buys it

Buyers fall into four groups: providers (hospitals, clinics, physician groups) who need clinical and operational tools; payers who need claims, prior authorisation and member engagement systems; health-tech companies building a product to sell to the first two; and pharma, device and life-science companies who need software around a therapy or a device. Builders are in-house IT teams, EHR vendors and their app ecosystems, and specialist engineering partners like Ailoitte’s healthcare software development team. Most serious products involve all three, which is why the chapter on choosing a partner matters as much as the one on architecture.

The case for purpose-built software rests on four pressures that general tools cannot relieve: clinicians spend too long on documentation, patients expect digital access, a breach in healthcare costs more than in any other industry, and the market is growing fast enough that standing still means falling behind.

• 36 minof EHR time for a 30 minute primary care visit
• 80%of patients want to self-schedule from home or mobile
• $7.42Maverage cost of a healthcare data breach in 2025

Clinician time and burnout

A JAMA Network Open study of 307 primary care physicians found a typical 36.2 minutes of EHR time for every 30 minute visit, including 6.2 minutes outside scheduled hours. The AMA’s 2025 burnout survey of roughly 19,000 physicians put burnout at 41.9 percent, down from 48.2 percent in 2023 but still higher in emergency medicine and oncology. Documentation load is the most visible target for software, which is why ambient AI notes (chapter 7) have moved faster than any other clinical AI feature.

Patient expectations

Experian Health’s 2025 State of Patient Access survey found 80 percent of patients want to book appointments anytime from home or a mobile device, while only 54 percent of providers offer self-scheduling. Telehealth is now routine on the clinician side too: the AMA Physician Practice Benchmark Survey reports 71.4 percent of physicians used telehealth weekly in 2024, up from 25.1 percent in 2018. A provider without a usable patient app or portal is now the exception.

The cost of getting security wrong

IBM’s Cost of a Data Breach Report 2025 put the average healthcare breach at USD 7.42 million against a cross-industry average of 4.44 million, the fourteenth year in a row that healthcare was the costliest sector. In 2025, 710 breaches of 500 or more records were reported to the US Office for Civil Rights, affecting at least 61.6 million people, and the 2024 Change Healthcare ransomware attack alone reached about 192.7 million individuals. Security is a product requirement, not an IT afterthought.

Market size

Global Market Insights values the global digital health market at USD 312.9 billion in 2024, growing at about 21 percent a year through 2034. The number matters less than the direction: budgets for digital care, remote monitoring and AI-assisted operations keep growing, and McKinsey found that 85 percent of US healthcare leaders were already exploring or had adopted generative AI by early 2025.

Adopters saved about 16 minutes of documentation time per eight hours of scheduled patient care, and frequent users saved 27 minutes, but after-hours EHR work did not fall significantly.

Ambient Clinical Documentation Collaborative, JAMA, April 2026, 8,581 ambulatory clinicians across five health systems

Most products combine two or three of the categories below. A telehealth app needs scheduling and payments from practice management, a patient app needs records from the EHR, and a remote monitoring platform needs alerts that land in a clinician’s workflow. The table shows each type on its own so you can see where the integration lines fall.

Source: Ailoitte delivery experience. Categories follow the structure used across our healthcare software development services.

EHR and EMR

The electronic health record is the system everything else connects to. ONC reports that 99.4 percent of US non-federal acute care hospitals had a certified EHR in 2024, so most new EHR work is specialty systems, cloud-native replacements for small and mid-sized groups, and AI-native products that rethink the clinician interface. See our EHR and EMR software development page and the EHR software development guide for the build in detail.

Telehealth and virtual care

Video visits, asynchronous consults, e-prescribing and remote triage. The hard parts are not the video but the identity, consent, licensing by state or country, prescribing rules and getting the visit note into the EHR. Our telemedicine app development service and the doctor on demand app guide cover features, stack and cost.

Patient engagement and mHealth

Portals, apps, reminders, education, medication adherence and wellness tools. These products live or die on integration with scheduling and records, and on an interface that a 70 year old and a 17 year old can both use. The custom mHealth app development guide goes deeper on compliance and AI features.

Remote patient monitoring

Connected blood pressure cuffs, glucose meters, pulse oximeters and wearables feeding a care team dashboard. RPM has grown fast in the US because it is reimbursed: Medicare paid more than USD 500 million for RPM in 2024 and the 2026 fee schedule added codes for shorter monitoring periods (chapter 7). Start with what remote patient monitoring software is and the guide to HIPAA-ready apps for Bluetooth medical devices.

Clinical decision support and software as a medical device

Any software that analyses patient data to inform a diagnosis or treatment may be a medical device. Whether it is depends on the FDA’s clinical decision support criteria in the US and MDR Rule 11 in the EU (chapter 4). The SaMD vs SiMD explainer and our medical device software development page describe the IEC 62304 lifecycle this work requires.

Which type to start with

If you are a provider, start with the workflow that costs you the most staff time, which is usually scheduling, documentation or prior authorisation. If you are a founder, start with the narrowest product that a single buyer will pay for and that needs only one integration, then widen. A platform vision is fine; a platform first release is how budgets disappear.

Compliance in healthcare is layered: privacy law decides how data is protected, interoperability rules decide how it must be shared, device law decides whether the software itself is regulated, and standards decide how you prove all of it. The rules below are current as of October 2026. Where a rule is only proposed we say so, because building to a proposal that later changes is expensive, and ignoring one that becomes final is worse.

HIPAA and HITECH

The HIPAA Privacy, Security and Breach Notification Rules remain the US baseline for protected health information, and HITECH extends them directly to business associates, which means every vendor that touches PHI, including your software partner, signs a business associate agreement and carries its own liability. Penalties were inflation-adjusted on 28 January 2026 to a maximum of USD 2,190,294 per identical provision per year. Recent enforcement shows the pattern: in 2026 the Office for Civil Rights settled with a software business associate whose breach affected about 15 million people, and its twentieth ransomware action again cited a missing risk analysis. If you do only one compliance document well, make it the risk analysis.

US interoperability rules

The 21st Century Cures Act information blocking rules apply to providers, certified health IT developers and health information networks, with civil penalties of up to USD 1 million per violation for developers and networks; in February 2026 ASTP/ONC began issuing nonconformity letters over API performance. The certification programme now requires USCDI version 3 as the data baseline from January 2026, with USCDI v5 and US Core 8 approved for voluntary adoption. The CMS Interoperability and Prior Authorization rule requires affected payers to decide prior authorisations within 72 hours (expedited) or 7 days (standard) from January 2026, and to expose Patient Access, Provider Access, Payer-to-Payer and Prior Authorization FHIR APIs from January 2027. TEFCA, the national exchange framework, is live with more than a billion records exchanged in its first year of scale. For the build this means one thing: HL7 FHIR R4 with US Core profiles is the data model for anything that will touch a US record. Our HL7 FHIR guide and FHIR R4 integration explainer cover the standard itself.

FDA and software as a medical device

The FDA regulates software that meets the definition of a device, and it has now authorised more than 1,600 AI-enabled medical devices. Three documents matter most for product teams. The revised Clinical Decision Support guidance of January 2026 widens the space for non-device tools that give a single recommendation a clinician can independently review. The Predetermined Change Control Plan guidance, final since December 2024, lets a manufacturer pre-authorise how an AI model will be retrained and updated. And the Quality Management System Regulation, in force since 2 February 2026, aligns US device quality requirements with ISO 13485. The lifecycle guidance for AI-enabled device software, drafted in January 2025, is still a draft.

GDPR, the European Health Data Space and the EU AI Act

In the EU, health data is a special category under GDPR Article 9 and can only be processed under a specific exception such as explicit consent or the provision of care, which drives consent design, data minimisation and a data protection impact assessment for most products. The European Health Data Space regulation entered into force in March 2025: general application starts 26 March 2027, EHR system requirements and the first categories of patient data (summaries, e-prescriptions) apply from March 2029, and imaging, lab results and discharge reports follow in 2031. Any EHR system sold in the EU will need to meet its interoperability and logging requirements.

The EU AI Act timeline changed in July 2026. The Digital Omnibus on AI, now in force, moved stand-alone high-risk obligations to 2 December 2027 and the obligations for AI embedded in medical devices to 2 August 2028, while transparency duties for AI that interacts with people still apply from 2 August 2026. Triage, diagnostic and treatment-planning AI will fall under the high-risk regime, which means risk management, data governance, technical documentation, human oversight and logging. Our EU AI Act compliance guide walks through the obligations.

EU MDR and the standards behind all of it

Under EU MDR Rule 11, software that provides information for diagnostic or therapeutic decisions is at least Class IIa, rising to IIb or III with the severity of harm a failure could cause; a December 2025 Commission proposal to down-classify lower-impact software is still a proposal. The standards that turn these laws into engineering evidence are IEC 62304 for the software lifecycle (safety classes A, B and C), ISO 14971 for risk management, ISO 13485 for the quality system, ISO 27001:2022 for information security, and in the US the NIST Cybersecurity Framework 2.0 and NIST SP 800-66 Revision 2, which maps the HIPAA Security Rule to concrete controls and counts as a “recognised security practice” that OCR must consider when setting penalties. HITRUST and SOC 2 Type II are the assurance reports US buyers most often ask a vendor for.

UK NHS requirements

Selling into the NHS requires the Digital Technology Assessment Criteria (clinical safety, data protection, technical security, interoperability, usability and accessibility, revised in February 2026) and clinical risk management to DCB0129 for the manufacturer and DCB0160 for the deploying organisation, with a named clinical safety officer and a hazard log.

Regulation at a glance

Sources: Federal Register, HHS OCR, ASTP/ONC, CMS, FDA, EUR-Lex and NHS England, linked above.

Compliance checklist

• Data map completed: every place PHI or personal health data is created, stored, moved or displayed, including logs, backups and analytics
• Business associate or data processing agreements signed with every vendor that touches the data, cloud providers included
• Encryption at rest and in transit verified, with keys managed outside the application
• Role-based access with multi-factor authentication and an immutable audit trail on every record read
• Security risk analysis written, reviewed in the last 12 months and mapped to NIST SP 800-66
• Incident response and 72 hour restoration plan tested, not just written
• FHIR R4 conformance tested against a public sandbox before the first EHR integration
• Device determination documented: does any function meet the FDA, MDR or UK definition of a medical device
• AI features inventoried against the EU AI Act risk classes and FDA CDS criteria
• Your progress0 of 9 done

A healthcare product that will last has four separable layers: the user-facing applications, a domain layer that owns the clinical and operational logic, an interoperability layer that speaks FHIR, HL7 v2 and DICOM to the outside world, and a set of shared services for identity, consent, audit and notifications. Keeping the integration layer separate is the single architecture decision that pays back most, because EHR interfaces change, new partners arrive and device protocols vary, and none of that should reach the core.

Security by design

1. Threat model before the first sprint, and keep it current. List the data, the actors and the ways each actor could misuse access.
2. Encrypt everything, including backups, logs and analytics exports. Use managed key services and rotate keys. Never let PHI reach a third-party analytics or error-tracking tool.
3. Least privilege for people and services, enforced with role-based access and multi-factor authentication, with break-glass access that is logged and reviewed.
4. An immutable audit trail for every read and write of a record, retained for the period the regulator requires (six years under HIPAA).
5. Zero-trust networking between services, with short-lived credentials, and segmentation so that one compromised component cannot reach the data store.
6. Penetration testing before every major release and vulnerability scanning on a schedule, both of which the proposed HIPAA update would make mandatory.
7. An incident response plan with a tested restore. OCR’s enforcement record shows that the organisations that are fined are the ones that could not show a risk analysis or a recovery plan.

The interoperability layer

HL7 FHIR R4 is the common language. In the US you build to the US Core profiles and USCDI data classes; in the EU the EHDS will define its own exchange format on top of FHIR. In practice you will still meet HL7 v2 messages for admissions, orders and results in hospitals, DICOM for imaging, and proprietary device protocols. Epic’s Showroom and open.epic sandbox, and the Oracle Health developer programme, both give free FHIR sandboxes; a read-only FHIR integration typically takes a few hundred engineering hours, a SMART on FHIR app embedded inside the EHR several times that. Budget for the vendor’s own review cycle, which can take longer than the build.

Testing and clinical validation

Healthcare testing has three layers. Standard software testing covers functionality, performance and accessibility. Conformance testing proves the FHIR API, HL7 interfaces and security controls behave as the standards require, using public validators and sandboxes. Clinical validation, required for any decision-support or AI function, proves the output is safe and useful for the intended users and population, with a hazard log, acceptance criteria agreed with clinicians, and a plan for monitoring after release. Our QA and testing practice runs all three as one pipeline.

Cloud and hosting

Sources: AWS HealthLake FAQ, Azure Health Data Services, Google Cloud Healthcare API documentation.

All three platforms will sign a business associate agreement and all three run managed FHIR services. The decision usually comes down to where the data must live (GDPR and some national laws require EU hosting, and the EHDS will add its own rules), which EHR vendor and AI models you will connect to, and the skills of the team that will operate it. Our cloud and DevOps team sets up the landing zone, the encryption and the audit pipeline as code so that every environment is compliant from the first deploy.

These are ranges, not quotes, and they are in US dollars for a product built by an experienced partner with a blended onshore and offshore team. Integrations and compliance scope move the number far more than screen count does. The ranges below are Ailoitte’s indicative figures for 2026 and sit within the published ranges of other healthcare developers we checked, which are linked under the table.

Source: Ailoitte indicative ranges, October 2026, cross-checked against published 2026 ranges from Bitcot, Zfort and Cleveroad.

What moves the price

• Integrations. Each EHR connection adds roughly USD 20,000 to 50,000, each medical device integration 5,000 to 15,000, and a bidirectional Epic or Oracle Health integration with vendor review can exceed that.
• Compliance scope. One country and one law is the cheapest case; HIPAA plus GDPR plus a device classification can add 15 to 30 percent to the build for documentation, validation and audit evidence.
• User roles and portals. A patient app, a clinician app and an admin console are three products sharing one backend.
• AI features. A generative or predictive feature adds model work, validation on local data, monitoring and, where the use is clinical, regulatory documentation.
• Team location and engagement model, covered next.

Team location and hourly rates

Source: 2026 published rate ranges from Zfort and Cmarix. Ailoitte delivers with a blended model: senior architects and discovery leads close to the client, engineering in Bengaluru.

Maintenance and compliance budget

• 15 to 25%of build cost per year for maintenance and support
• 10 to 15%of the budget for compliance evidence and audits
• Every 12 monthsa new risk analysis, penetration test and dependency review

Plan for 15 to 25 percent of the initial build cost each year. That covers security patches, operating system and dependency updates, changes forced by regulators (USCDI versions, new CMS APIs, EHDS requirements), integration changes when an EHR vendor updates its API, and a steady stream of small features. Products with AI features carry extra cost for model monitoring and periodic revalidation.

A worked example

A regional clinic group wants a patient app with online booking, results, secure messaging and video visits, connected to its existing EHR. Discovery and compliance mapping: USD 15,000 to 25,000 over three to four weeks. Build of the patient app, clinician view and admin console with one FHIR integration and a video provider: 110,000 to 170,000 over four to five months. Security testing, HIPAA evidence pack and app store release: 15,000 to 25,000. First year of operation: 25,000 to 40,000. Total first-year cost: roughly USD 165,000 to 260,000. An AI-assisted engineering team, which we describe in chapter 10, typically shortens the build phase by a quarter to a third, which is where the range narrows.

AI has moved from pilots to regulated features inside everyday healthcare products, but the evidence is uneven across use cases. The pattern that holds is this: AI that removes administrative work has strong evidence and fast adoption; AI that supports a clinical decision works where it is narrow, validated locally and reviewed by a human; and AI that predicts outcomes across a whole hospital population has a record of failing when it is deployed without local validation.

Ambient clinical documentation

Ambient scribes listen to a consultation and draft the note. The Permanente Medical Group’s analysis of 7,260 physicians and 2.5 million encounters found time savings equal to 1,794 working days in a year, with 84 percent of physicians reporting better patient interactions. A JAMA Network Open study across six health systems saw self-reported burnout fall from 51.9 to 38.8 percent after 30 days. The largest study to date, in JAMA in April 2026, is more sober: across 8,581 clinicians the average saving was about 16 minutes of documentation time per eight hour clinic day, rising to 27 minutes for frequent users, and after-hours work did not fall. The lesson for product teams is that adoption design matters as much as the model, and that savings should be measured, not assumed. Our ambient AI clinical documentation work and the AI medical scribe development guide cover the build.

AI in imaging and diagnostics

Radiology accounts for about three quarters of all FDA AI-enabled device authorisations, because imaging has clear inputs, clear outputs and established study designs. Stroke detection tools that alert a neuro-interventional team directly from the scanner are the clearest commercial success, with vendor-reported reductions in treatment time of around half an hour. These products are medical devices and are built under the lifecycle described in chapter 4.

Prediction models and the validation problem

Agentic AI for administrative work

The fastest growing use of generative AI in healthcare is not clinical at all. Prior authorisation, claims status, eligibility checks, coding suggestions, referral management and patient scheduling are repetitive, document-heavy and measurable, and the CMS rule described in chapter 4 forces payers to expose exactly the APIs that agents need. Our prior authorisation automation work cuts a process that took days to minutes by reading the request, assembling the clinical evidence from the record and submitting it through the payer API, with a person approving anything the rules do not cover. The same pattern applies to intake, inbox triage and discharge paperwork; the AI in healthcare topic page lists the common use cases.

Remote monitoring and reimbursement

Medicare RPM users grew tenfold between 2019 and 2023, and the 2026 physician fee schedule added CPT 99445 and 99470 so that providers can bill for 2 to 15 days of device data and for shorter management time, which opens monitoring to post-discharge and short-episode care. The HHS Office of Inspector General has also called for more oversight of RPM billing, so audit-ready data provenance (which device, which reading, which clinician reviewed it) is a product requirement.

Interoperability by default and zero trust

FHIR APIs, TEFCA and the EHDS are turning data exchange from a project into a property of the product. On the security side, the Change Healthcare attack and the proposed HIPAA update have made zero-trust architecture, mandatory MFA and tested recovery plans the baseline that hospital security teams now ask every vendor about before a contract is signed.

Most physicians who used the ambient scribe reported it improved their interactions with patients and their satisfaction at work, and the time saved was equivalent to nearly 1,800 working days over a year.

The Permanente Medical Group, NEJM Catalyst follow-up analysis, 2025

Predictions in healthcare technology age badly, so this chapter sticks to things that are already in law, already funded or already in clinical use and asks what they add up to by 2029.

Agents that act across organisations

Once payers expose prior authorisation and claims APIs in 2027 and providers run agents on their side, the two will negotiate directly: a provider agent assembles the evidence, a payer agent applies the policy, and people handle exceptions. The same pattern will reach referrals, scheduling between organisations and supply ordering. Products should be designed so that every workflow can be driven by an API as well as a screen.

National and continental data networks

TEFCA in the US and the EHDS in Europe will make record exchange a utility rather than a point-to-point project. The products that benefit are the ones that can consume a complete longitudinal record and do something useful with it: risk stratification, care gap closure, medication reconciliation. Expect patient-controlled access, where the patient grants an app access to the whole record, to become normal.

Care that moves home

Reimbursement for remote monitoring, hospital-at-home programmes and cheaper connected devices are moving routine chronic care out of the clinic. The software challenge is less the device and more the alert logic, the escalation path and the clinician workload, which is where AI triage of incoming readings will matter most.

AI regulation converges

By 2028 the EU AI Act high-risk obligations, FDA lifecycle expectations for AI devices and the HIPAA security update will all be in force. They ask for broadly the same things: documented data governance, validation, human oversight, monitoring after release and the ability to explain a change. Teams that build those disciplines now will not have to retrofit them.

What to build for now

Build on FHIR, keep the integration layer separate, log everything, give every AI feature a validation plan and a human review step, and design each workflow to run through an API. None of that is futuristic; it is what the rules already written will require.

The partner decision matters more in healthcare than in most software because the cost of a failed audit, a rejected device submission or an integration that never goes live is higher than the cost of the build. Hourly rate is the least useful comparison. Healthcare references, security evidence and interoperability experience are the useful ones.

What to look for

1. Live healthcare products with named integrations. Ask to see the EHR or device connections running, not a slide.
2. A compliance posture you can verify: ISO 27001, SOC 2 Type II, HITRUST or equivalent, and a HIPAA control set that is part of delivery.
3. Regulatory experience for your device class if any part of the product is a medical device: IEC 62304 process, design history files, submissions that were accepted.
4. Interoperability fluency: FHIR R4, US Core, HL7 v2, DICOM, and experience with the vendor review cycles of Epic and Oracle Health.
5. Clinical involvement in design, with clinicians in discovery and usability testing, not just engineers.
6. AI practice with validation, monitoring and documentation, not just prompts.
7. A fixed-scope discovery phase before any build quote, so that the quote is based on a data map and an integration list rather than a guess.
8. Code ownership, documented handover and no lock-in to the partner’s platform.

Partner evaluation checklist

• Shows at least two live healthcare products with named integrations and a reference you can call
• Has signed business associate or data processing agreements before and can share its template
• Runs penetration tests and can show a recent summary report
• Explains how AI features are validated on local data and monitored after release
• Offers a fixed-scope discovery before quoting the build
• Provides a named team with healthcare experience rather than a rotating bench
• Hands over code, infrastructure as code and documentation at every milestone
• Can describe what it would do in the first 24 hours of a suspected breach
• Your progress0 of 8 done

Engagement models

Three models cover most healthcare work. A fixed-scope discovery (two to four weeks) produces the data map, integration list, compliance plan and estimate, and is the right first step whatever follows. A dedicated team or pod, billed monthly, suits products that will evolve for years and need continuity of healthcare knowledge; our dedicated healthcare software developers work this way. A fixed-price build suits a tightly scoped first release after discovery, when the integrations are known. Time and materials without a discovery phase is the model to avoid.

Red flags

• A fixed price before any discovery or data mapping.
• No clear answer to where PHI will be stored and who can access it.
• No healthcare references, or references that cannot be called.
• Compliance described as something that happens before launch rather than throughout.
• AI features promised without a validation or monitoring plan.
• Reluctance to put code ownership and handover in the contract.

Ailoitte has shipped more than 300 products with users in 22+ countries and over 50 million downloads. The healthcare work below is chosen to show the range covered in this guide: connected care at payer scale, an EHR, telehealth, consumer health and clinical AI.

Case notes

AssureCare is a connected care platform serving more than 53 million members, linking payers, providers and pharmacies to coordinate care and lower costs, built as web, iOS and Android applications on Node.js, PostgreSQL, React and AWS. Its companion MedCompass handles care coordination, treatment approvals and medication tracking across plans, providers and community organisations.

iPatientCare is a cloud-based EHR for primary care, specialty and pharmacy practices with telehealth and secure messaging, designed with physicians for value-based care. CareFlow AI takes the next step: a HIPAA-compliant, AI-native EHR with ambient scribing, clinical decision support and FHIR exchange built in from the start.

EaseCare is a telehealth application with booking, video and chat consultations, e-prescriptions and records, and Mindfully is a mental health therapy platform with encrypted video sessions and a provider dashboard. Dr. Morepen brought a 40 year old consumer health brand with more than a million customers into a preventive health app with ordering, subscriptions and health tracking.

On the AI side, MediChat is a healthcare chatbot that handles booking, triage and medication reminders with EHR and telehealth integration, and a retrieval-augmented clinical knowledge assistant gives clinicians across a European hospital network with more than 15 branches governed access to protocols and records.

• 300+products shipped
• 22+countries with live users
• ISO 27001, SOC 2and ISO 9001 certified delivery

What they have in common

Every one of these products started with a data map and an integration list, carried its compliance evidence through delivery, and was built with a separate interoperability layer so that EHR, device and payer connections could change without touching the core. None of them was built as a platform first; each shipped one workflow well and widened from there.

How we work

Ailoitte is an AI-native product engineering company, which means our engineers work with AI tooling across discovery, design, code, test and documentation, under the same ISO 27001, ISO 9001 and SOC 2 Type II controls. Through AI Velocity Pods we ship a first release in an average of 38 days for well-scoped products, and for regulated work we follow IEC 62304 and ISO 14971 with submission-ready documentation. If you are planning a healthcare product, the discovery workshop is the right place to start, and our healthcare software development services page describes the full offer.

Written by Sunil Kumar CEO, Ailoitte

Sunil leads Ailoitte, an AI-native product engineering company that has shipped more than 300 products, including connected care, EHR, telehealth and clinical AI platforms, for clients in the US, Europe, the Middle East and Asia. He writes about how AI changes the way software is planned, built and bought.

Reviewed by Ailoitte Healthcare Engineering team

About Ailoitte