Evidence map›Paper›PMID 30659819›Full record

ReviewChest2019

Hierarchical Condition Categories for Pulmonary Diseases: Population Health Management and Policy Opportunities.

Gary E Weissman

Abstract readReview
In one paragraph

Review in Chest, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Gary E WeissmanPulmonary, Allergy, and Critical Care Division, Perelman School of Medicine, Palliative and Advanced Illness Research Center, Department of Medicine, Perelman School of Medicine, and the Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA. Electronic address: gary.weissman@uphs.upenn.edu.

Funding

Using natural language processing and machine learning to identify potentially preventable hospital admissions among outpatients with chronic lung diseasesK23HL141639 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI WEISSMAN, GARY · 2018 to 2022
$933k
NHLBI NIH HHS K23 HL141639NHLBI NIH HHS L30 HL138788
6 · The paper itself

Abstract

Hierarchical condition categories (HCCs) are groups of diagnostic codes that are used to adjust federal payments to insurers and health systems based on differences in expected spending. Risk models built on HCCs improve on previous adjustment strategies that used demographic characteristics but did not include clinical diagnoses. Thus, accurate coding by clinicians of inpatient and outpatient encounters ensures capitated payments and reimbursements that are commensurate with predicted expenditures. Pulmonary diseases and various forms of critical illness play a significant role in this risk adjustment process both through their associated HCC codes and through interactions with other risk categories representing cardiac and psychiatric diseases. Ongoing uncertainty in federal health policy ensures a changing role for HCCs and risk-adjusted reimbursements across a variety of payment models and federal programs.

Indexed as

Health ExpendituresHealth PolicyPopulation Health ManagementCritical CareHumansLung DiseasesUnited Statescritical carehealth administrationhealth-care utilizationpoliticspulmonary

Identifiers

PMID30659819
PMCPMC6514430

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.