Health Data Continuity Index (HDCI)

Problem Definition: Data Abundance Without Integration

Health system performance is conventionally measured through cost efficiency, queue lengths, and population-level morbidity statistics. These metrics describe the system as a production process — a ratio of inputs to outputs. This framing systematically omits one critical variable: how much of the collected health data converts into coordinated, integrated care.

Finland is internationally exceptional in health data coverage. Yet this same system routinely produces a structurally predictable failure: the patient with multiple simultaneous conditions, several medications from different prescribers, chronic findings across multiple specialties — whose complete picture no single clinician holds or is prompted to synthesise. Data exists for each component. The integration does not occur.

This is not a data volume problem. It is not a digitisation problem. It is an integration gap — a structural discontinuity between what the system records and what it acts upon.

The analogy to ACI's energy analysis is precise: the Nordic power system does not lack capacity data — it lacks instruments that make capacity adequacy visible under compound stress. The Finnish health system does not lack patient data — it lacks instruments that make integration failure visible under multi-morbidity conditions.

HDCI is the instrument for the second problem.

This paper identifies an asymmetry that mirrors the findings of WP-017 (Parliamentary Decision Latency and Market Signal Correlation) in a different sector. The Finnish welfare system reacts rapidly to signals it can measure: cost overruns, queue lengths, and morbidity indices produce administrative and political responses. It reacts slowly — or not at all — to integration failure, because integration failure is not measured. No measurement means no sanction, and no sanction means no correction. HDCI is an instrument for breaking this loop: making integration quality visible so that it can be governed. HDCI is to health data what IQS (Integration Quality Score, WP-018) is to energy capacity — a measure of integration quality, not installed volume.

HDCI — A Constructed Measurement Framework

Health Data Continuity Index is a constructed index: it derives new indicators from existing data sources rather than aggregating pre-existing official metrics. This is methodologically equivalent to ACI's energy instruments (EPP, FS(p), SP_cluster) — which also derive new diagnostic quantities from publicly available grid and market data.

HDCI consists of five components:

DCI — Data Capture Index

Measures the coverage of recorded health data relative to expected documentation given population morbidity profile.

IAI — Integration Achievement Index

Measures the fraction of care contacts in which multi-specialty integration is achieved for patients with multiple simultaneous conditions. This is HDCI's central component — the direct measure of coordination rather than volume.

RVI — Response Velocity Index

Measures the interval between initial clinical finding and first responsive care action. Defined at the level of the first recorded finding (diagnosis code, laboratory result, risk flag) rather than the referral letter — capturing the full latency of institutional response.

RKI — Risk Accumulation Index

Measures the prevalence of uncoordinated polypharmacy: patients with five or more medications sourced from two or more prescribers, without a shared care plan entry.

PAI — Prevention Activity Index

Measures the fraction of total care contacts that are preventive in nature: screenings, risk assessments, lifestyle counselling.

HDCI v1 — Core Index

Three-component core index. RKI_adjusted, IAI, and RVI are the three components with direct outcome validation pathways and the lowest endogeneity risk. DCI and PAI are retained as diagnostic side-indicators but excluded from the composite (see §6).

DCI and PAI — Side Indicators

DCI (Data Capture Index) has a structural endogeneity problem — it shares causal ancestors with the care contact rate it uses as a predictor. It is reported separately as a documentation completeness indicator, not included in HDCI_v1. PAI (Prevention Activity Index) is an input-effort indicator that measures care system activity, not integration effectiveness. It is reported as dashboard metadata.

All HDCI_v1 weights are Bayesian priors subject to outcome-constrained empirical re-estimation. The pilot (DT-006) will report both prior-weighted and outcome-optimised HDCI_v1.

Empirical Operationalisation and Institutional Pathway

HDCI is not a theoretical proposal. The data required to calculate it exists. The obstacle is access structure, not data existence.

3.1 Data Sources and Availability

The structural situation is identical to ACI's energy analysis: Fingrid's production data is open; NVE's reservoir data requires API access and proxy construction. HDCI's case is that all required data exists within Finnish health registries — but patient-level linkage requires formal secondary use licensing through Findata.

3.2 The National Health Index as Precedent

THL and Kela jointly produce the Kansallinen terveysindeksi — a composite index combining records from THL, Kela, the Pension Insurance Centre, and Statistics Finland. This demonstrates that multi-registrar health data linkage is already operationally established in Finland. HDCI does not require a new technical or legislative framework. It requires a decision to calculate a different quantity from an already-functioning linkage infrastructure.

The National Health Index measures how sick the population is. HDCI measures how well the system responds to what it knows about the sick population. Both use the same data infrastructure. Neither replaces the other.

3.3 FHDS: Institutional Home

Based on public statements and ministerial communications, THL is expected to assume a central role in the Finnish Health Data Space (FHDS) data governance and licensing function. FHDS is intended to consolidate health data infrastructure, licensing, and research facilitation — and to enable more individually tailored care, cost containment, and accelerated research. (Source: STM FHDS preparatory documents 2024–2025; final governance model subject to legislative decision.)

HDCI is a natural first use case for FHDS. It does not require new legislation, new agencies, or new data collection. It requires a decision: calculate the integration index from already-available registry linkages and publish it as part of national health system monitoring.

3.4 Calibration Principles

Theoretical weights are calibrated against known outcome indicators:

• Preventable hospitalisations — high IAI and low RVI predict fewer avoidable admissions
• Adverse medication events — high RKI correlates with documented polypharmacy harm incidents
• Inter-welfare-area variation — morbidity-adjusted differences in care outcomes identify integration failure effects

3.5 Two-Level Architecture

HDCI operates at two levels with different temporal properties:

Registry-level HDCI — calculated from FHDS-linked THL and Kela registries. Suitable for population-level monitoring and inter-welfare-area comparison. Update frequency: quarterly to annual. This level is operable now with Findata licensing.

Field-level HDCI — a conceptual architecture in which HDCI components are calculated in near-real-time from continuously collected individual-level data, processed locally on a sovereign device without requiring central registry access. The design target: explainable, offline-capable, auditable health intelligence at the point of care. This level describes a design direction for future development, not a deployed capability.

Policy Implications: From Cost Accounting to Integration Accounting

HDCI's introduction is not a measurement addition — it is a framing shift. The current welfare system governance framework relies on three primary performance measures:

These metrics describe the system as a production process. HDCI describes it as a coordination process. The distinction matters because a system can score well on all three current metrics while systematically failing to integrate care for its most complex patients.

Institutional Responsibility

The Integration Incentive Gap

HDCI also makes visible a structural problem in the current governance logic: welfare areas that achieve integration and thereby reduce future care episodes receive less future funding. The incentive structure penalises the outcome HDCI is designed to encourage. Integration accounting — measuring coordination rather than consumption — is a prerequisite for incentive reform.

International Comparison

Integration failure is not a Finnish peculiarity — it is a structural property of health systems that evolved from episodic acute-care models into complex multi-morbidity environments. What is comparatively unusual about Finland is the gap between data richness and integration measurement.

HDCI's IAI, RVI, and RKI components directly correspond to OECD Patient Safety and Quality care coordination indicators. A Nordic HDCI comparison — calculated from Finnish, Swedish, and Danish registry data — would constitute the first composite integration index in the Nordic health policy space.

The Netherlands experience is particularly instructive for HDCI's design rationale. Bundled payments improved coordination within disease categories but produced cost increases and struggled with multimorbid patients — precisely the population where integration failure is most consequential. The Dutch are now evolving toward whole-system person-centred payment. HDCI's architecture starts where the Dutch are heading: a whole-system integration index covering all multi-morbid patients, not organised by disease category. This is not a coincidence — it reflects the same structural finding that disease-specific integration instruments are insufficient for the multi-morbidity challenge.

Swedish precedents: the HDCI logic already in motion

The structural analogy to ACI's energy analysis extends to the health domain. Just as Stockholm Exergi validated the SGFA energy architecture by making a final investment decision in March 2025, Sweden has already deployed the institutional infrastructure that HDCI requires — before Finland has formally decided to build it.

Vårdanalys — Sweden's independent health analysis authority — produces systematic annual assessments of care integration, including sammanhållen vård (coordinated care), transitions between care levels, and polypharmacy management. These are the IAI, RVI, and RKI components of HDCI, measured separately rather than as a composite. Vårdanalys demonstrates that independent, systematic integration measurement is institutionally achievable within a Nordic health system — without requiring new data collection, only a mandate and an analytical framework.

Information-Driven Healthcare (Vinnova, 2019–2024) — a national programme that united all 21 Swedish regional health authorities with academia and industry to deploy AI-assisted care at scale. The programme's explicit objective — offering more information-driven, personalised, and scalable healthcare by systematically utilising all existing data — is precisely the institutional commitment that HDCI operationalises as a measurement. Sweden built the institutional coalition first, then deployed the tools. The programme is now concluded; its infrastructure remains.

Conclusions and Research Priorities

Five structural observations emerge from this analysis:

1. Data existence does not imply data use. Finland's registry infrastructure is internationally exceptional; its integration measurement is not. The gap is structural, not technical.

2. HDCI is calculable. All five components have identified data sources within existing Finnish health registries. The path from theoretical framework to empirical index requires Findata licensing, not new data collection.

3. FHDS is the institutional home. THL's readiness to assume FHDS governance creates a natural institutional location for HDCI — an index calculated from the same registry linkages FHDS will facilitate.

4. Current metrics are insufficient but not wrong. Cost, queue, and morbidity metrics are valid. They are insufficient because they do not measure the coordination dimension. HDCI complements rather than replaces existing monitoring.

5. The incentive structure requires reform. Integration accounting is necessary but not sufficient — incentive reform must follow. Measuring integration is the diagnostic precondition for correcting the governance dynamics that currently penalise it.

Research Priorities

HDCI is not a solution. It is a diagnostic instrument. Its purpose is to make visible what the system already knows but does not currently measure: how much of its data becomes care.

Measurement Validity and Identifiability

HDCI is a constructed measurement instrument. Its analytical value depends on whether its components measure what they claim to measure, and whether observed variation reflects genuine integration differences rather than artefacts of data structure or documentation practice. This section identifies the principal validity threats and specifies how they are addressed in the pilot design.

6.1 DCI — Expected Entry Model Specification and Endogeneity

DCI has an endogeneity problem that must be stated explicitly: morbidity drives care contacts, which drive Kanta entries, which constitute the DCI numerator. The same causal structure produces both the predictor and the outcome. Without an exogenous instrument, DCI risks becoming a residual measure of documentation intensity rather than a measure of data capture quality.

The pilot addresses this by adding Avohilmo contact rate as an exogenous instrument in the expected entry model:

expected_entries_i = f(morbidity_index, age, sex, Avohilmo_contact_rate_i)

Avohilmo contact rate is independently measured and separates care contact frequency (how often the patient enters the system) from Kanta entry rate (how often those contacts produce structured records). A patient with high contact rate but low Kanta entry rate has a genuine documentation gap — not a care frequency artefact. A patient with low contact rate and proportionally low Kanta entries does not. This distinction is what DCI is intended to capture.

Model specification: negative binomial regression (appropriate for count data with overdispersion). Sensitivity analysis: Poisson specification and stratum-mean alternative. If rankings across specifications are unstable, DCI requires further instrument development before use as a composite component.

6.2 IAI — Structural Proxy, Not Clinical Truth-Metric

IAI is a structural proxy for integration, not a clinically invariant measure. The 30-day episode window treats all care episodes as having the same temporal integration structure. This is false in clinical reality: a post-surgical patient may require multi-specialty contact within days; a stable diabetic may have meaningful integrated episodes spanning months; a psychiatric patient's integration needs operate on a different timescale entirely. A single window cannot be neutral across these clinical contexts.

IAI should therefore be interpreted as a system-level diagnostic, not as a patient-level truth claim about whether care was integrated. What it measures is whether the system produces multi-specialty contact within a defined time window — a structural observation about system organisation, not about care quality.

Sensitivity analysis: 15, 30, and 90-day windows. If sub-region IAI rankings are stable across windows, the 30-day default is robust as a structural indicator. If rankings change materially, disease-stratified analysis is required and IAI reverts to a domain-specific measure rather than a composite component. This is a methodological finding, not a failure of the instrument.

6.3 RKI — Adjusted for Kanta Adoption Rate

RKI is the strongest HDCI component — prescriber multiplicity combined with absence of a shared care plan is the most direct structural proxy for coordination failure available in Finnish registry data. However, its validity depends on Kanta care plan adoption rates, which vary by municipality and care setting and are not under patient or clinician control.

The pilot will report two RKI values:

RKI_raw = N_risk / N_poly
RKI_adjusted = RKI_raw / Kanta_care_plan_adoption_rate

RKI_adjusted isolates genuine coordination failure from documentation infrastructure variation. A high RKI_adjusted in a care setting with high Kanta adoption is a stronger signal of coordination failure than a high RKI_raw in a setting with low adoption. Both are reported; composite HDCI uses RKI_adjusted.

Cross-validation against adverse medication event outcomes (H2) will test whether RKI_adjusted captures genuine coordination failure: if RKI_adjusted predicts adverse events independently of adoption rate, the adjustment is valid. If residual adoption-rate effects persist after adjustment, further modelling is required.

6.4 RVI — Institutional Throughput, Not Clinical Appropriateness

RVI measures the latency between the system's first recorded awareness of a clinical finding and its first recorded response. It is a measure of institutional throughput — how quickly the system converts observation into action — not of clinical appropriateness. A low RVI (fast response) may reflect efficient care, aggressive diagnostic coding, or high system sensitivity to marginal findings. These are not equivalent.

The "first finding" is not a neutral observation: it depends on the testing frequency, coding practices, and diagnostic sensitivity of the care setting. A care setting that orders more tests will produce earlier "first findings" and therefore mechanically higher RVI values, independently of actual response quality. RVI should be interpreted as a system-level indicator of response velocity, not as a patient-level measure of care quality.

6.5 HDCI as Composite Proxy Index — Not a Latent Variable Model

HDCI does not assume that "integration" is a unitary latent variable. Integration in healthcare is multidimensional: information flow, care responsibility coordination, and clinical decision consistency are distinct constructs that do not necessarily co-vary. HDCI aggregates observable proxies for these dimensions into a single diagnostic score. It is a composite proxy index, not a structural equation model for integration as a latent entity.

This distinction matters for interpretation: HDCI scores are diagnostic signals about where integration-relevant observables are weakest, not measurements of an underlying integration quantity. The instrument is analogous to a credit score or an air quality index — practically useful as a signal, but not a direct measurement of the phenomenon it proxies.

6.6 Composite Weights — Version 0.6 Revision

Based on the identifiability analysis above, weights are revised as follows:

HDCI = 0.15·DCI + 0.30·IAI + 0.25·RVI + 0.25·RKI + 0.05·PAI

Changes from v0.5: RKI weight increased 0.20 → 0.25 (strongest component, direct outcome validation pathway). PAI weight reduced 0.10 → 0.05 (input-effort indicator only; does not measure prevention effectiveness). PAI is retained as a process indicator but its composite contribution is minimised to reflect its limited outcome validity.

All weights remain Bayesian priors subject to outcome-constrained re-estimation. The pilot will report both prior-weighted HDCI (v0.6 weights) and outcome-optimised HDCI. Divergence between the two is a finding requiring examination, not a failure.

6.7 Documentation Bias as a Confound

All five components are derived from administrative documentation systems. A welfare area with higher documentation quality will score higher on HDCI independently of care integration quality. Three mitigations are built into the pilot design: outcome validation tests real health effects; within-Pohjois-Savo comparison controls for regional documentation culture; DCI uses Avohilmo contact rate as an exogenous instrument to separate documentation completeness from care frequency. Residual documentation bias cannot be eliminated — it must be acknowledged in all publications.

6.8 Causal Structure — DAG

The following directed acyclic graph makes explicit the causal relationships between inputs, components, and outcomes. It identifies the endogeneity risks and the exogenous instruments used to address them.

EXOGENOUS INPUTS
─────────────────────────────────────────────────────
Morbidity (Sotkanet)        Age / Sex
       │                         │
       └──────────┬──────────────┘
                  ▼
         Care Contacts ◄── Avohilmo Contact Rate
         (endogenous)       (exogenous instrument)
                  │
       ┌──────────┼──────────────┐
       ▼          ▼              ▼
  Kanta        Multi-spec    First finding
  entries      episodes      → First action
  (DCI)        (IAI)         (RVI)
       │                         │
       └──────────┬──────────────┘
                  │
       ┌──────────┤
       ▼          ▼
  Prescriber   Kanta care
  multiplicity plan entries
       └────────► RKI_raw
                  │
  Kanta adoption ─┤
  rate (control)  ▼
                  RKI_adjusted ◄── strongest component

PREVENTIVE
contacts / total ─► PAI (input-effort only)

COMPOSITE
─────────────────────────────────────────────────────
HDCI = 0.15·DCI + 0.30·IAI + 0.25·RVI + 0.25·RKI_adj + 0.05·PAI

VALIDATION OUTCOMES
─────────────────────────────────────────────────────
H1: Preventable hospitalisations ◄── HDCI composite
H2: Adverse medication events   ◄── RKI_adjusted (primary)
H3: Inter-municipality variation ◄── HDCI components

The DAG makes visible the two main endogeneity risks: (1) DCI shares causal ancestors with care contact rate — addressed by the Avohilmo instrument; (2) RKI shares causal ancestors with Kanta adoption rate — addressed by the adjusted/raw decomposition. RVI's endogeneity (diagnostic system sensitivity affecting "first finding") is acknowledged but not fully resolved in v0.6 — it is a constraint on RVI interpretation, not a reason to exclude it.

Incentive Coupling Mechanism — From Diagnostic to Decision Instrument

HDCI is a diagnostic instrument. Without an incentive coupling mechanism, it remains a measurement without consequence — a score that describes integration failure but does not correct it. This section specifies the minimum viable incentive coupling model and identifies the structural conditions under which it avoids perverse outcomes.

7.1 The Integration Incentive Gap

The current welfare area governance structure contains a structural perverse incentive: a welfare area that achieves integration — reducing future care episodes through coordinated prevention and polypharmacy management — receives less future funding because it generates fewer billable contacts. The incentive structure penalises the outcome HDCI is designed to encourage. This is not an oversight — it is a structural property of consumption-based funding models applied to coordination-intensive care.

HDCI makes this gap visible and measurable. The next step is coupling HDCI measurement to funding adjustment in a way that corrects the perverse incentive without creating new ones.

7.2 Funding Adjustment Model

The proposed coupling mechanism follows a morbidity-adjusted improvement logic:

The cost ceiling condition (cost_per_capita ≤ morbidity_adjusted_baseline + δ) prevents a welfare area from gaming the bonus by reducing care volume without reducing coordination failure. The risk selection guard prevents patient selection — a welfare area cannot improve its HDCI by reducing exposure to complex multi-morbid patients. Both guards are verifiable from existing registry data.

7.3 Comparison: Dutch Bundled Payment Experience

The Netherlands introduced bundled payments for diabetes, COPD, and vascular risk management in 2010. The model improved coordination within disease categories but produced two structural limitations: (1) costs increased 13–52% per disease group over seven years, as savings in secondary care were not recaptured; (2) the model failed for multimorbid patients who crossed disease-bundle boundaries. The Dutch are now evolving toward person-centred bundled payments with shared savings — the direction HDCI's architecture starts from.

The HDCI incentive model avoids the Dutch limitations by design: it is whole-system (not disease-specific), it uses morbidity-adjusted baselines (not raw contact counts), and it includes a cost ceiling that prevents unbounded spending alongside integration improvement. The Dutch experience is the empirical precedent; HDCI's model is the methodological response.

7.4 IAI Conditioning — Integration Opportunity Set

A referee finding requires an explicit methodological addition to IAI. Not all patients require multi-specialty integration — a welfare area with a higher proportion of multi-morbid patients will mechanically show higher IAI values even if its integration capability is unchanged. IAI must be conditioned on the integration opportunity set to be a valid inter-area comparator.

This conditioning makes IAI invariant to morbidity composition differences across welfare areas. A welfare area with 40% multi-morbid patients and 60% of those receiving integrated care scores identically to one with 20% multi-morbid patients and 60% integration rate — which is the correct result.

7.5 RVI Anchor Finding Specification

A second referee finding: RVI is partially confounded by diagnostic aggressiveness. A care setting that tests more intensively will produce earlier "first findings" and mechanically higher RVI values independently of response quality. The correction is to anchor RVI on a set of hard clinical findings where the finding threshold is physiologically defined rather than set by clinician discretion.

Anchor finding set (v0.7 specification):

RVI_anchored is the Nordic comparison instrument. RVI_raw is the within-welfare-area operational indicator. The distinction allows Nordic comparison (which requires cross-system invariance) while preserving operational utility for internal monitoring.

7.6 Revised Component Summary

HDCI_v1 = 0.40 · RKI_adjusted + 0.35 · IAI_conditioned + 0.25 · RVI_anchored

7.4 Campbellin laki, gaming-riskit ja sandbox-validointi

Tämä osio on lisätty vastauksena vertaisarvioinnin tunnistamiiin rakenteellisiin riskeihin (kesäkuu 2026).

Campbellin laki ja HDCI. Kun HDCI kytketään rahoitusohjaukseeen (§07.2), se lakkaa olemasta mittari ja muuttuu institutionaaliseksi optimointifunktioksi. Campbellin laki (Campbell 1979) ennustaa, että jokaisesta rahoitukseen kytketystä mittarista tulee ennenpitkää optimoinnin kohde: järjestelmä ei optimoi integraatiota vaan HDCI:n rakennetta. Koska HDCI on monikomponenttinen, optimointi voi tapahtua samanaikaisesti useassa suunnassa — mikä tekee yksittäisten indikaattorien valvomisesta erityisen vaikeaa.

Kolme tunnistettua gaming-polkua. Seuraavat kolme reittiä ovat etukäteen tunnistettavissa ennen pilotointia:

• Kirjaamiskäytäntöjen muutos → IAI/RKI paranee ilman hoidon sisällön muutosta. Diagnoosikoodien tai käyntikirjausten systemaattinen muutos voi nostaa integraatio-indikaattoreita ilman kliinistä vaikutusta.
• Diagnoosikynnysten tai testauksen intensiteetin säätö → RVI muuttuu ilman todellista löydösasteen muutosta. RVI on erityisen herkkä testauskäytäntöjen vaihtelulle.
• Preskriptorin hallinnollinen keskittäminen → RKI paranee ilman kliinistä integraatiota. Polyfarmasia-indikaattori paranee jos kirjaukset ohjataan yhdelle lääkärille ilman että tosiasiallinen hoidon koordinointi paranee.

Gaming-testaus on sisällytettävä pilottivaiheeseen eksplisiittisesti — ei jätettävä jälkikäteiskorjauksen varaan. Invarianssitestaus (kirjaamiskäytäntöjen vaihtelu, alue-erot, case-mix) on minimivaatimus ennen rahoituskytkentää.

HDCI sandbox-implementaationa. BRIDGE-projektin (EU/IHI 2025–2028) MOSAIC-viitekehys tarjoaa luontevan vertailukohdan: regulatory sandbox, jossa innovaatio testataan kontrolloidusti ennen täysimittaista käyttöönottoa. HDCI on lähempänä "regulatory sandbox -casea" kuin valmista policy-instrumenttia. Sandbox-toteutus Pohjois-Savossa (DT-006) mahdollistaisi:

• eri hyvinvointialueet sandbox-instansseina erilaisilla HDCI-painotuksilla
• kontrolloidut variantit (eri α, β, γ -painot) rinnakkaisina koeasetelmina
• outcome-validointi: parantuuko tosiasiallinen integraatio vai pelkästään indeksi
• gaming-havainnointi reaaliajassa ennen rahoituskytkentää

Ilman sandbox-vaihetta HDCI:n rahoituskytkentä on ennenaikainen. Campbellin lain implikaatio on selvä: mitä nopeammin mittari kytketään ohjaukseen, sitä nopeammin se lakkaa mittaamasta alkuperäistä ilmiötä.

Viitteet: Campbell, D.T. (1979). Assessing the Impact of Planned Social Change. Evaluation and Program Planning 2(1), 67–90. BRIDGE project (2025–2028), Innovative Health Initiative. VTT MOSAIC-arkkitehtuuri sääntelysandboxeille. ACI CN-026 §6 (Campbellin laki mittausjärjestelmissä).

Policy Context: The Kela Private Healthcare Experiment

In 2025–2026, Finland began piloting expanded Kela reimbursements for private healthcare visits as a mechanism for reducing public sector queue lengths. The political logic is reactive: queues have formed, private capacity exists, Kela reimbursements could route patients to that capacity and thereby shorten waiting times.

From the HDCI perspective, this is the wrong solution to the right problem — and it illustrates precisely the measurement gap that HDCI is designed to close. The Kela experiment treats queue length as the primary indicator of system health. It responds to that indicator by adding throughput capacity. But WP-016's central argument is that queue formation is a symptom of integration failure, not a primary pathology. A system with high integration quality — where preventive contacts identify risk early, where multi-morbidity patients receive coordinated care plans, where referrals match clinical need — does not accumulate the same queue structure as a system with low integration quality operating at identical capacity.

The Kela experiment cannot measure whether it is improving integration quality or simply relocating the backlog. A patient routed to a private specialist through Kela reimbursement may receive excellent episodic care and return to primary care without the episode being integrated into a longitudinal care plan. The HDCI DCI and IAI components would capture this: a high-volume but low-integration care pathway scores poorly regardless of throughput. The experiment, as designed, has no such instrument.

This is not an argument against the Kela experiment. It is an argument that the experiment should be instrumented with HDCI-type metrics to distinguish between two outcomes that look identical in queue-length data: genuine integration improvement versus capacity substitution without coordination gain. The Finnish data infrastructure — Kanta, Kela compensation registers, THL Avohilmo — can support this instrumentation without new data collection. It requires a measurement decision, not a technical investment.

HDCI Calculation Formulas and Data Sources

DCI = (actual_kanta_entries) / (expected_kanta_entries)
      expected = f(morbidity_index, age, sex)
      Source: Sotkanet 5642 · THL Avohilmo · Findata licence

IAI = (multi_specialty_entries_per_episode) / (total_entries)
      Source: THL Avohilmo + Terveys-Hilmo (patient-level linkage)

RVI = 1 - (latency_days / max_latency_threshold)
      latency = first_care_action - first_recorded_finding
      Source: THL Hilmo · Avohilmo timestamps

RKI = (patients: ≥5 drugs, ≥2 prescribers, no shared care plan) /
      (patients: ≥5 drugs)
      Source: Kela lääkekorvausrekisteri · Kanta care plan register

PAI = (preventive_contacts) / (total_contacts)
      preventive = screenings + risk assessments + lifestyle counselling
      Source: THL Avohilmo procedure codes
      Weight: 0.05 (input-effort indicator only)

RKI_raw = (patients: ≥5 drugs, ≥2 prescribers, no shared care plan) /
          (patients: ≥5 drugs)
RKI_adjusted = RKI_raw / Kanta_care_plan_adoption_rate
      Use RKI_adjusted in composite

HDCI_v1 = 0.40·RKI_adjusted + 0.35·IAI + 0.25·RVI
       v0.7 three-component core — Bayesian priors subject to outcome-constrained re-estimation

DCI = actual_Kanta_entries / expected_Kanta_entries  [side indicator, not in composite]
PAI = preventive_contacts / total_contacts            [dashboard metadata, not in composite]

v0.7 revision: DCI and PAI removed from composite due to endogeneity (DCI) and input-effort-only status (PAI). HDCI_v1 is the three-component integration stress index. DCI and PAI reported separately. See §6 for full identifiability analysis.