Prospective payment systems and discretionary coding - Evidence from English mental health providers

TY - JOUR

T1 - Prospective payment systems and discretionary coding - Evidence from English mental health providers

AU - Moscelli, Giuseppe

AU - Jacobs, Rowena

AU - Gutacker, Nils

AU - Aragon Aragon, Maria Jose Monserratt

AU - Chalkley, Martin John

AU - Mason, Anne

AU - Boehnke, Jan Rasmus

N1 - © 2018 The Authors

PY - 2019/3

Y1 - 2019/3

N2 - Reimbursement of English mental health hospitals is moving away from block contracts and towards activity and outcome-based payments. Under the new model, patients are categorised into 20 groups with similar levels of need, called clusters, to which prices may be assigned prospectively. Clinicians, who make clustering decisions, have substantial discretion and can, in principle, directly influence the level of reimbursement the hospital receives. This may createincentives for up-coding. Clinicians are supported in their allocation decision by a clinical clustering algorithm, the Mental Health Clustering Tool (MHCT), which provides an external reference against which clustering behaviour can be benchmarked. The aims of this study are to investigate the degree of mismatch between predicted and actual clustering and to test whether there are systematic differences amongst providers in their clustering behaviour. We use administrative data for all mental health patients in England who were clustered for the first time during the financial year 2014/15 and estimate multinomial multilevel models of over-, under- or matching clustering. Results suggest that hospitals vary systematically in their probability of mismatch but this variation is not consistently associated with observed hospital characteristics.

AB - Reimbursement of English mental health hospitals is moving away from block contracts and towards activity and outcome-based payments. Under the new model, patients are categorised into 20 groups with similar levels of need, called clusters, to which prices may be assigned prospectively. Clinicians, who make clustering decisions, have substantial discretion and can, in principle, directly influence the level of reimbursement the hospital receives. This may createincentives for up-coding. Clinicians are supported in their allocation decision by a clinical clustering algorithm, the Mental Health Clustering Tool (MHCT), which provides an external reference against which clustering behaviour can be benchmarked. The aims of this study are to investigate the degree of mismatch between predicted and actual clustering and to test whether there are systematic differences amongst providers in their clustering behaviour. We use administrative data for all mental health patients in England who were clustered for the first time during the financial year 2014/15 and estimate multinomial multilevel models of over-, under- or matching clustering. Results suggest that hospitals vary systematically in their probability of mismatch but this variation is not consistently associated with observed hospital characteristics.

KW - Mental health

KW - hospitals

KW - episodic payment

KW - classification

KW - discretionary behaviour

KW - mixed-effects models

KW - mental health

UR - http://www.scopus.com/inward/record.url?scp=85059196708&partnerID=8YFLogxK

U2 - 10.1002/hec.3851

DO - 10.1002/hec.3851

M3 - Article

C2 - 30592102

SN - 1057-9230

VL - 28

SP - 387

EP - 402

JO - Health Economics

JF - Health Economics

IS - 3

ER -