Group Health Claims Analysis · Indonesia · Feb 2023 – Feb 2024
A composite risk-scoring analysis of 554 insured members across Rp 4.34 billion in annual claims — revealing the structure of cost concentration and identifying segments for targeted intervention.
Part I — Overview
The group health plan covers ... members, generating over ... claims totalling ... over the twelve-month analysis period. While aggregate figures appeared within expected ranges, a member-level view revealed a pronounced concentration of claims costs within a small sub-population.
Standard utilisation reports present averages — and averages, by definition, smooth over outliers. The objective of this analysis was to move beyond averages: to map each claim to each member, apply a composite risk score, and surface the segments driving disproportionate spend.
The analysis covers February 2023 through February 2024, across all benefit categories — inpatient, outpatient, dental, and maternity. Every claim, every member, every rupiah — mapped and scored.
Analysis Parameters
Analysis Period
...
Population Size
...
Total Claims
...
Chronic Condition Rate
...
Avg Claims Cost per Member
...
Member population · Total cost vs. claim frequency · Segmented by risk tier
Each point represents one member. Hover for member-level detail. Position reflects actual claims count and total cost; points within the same tier are offset slightly for readability.
Part II — Cost Distribution
Healthcare cost concentration is a well-documented phenomenon — utilisation tends to cluster among a subset of any insured population. The degree of that concentration, however, varies significantly across groups and determines how targeted an intervention strategy needs to be.
This degree of concentration has a direct practical implication: the upper-risk segment is small enough to be individually case-managed, yet large enough that targeted intervention would produce a material reduction in overall plan spend.
Disaggregating by relationship type reveals a notable imbalance: spouses account for ...% of the covered population but ...% of total claims cost — a per-capita cost ratio of ...× relative to the plan average. This gap is ... per covered person compared to employees.
Covered children, by contrast, consume only ...% of total cost despite representing ...% of the population — a cost ratio of 0.61×. These structural differences suggest that relationship type should be incorporated as a variable in any intervention targeting model.
Member share vs. cost share · By relationship type
Bars to the left represent population share; bars to the right represent cost share. Deviation between the two indicates cost efficiency relative to plan average.
Part III — Risk Stratification
We move beyond simple "top spenders" lists. By mapping every member onto a risk matrix, we instantly separate those requiring immediate clinical intervention from those whose risk is driven primarily by transactional volume.
Population distribution — 554 members by tier
Cost concentration · Member share vs. spend share
Tier 1 & 3 represent only 26% of members, but account for 74% of total healthcare expenditure.
Benefit Type Breakdown
Inpatient admissions are the primary cost driver by episode value. They account for a disproportionate share of total expenditure, at an average of Rp 16M per admission. These high-severity events are the primary target of Tier 3 intervention planning.
Dental claims (21.5% of volume, 16.2% of cost) are the inverse — high frequency but well-controlled cost per episode. The dental benefit shows a favourable cost efficiency ratio relative to other categories.
Claim volume vs. cost share by benefit category
Part IV — Chronic Condition Profile
Of 554 members, 99 carry at least one chronic condition flag. The 52 members with a positive Charlson Comorbidity Index (CCI) score represent the clinically complex sub-population. Understanding the distribution of diagnoses informs the design of condition-specific outreach and care coordination programmes.
Leading diagnoses · Claim volume and episode cost · ICD-10 coded
Bubble size = total cost contribution. Position = claim frequency (x-axis) vs. average cost per claim (y-axis). Both axes are log-scaled to accommodate the range.
CCI-Positive Conditions — 52 members
Charlson Comorbidity Index conditions flagged across the 52 CCI-positive members. A member may carry more than one condition.
Additional Chronic Flags — Non-CCI
Chronic conditions not captured by CCI but flagged in claims data. These conditions increase future utilisation risk.
Part V — Provider Utilisation
Five providers account for 38.3% of total claims. Mapping utilisation geographically surfaces both volume concentration and cost-per-claim variation — factors relevant to network contracting and referral management.
Provider locations · Jakarta metropolitan area · Scaled by claim volume
Provider cost share — top 5 by claims paid
The leading provider accounts for 576 claims (28% of volume) and Rp 677.5M in payments — representing 15.6% of total plan spend. The top three providers combined represent 28.7% of total expenditure. This concentration creates leverage for volume-based contract negotiation.
Network Strategy Considerations
Review volume-pricing agreements with the leading provider. Evaluate cost-per-claim variation across the network. Assess whether referral patterns can be optimised to favour providers with lower severity-adjusted costs.
Part VI — Model Performance
No classification model is without error. In healthcare risk scoring, the nature of acceptable error depends on the intervention budget and programme design. A higher classification threshold improves precision — flagging fewer members but with greater confidence. A lower threshold improves recall — capturing more at-risk members at the cost of a higher false-positive rate.
Fewer members flagged, with higher confidence in each classification. Intervention resources are concentrated on confirmed high-risk cases. Trade-off: members with atypical risk profiles may be missed.
A wider net captures more at-risk members, reducing the rate of missed cases. Trade-off: higher false-positive rate increases intervention volume and programme cost per outcome.
Part VII — Implementation
Risk stratification produces value only when it is operationalised. The following phased plan translates classification outputs into structured interventions, prioritised by tier and time horizon.
Projected Impact — Year 1