Prediction of 30-day pediatric unplanned hospitalizations using the Johns Hopkins Adjusted Clinical Groups risk adjustment system

AbstractThe COVID-19 pandemic has led to disruptions in healthcare utilization and spending. While some changes might persist (e.g. substitution of specialist visits by online consultations), others will be transitory (e.g. fewer surgical procedures due to cancellation of treatments). This paper discusses the implications of transitory changes in healthcare utilization and spending for risk adjustment of health plan payment. In practice, risk adjustment methodologies typically consist of two steps: (1) calibration of payment weights for a given set of risk adjusters and (2) calculation of payments to insurers by combining the calibrated weights with enrollee characteristics. In this paper, we first introduce a simple conceptual framework for analyzing the (potential) distortions from the pandemic for both steps and then provide a hypothetical illustration of how these distortions can lead to under- or overpayment of insurers. The size of these under-/overpayments depends on (1) the impact of the pandemic on patterns in utilization and spending, (2) the distribution of risk types across insurers, (3) the extent to which insurers are disproportionately affected by the pandemic, and (4) features of the risk adjustment system.

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Development and Validation of a Disease-Specific Risk Adjustment System Using Automated Clinical Data

Health Services Research ◽

10.1111/j.1475-6773.2010.01126.x ◽

2010 ◽

Vol 45 (6p1) ◽

pp. 1815-1835 ◽

Cited By ~ 28

Author(s):

Ying P. Tabak ◽

Xiaowu Sun ◽

Karen G. Derby ◽

Stephen G. Kurtz ◽

Richard S. Johannes

Keyword(s):

Clinical Data ◽

Risk Adjustment ◽

Specific Risk ◽

Adjustment System ◽

Development And Validation ◽

Disease Specific

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Severity of illness scoring systems

10.1093/med/9780199600830.003.0029 ◽

2016 ◽

Author(s):

Graeme K. Hart ◽

David Pilcher

Keyword(s):

Risk Adjustment ◽

Time Course ◽

Absolute Risk ◽

Severity Of Illness ◽

Large Data ◽

Scoring Systems ◽

Data Sets ◽

Course Of Illness ◽

Mortality Outcome ◽

Adjustment System

Clinical outcome comparisons for research and quality assurance require risk adjustment measures validated in the population of interest. There are many scoring systems using intensive care unit (ICU)-specific or administrative data sets, or both. Risk-adjusted ICU and hospital mortality outcome measures may be not granular enough or may be censored before the absolute risk of the studied outcome reaches that of the population at large. Data linkage methods may be used to examine longer-term outcomes. Organ failure scores provide a method for assessing the intra-episode time course of illness and scores using treatment variables may be useful for assessing care requirements. Each adjustment system has specific merits and limitations, which must be understood for appropriate use. Graphical representations of the comparisons facilitate understanding and time-appropriate response to variations in outcome. There are, as yet, no universally-accepted measures for severity of illness and risk adjustment in deteriorating patients outside the ICU.

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