Healthcare AI glossary

AI Audit Trail (Clinical)

A clinical AI audit trail is the immutable, time-stamped record of an AI-assisted clinical document's full lifecycle: the raw AI output, every human edit made to it, the identity of the reviewing clinician, and the timestamps for each step. It exists so an organization can show exactly what the AI proposed, what a person changed, and who signed off — turning the principle that a human stays in control of the final record into verifiable evidence rather than a claim.

AI Front Desk

An AI front desk answers patient inquiries — bookings, questions, follow-ups — the moment they arrive, across channels and languages, around the clock. It gathers the basics, checks availability, and moves the patient toward a booking or the right next step, while anything that carries medical or operational weight is confirmed by staff.

AI Medical Scribe

An AI medical scribe captures the doctor–patient conversation with automatic speech recognition and converts it into structured clinical documentation — typically a SOAP note mapped to coding and electronic medical record (EMR) fields — so the physician reviews and approves a draft instead of typing during the consultation.

Ambient Clinical Documentation

Ambient clinical documentation is technology that quietly listens to a clinical encounter and automatically drafts the visit note — typically a SOAP note — which the clinician then reviews and signs. It removes manual typing during the consultation so the clinician can stay focused on the patient rather than the keyboard.

BPJS Kesehatan

BPJS Kesehatan (Badan Penyelenggara Jaminan Sosial Kesehatan) is Indonesia's national health insurance body, covering the large majority of the population and processing hundreds of millions of claims a year. Because reimbursement depends on accurate clinical documentation and coding, the quality of the encounter note is the single biggest constraint on whether a BPJS claim is paid.

Cantonese Medical Speech Recognition

Cantonese medical speech recognition is automatic speech recognition (ASR) tuned for Cantonese clinical speech — medical terminology, frequent code-switching with English, and everyday Hong Kong usage — so spoken consultations can be turned into structured clinical notes. General-purpose dictation tools tend to stumble on this mix; clinical Cantonese ASR is trained for it.

Care Loop

Care Loop is the continuity-of-care layer that keeps the patient relationship alive after a visit or screening: report explanation, abnormal-finding follow-up, recheck reminders, and longitudinal health engagement. It carries the same patient context forward so follow-up is specific rather than generic, with clinically meaningful messages kept under doctor supervision.

Clinical Documentation Improvement (CDI)

Clinical Documentation Improvement (CDI) is the discipline of making clinical documentation complete and precise enough that coding and claim files are better supported before submission. It is distinct from claims processing: CDI fixes the source record, while claims processing reads whatever record it is given.

E-Klaim / BPJS VClaim

E-Klaim is the claim-submission application Indonesian hospitals use to grade (group) and submit INA-CBGs claims to BPJS Kesehatan — Badan Penyelenggara Jaminan Sosial Kesehatan, the country's national health insurer — while VClaim is BPJS Kesehatan's verification web service and API that validates eligibility, referrals and claim data. Together they form the operational pipeline through which a coded inpatient episode becomes a paid claim.

Faskes (FKTP and FKRTL)

Faskes (fasilitas kesehatan, healthcare facility) is the tiered structure of providers in Indonesia's JKN system. FKTP (Fasilitas Kesehatan Tingkat Pertama) are first-level facilities — clinics, puskesmas, GP practices — that act as the entry point and gatekeeper; FKRTL (Fasilitas Kesehatan Rujukan Tingkat Lanjutan) are advanced referral facilities, mainly hospitals. A JKN patient normally starts at an FKTP and reaches an FKRTL only via a referral (surat rujukan).

Governed Healthcare AI

Governed healthcare AI is AI that operates inside a healthcare institution's clinical governance: every output is drafted by AI, reviewed and approved by a qualified clinician, and recorded with an audit trail. It is the operating principle behind Micromeet's platform — AI writes, doctors decide.

HbA1c (Glycated Hemoglobin)

HbA1c, or glycated hemoglobin, is a blood marker that reflects average blood glucose over roughly the past two to three months rather than a single moment. It is widely used to screen for and monitor diabetes because it captures longer-term glucose control without the need for fasting.

Hiperkes (Hygiene Perusahaan dan Kesehatan Kerja)

Hiperkes (Hygiene Perusahaan dan Kesehatan Kerja, or industrial hygiene and occupational health) is the Indonesian discipline and certification covering company hygiene and worker health. Doctors and paramedics handling occupational health examinations are typically expected to hold Hiperkes training, and the term frames how occupational MCU and fitness-for-work assessments are conducted.

HL7 FHIR R4

HL7 FHIR R4 (Health Level Seven, Fast Healthcare Interoperability Resources, Release 4) is the standard for exchanging healthcare data as modular, web-friendly resources accessed through RESTful APIs. It defines how clinical and administrative information — patients, encounters, observations, conditions — is represented and shared between systems, and Release 4 is the first normative edition, which is why it is widely adopted as a stable integration target.

Human-in-the-Loop (in healthcare AI)

Human-in-the-loop design places a qualified human reviewer at every decision point where an AI output has clinical consequences — with enough information, authority, and time to meaningfully evaluate and modify that output before it affects patient care. A clinician clicking 'approve' on a report they have not read is the form without the substance.

ICD-10

ICD-10 is the 10th revision of the World Health Organization's International Classification of Diseases — a standardized coding system of over 70,000 codes used to record diagnoses for clinical, statistical, and insurance-claim purposes. In Indonesia it is the coding basis for BPJS/INA-CBGs reimbursement, where the assigned codes determine the payment package.

ICD-11

ICD-11 is the 11th revision of the World Health Organization's International Classification of Diseases, activated for reporting in January 2022. It introduces a more granular, flexible, digital-first coding structure with over 55,000 foundation entities, and Indonesia's Ministry of Health (Kemenkes) has been working toward its adoption.

iDRG (Indonesian Diagnosis Related Groups)

iDRG (Indonesian Diagnosis Related Groups) is Indonesia's diagnosis-related-group casemix system for grouping inpatient episodes into payment tariffs, developed to succeed INA-CBGs (Indonesian Case Based Groups). Like its predecessor it bundles each admission into a tariff package driven by diagnosis and procedure coding plus documented severity, so the grouped tariff — and the payment — still depends on the accuracy and completeness of the clinical record.

INA-CBGs (Indonesian Case Based Groups)

INA-CBGs (Indonesian Case Based Groups) is the case-mix payment system BPJS Kesehatan uses to reimburse hospitals under Indonesia's national health insurance (JKN): each episode of care is grouped into a tariff package based on diagnosis and procedure codes rather than itemized fees. Because the group — and the payment — is driven by ICD diagnosis and procedure coding plus documented severity, the quality of the clinical note directly determines whether a claim is paid correctly.

ISO/IEC 27001:2022

ISO/IEC 27001:2022 is the international standard for an Information Security Management System (ISMS) — a framework of risk assessment, security controls, and continuous improvement, audited by an accredited third-party certification body. It demonstrates security-management maturity, not the absence of any incident.

JKN (Jaminan Kesehatan Nasional)

JKN (Jaminan Kesehatan Nasional) is Indonesia's National Health Insurance programme, administered by BPJS Kesehatan, which provides health coverage to the large majority of the Indonesian population. It is the framework under which referrals (surat rujukan), follow-up control letters (surat kontrol), facility tiers (Faskes), and INA-CBGs reimbursement all operate.

K3 (Keselamatan dan Kesehatan Kerja)

K3 (Keselamatan dan Kesehatan Kerja) is Indonesia's occupational safety and health framework — the body of law and practice governing worker safety, hazard control, and worker health, including the medical examinations that determine fitness for work. It underpins how occupational MCU findings translate into fit-to-work decisions and follow-up.

MCU (Medical Check-Up)

An MCU, or medical check-up, is a structured health screening that compiles multi-parameter results — laboratory tests, vitals, physical examination, and specialist findings — into a doctor-reviewed report. In Indonesia, annual MCUs are legally mandated for employees in hazardous work environments under Permenaker No. 02/1980, making MCU one of the highest-volume documentation workflows in the market.

Micromeet AI Care Command Center

Micromeet AI Care Command Center is the governed institution operations workbench for Micromeet products: it turns AI and human work into visible, accountable institution queues — every task has an owner, a status, an SLA, and an audit trail, with approved writeback to systems of record. It is what makes the difference between AI features bolted onto a clinic and an AI layer the institution can measure, manage, and trust.

PDPA (Singapore)

The PDPA (Personal Data Protection Act) is Singapore's data protection law governing the collection, use, disclosure, and care of personal data. For healthcare it operates alongside the Ministry of Health's Health IT Security Framework, setting the requirements technology vendors must meet to handle Singapore patient data.

PDPO (Hong Kong)

The PDPO (Personal Data (Privacy) Ordinance) is Hong Kong's data protection law governing how personal data — including health data — is collected, used, and transferred, with specific restrictions on data leaving Hong Kong jurisdiction. It applies to technology vendors handling Hong Kong patient data alongside Hospital Authority IT security policies.

Permenaker No. 02/1980

Permenaker No. 02/1980 (Peraturan Menteri Tenaga Kerja) is the Indonesian regulation that mandates medical examinations for workers — pre-employment, periodic, and special health checks — to assess fitness for work. It is a primary legal driver of corporate medical check-up (MCU) volume in Indonesia, especially for hazardous work environments.

Reference Range

A reference range is the interval of values considered normal for a healthy population for a given test — for example a blood test — against which a patient's result is flagged as normal, high, or low. The range is context-dependent: it varies with sex, age, and the laboratory method used, so the same number can be normal in one context and abnormal in another.

Resume Medis (Discharge Summary)

Resume medis is the Indonesian medical discharge summary — the document that summarizes an inpatient episode's diagnoses, procedures, treatment course and follow-up plan at the point of discharge. It serves two jobs at once: it carries the clinical handover that keeps care continuous after the patient leaves, and it is a primary source document for diagnosis and procedure coding when the claim is grouped and submitted.

SATUSEHAT

SATUSEHAT is Indonesia's national health data integration platform, built by the Ministry of Health (Kemenkes) to connect health facilities through a shared, standards-based data exchange. It is the backbone of Indonesia's digital-health transformation, designed so patient data can flow between systems rather than sitting in silos.

Severity Level (INA-CBGs)

A severity level (I, II, or III) in INA-CBGs is the tier of clinical complexity assigned to a coded case that raises the reimbursement package: level I is without complication/comorbidity, level II with minor, and level III with major complication or comorbidity. The level is justified by documented secondary diagnoses, so when comorbidities are present but not documented and coded, the hospital is paid for a less complex — and lower-value — case than it actually delivered.

SOAP Note

A SOAP note is the standard structure for a clinical encounter note — Subjective (what the patient reports), Objective (exam findings and results), Assessment (the clinician's interpretation), and Plan (next steps). It is the format most clinical documentation, and most AI medical scribes, produce.

Speed-to-Lead (Patient Access)

Speed-to-lead in healthcare patient access is how quickly a clinic responds to a new patient inquiry — the interval between a prospective patient reaching out and a first meaningful response. It is a strong driver of whether an inquiry converts into a booked visit, because interest fades fast and a prompt reply captures it before the patient looks elsewhere.

Surat Kontrol (BPJS follow-up control letter)

A surat kontrol is the follow-up control letter used in Indonesia's BPJS Kesehatan (JKN) system for routine follow-up care: it sets the control date when a patient should return, and patients who cannot attend should rebook. It exists because routine follow-up is different from emergency care and hospital schedules and doctor capacity must stay predictable.

Surat Rujukan (BPJS referral letter)

A surat rujukan is the referral letter that authorizes a JKN patient to move from a first-level facility (FKTP) to an advanced referral facility (FKRTL) in Indonesia's BPJS Kesehatan system. Without a valid referral, a non-emergency visit to a hospital generally cannot be claimed under JKN, which makes the referral a critical piece of documentation in the patient journey.

TLS 1.3 (Transport Layer Security)

TLS 1.3 is the latest version of the Transport Layer Security protocol, which encrypts data in transit between a browser or app and a server so it cannot be read or tampered with on the network. In healthcare AI it is the baseline expectation for protecting patient data while it moves between systems.

UU PDP (Personal Data Protection Law No. 27/2022)

UU PDP (Undang-Undang Perlindungan Data Pribadi, Law No. 27 of 2022) is Indonesia's Personal Data Protection Law, which treats health data as a category of sensitive personal data with heightened obligations. It governs how healthcare organizations and their technology vendors collect, process, transfer, and retain Indonesian patient data, including data-subject rights and breach notification.

Voice-to-EMR

Voice-to-EMR (also called V2N) is the workflow that turns a spoken clinical encounter into structured documentation written back into the electronic medical record (EMR). It pairs medical automatic speech recognition with structuring into a SOAP note and coding-ready context, with a clinician reviewing before anything is filed.