The Clinical Frontier · 8 October 2026 · Issue 021 Haiku 5.5 cuts per-token cost by 90%. Here is what that means for cardiac triage in Indian hospitals.
In this issue
- Anthropic released Claude Haiku 5.5 on 7 October 2026, its cheapest, fastest small model, priced 90% below Haiku 4.5 for short requests and delivering up to 2.5x faster inference per agent turn.
- Cardiovascular disease is India’s leading non-communicable cause of death, with a growing burden across working-age adults. ECG machines are deployed widely, but interpretation backlogs persist, especially at night and in tier-2 and tier-3 facilities.
- Haiku 5.5’s cost and speed profile makes AI-assisted ECG report generation and urgency classification economically viable at the scale India needs.
- The new adjustable effort setting (Low to Max) lets hospital teams tune cost against accuracy: cheap passes for routine normal screens, higher effort for borderline or critical findings.
The signal
On 7 October 2026, Anthropic released Claude Haiku 5.5, describing it as “the cheapest, fastest, and most capable small model we’ve ever released.” The pricing shift is material: for requests up to 100,000 tokens, input is priced at $0.10 per million tokens, compared with $1.00 for Haiku 4.5, a 90% reduction. Cache reads fall from $0.10 to $0.01 per million tokens. Averaged across request lengths, Anthropic states Haiku 5.5 costs about 75% less than Haiku 4.5.
Early enterprise customers confirm the speed claims. Asana’s engineering team recorded over 30% lower latency for task completions and up to 2.5x faster inference per agent turn. Box scored the model 11 points above Haiku 4.5 on its internal evaluation at roughly half the latency. HubSpot reported a 92.8% average on its CRM simulation suite, the best result its team had seen, with the fastest completion time of any model tested.
Haiku 5.5 is also the first Haiku-class model with an adjustable effort setting, ranging from Low through Medium, High, Xhigh, and Max. That single feature changes what is possible in tiered clinical workflows.
India’s cardiac context
Cardiovascular disease is India’s leading cause of non-communicable death and a leading contributor to overall mortality, with the share of CVD deaths rising sharply over the past three decades as India’s epidemiological profile shifts. The burden falls disproportionately on working-age adults. No other diagnostic tool captures cardiac risk at the point of care as quickly, cheaply, and universally as a 12-lead ECG.
ECG machines are deployed across primary health centres and district hospitals under the National Health Mission’s cardiovascular screening programmes. The bottleneck is interpretation, not hardware. India’s cardiologist-to-population ratio is low and geographically concentrated in metro centres. A district hospital in a tier-3 city may have an ECG machine in every ward but limited access to a resident cardiologist outside regular hours. The result is a gap between ECG acquisition and structured, actionable clinical interpretation, which can delay identification of time-critical findings.
How a Haiku 5.5 ECG pipeline works
Modern ECG machines do not only produce a tracing. They generate a structured measurement output alongside it: QRS duration, QTc, PR interval, axis, ST-segment deviation values, and a preliminary algorithm interpretation flag. That machine-generated text is the input a language model can process directly.
A Haiku 5.5 ECG pipeline built on that output would operate in four steps.
Step 1: Receive machine output. The ECG device, whether a Philips, GE, Schiller, or any system producing HL7 ORU messages or a measurements table in a PDF report, exports the measurement string and algorithm flag to a lightweight API integration.
Step 2: Classify urgency. Haiku 5.5, running at Low or Medium effort for routine cases, classifies the ECG summary into one of four tiers: normal, non-urgent finding (requires routine review), urgent cardiology review (requires same-shift attention), or STEMI suspect (requires immediate escalation and activation protocol). The adjustable effort setting means the model runs cheaply for normal-screen passes and escalates to High or Xhigh effort for borderline cases that the primary algorithm flags ambiguously.
Step 3: Generate a structured report. For the attending clinician, Haiku 5.5 produces a brief, structured summary in plain language: key measurements, primary finding, urgency classification, and recommended action. This replaces the task of translating raw measurement strings into clinical language, which currently falls to a resident or on-call physician.
Step 4: Package for ABDM. The structured report is serialised as a FHIR DiagnosticReport resource under the ABDM FHIR R4 Implementation Guide profile, linked to the patient’s ABHA ID and Encounter record, and submitted via the Health Information Provider gateway for consent-based routing to a referral cardiologist or tele-cardiology service.
At $0.10 per million input tokens, a single ECG interpretation request consuming 500 tokens costs $0.00005. A district hospital processing 200 ECGs daily spends under two cents on AI inference for the classification step. The infrastructure and integration costs dominate; the per-study AI cost is negligible.
The effort parameter: matching intelligence to clinical urgency
Haiku 5.5’s Low-to-Max effort setting is a natural fit for a cardiac triage workflow where not every case warrants the same computational depth.
- Low effort: Routine screening pass. Quick classification of normal sinus rhythm or minor, clearly non-urgent findings. Fast and cheap. Appropriate for population-level screening programmes.
- Medium to High effort: Borderline cases with bundle branch blocks, prolonged QTc, equivocal ST-segment changes, or prior history context that adds ambiguity to a reading.
- Xhigh to Max effort: Complex presentations where the algorithm flag and clinical context are in tension and a richer reasoning pass may surface a critical finding before it is escalated.
This tiered cost structure does not change the clinical decision pathway. The physician remains responsible for diagnosis and treatment. What the effort parameter changes is the efficiency of the documentation and routing layer that feeds the physician’s queue.
Why India, why now
Three structural factors make Haiku 5.5’s economics particularly timely for Indian health-IT teams.
NHCX pre-authorisation: PM-JAY cashless workflows under the National Health Claims Exchange increasingly require structured diagnostic reports. A FHIR DiagnosticReport generated from an ECG supports NHCX claim submission and pre-authorisation without manual re-entry, reducing administrative friction for both hospital billing teams and insurers.
ABDM diagnostic exchange: The ABDM Health Information Provider gateway is the pathway for routing ECG findings to a tele-cardiology opinion service or a higher referral centre. Structured, ABHA-linked DiagnosticReport bundles are the prerequisite for that exchange. A text pipeline from machine output to structured report is more reliable than PDF transmission and more scalable than manual entry.
Tele-cardiology services: State NHM tele-cardiology networks and private cardiac platforms receive structured ECG data, not raw images or unstructured PDFs. A pipeline that converts machine output to a structured, ABHA-linked report and routes it via ABDM makes tele-cardiology consultations faster and more reliable.
The takeaway
Claude Haiku 5.5’s 90% cost reduction and 2.5x speed improvement, verified by Anthropic’s early enterprise customers, make the economics of AI-assisted ECG triage straightforward for Indian hospitals. The per-study AI inference cost is effectively zero relative to the operational cost of a missed or delayed cardiac finding.
The use case is a classification and documentation layer, not a diagnostic replacement. The bottleneck it addresses is the delay between machine-generated measurements and a structured, routed clinical action. India’s cardiovascular burden is large and concentrated at a point in the care chain where fast, low-cost screening tools can make a material difference. Cost-effective triage tools that accelerate structured interpretation at the point of care, and route urgent findings through the ABDM exchange layer to wherever a cardiologist is reachable, are a practical response to that challenge.
Health-IT teams can build this now. The model is available on the Anthropic API and on Amazon Web Services, Google Cloud, and Microsoft Azure. The ABDM FHIR R4 Implementation Guide from NRCES defines the DiagnosticReport profile. The components are in place; the deployment decision is the remaining step.
The Clinical Frontier is a daily briefing from HCITExperts for India’s health-IT community. Primary source: Claude Haiku 5.5, Anthropic, 7 October 2026. More tomorrow.