Predictive modeling in health care (2)

Data-Driven Approaches for Health Care Machine Learning for Identifying High Utilizers

Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program.

Five years ago I explained in this blog our experience on predictive modeling. This a key reference book.

Predictive modeling in health care

Predicting Patients with High Risk of Becoming High-Cost Healthcare Users in Ontario (Canada)
 
Predicción del riesgo individual de alto coste sanitario para la identificación de pacientes crónicos complejos

Two articles appear on the same topic, published at the same time, in Canada and Catalonia (I am coauthor of the latter). The results of both studies are similar. Their goal is to identify those patients that will belong to the highest spenders next year.
Canada results:

If the top 5% patients at risk of becoming HCUs are followed, the achieved sensitivity and specificity is 42.2% and 97%, respectively. These values suggest very reasonable predictive power, indicating that the model picks up 42.2% of all high-cost healthcare users and correctly identifies 97% of those who are not high users.

Catalonia results:

En el modelo, todas las variables fueron estadísticamente significativas excepto el sexo. Se obtuvo una sensibilidad del 48,4% (intervalo de confianza [IC]: 46,9%-49,8%), una especificidad del 97,2% (IC: 97,0%-97,3%), un VPP del 46,5% (IC: 45,0%-47,9%) y un AUC de 0,897 (IC: 0,892-0,902).

The models are slightly different, while the results are close.

I suggest you have a look at them, predictive modeling is one of the main current topics of health services research. Some people consider that it is under the umbrella of Big Data, although it was born before such a term was created.

PS. A must read. Bob Evans, and The Undisciplined Economist: Waste, Economists and American Healthcare

PS. In memoriam: Gary S. Becker, 1930-2014. The Becker-Posner blog is terminated.

Predictive modeling in health care (2)

Analysing the Costs of Integrated Care: A Case on Model Selection for Chronic Care Purposes

How do you want to manage, with a rearview mirror or just looking forward? Big data allows to look forward with better precision. The uncertainty about the disease and about the cost of care is large when you enter in hospital from an emergency department. But, after the diagnosis (morbidity), could we estimate how much could cost an episode?. If so, then we could compare the expected cost and the observed cost on a continous process.
Right now this is possible. Check this article that we have just published and you'll understand that costs of different services according to morbidity can be reckoned and introduced in health management. This analysis goes beyong our former article, much more general. So, what are we waiting for? Big data is knocking at the door of health care management, predictive modeling is the tool.

Amazing concert by Caravan Palace in Sant Feliu de Guixols three weeks ago.

On predictive modeling

A better understanding of population morbidity allows to predict how such population will evolve. Currently there is an increasing interest on chronic care and a specific program has been set up. The potential tools available to define chronic populations have been presented and you can check them in this document.Although we do need more details, it is a first step in the right direction. However, I'm not so sure about the split of chronic care from integrated care. Why now?

Estimating individual life expectancy for alzheimer patients

Personalized predictive modeling for patients with Alzheimer's disease using an extension of Sullivan’s life table model

Alzheimer's disease is the most common type of dementia. Ageing is boosting its spread over populations. Eric Stallard et al. asked wether it was posible to estimate the residual total life expectancy (TLE) and its decomposition into disability-free life expectancy (DFLE) and disabled life
expectancy (DLE) for individual patients. It sounds really of interest, though it may seem unattainable.
Fortunately you may find succesful results in this article, it says:

Methods: We estimated a new SLT/L-GoM model of the natural history of AD over 10 years in the Predictors 2 Study cohort: N = 229 with 6 fixed and 73 time-varying covariates over 21 examinations covering 11 measurement domains including cognitive, functional, behavioral, psychiatric, and other symptoms/signs. Total remaining life
expectancy was censored at 10 years. Disability was defined as need for full-time care (FTC), the outcome most strongly associated with AD progression. All parameters were estimated via weighted maximum likelihood using data-dependent weights designed to ensure that the estimates of the prognostic subtypes were of high quality.
Goodness of fit was tested/confirmed for survival and FTC disability for five relatively homogeneous subgroups defined to cover the range of patient outcomes over the 21 examinations.

Results: The substantial heterogeneity in initial patient presentation and AD progression was captured using three clinically meaningful prognostic subtypes and one terminal subtype exhibiting highly differentiated symptom severity on 7 of the 11 measurement domains. Comparisons of the observed and estimated survival and FTC disability probabilities demonstrated that the estimates were accurate for all five subgroups, supporting their use in AD life expectancy calculations. Mean 10-year TLE differed widely across subgroups: range 3.6–8.0 years, average 6.1 years. Mean 10-year DFLE differed relatively even more widely across subgroups: range 1.2–6.5 years, average 4.0 years. Mean 10-year DLE was relatively much closer: range 1.5–2.3 years, average 2.1 years.

Excellent, good job from Duke University, where I did part of my PhD, using the same methodology Grade of Membership.

PS. My speech at the Economist's day.

Anders Zorn au Petit Palais



Health spending in late life

Predictive modeling of U.S. health care spending in late life

In US, it is said that a quarter of public expenditure for the elderly (Medicare) is spent in the last 12 months of life. Really what happens is that the last year is only close to 10% of the whole lifetime health spending. Anyway, a new article in Science highlights commmon misunderstandings on such figure and disentangles the fundamentals.

These common interpretations of end-of-life spending flirt with a statistical fallacy: Those who endup dying are not the same as those who were sure to die. Ex post, spending could appear concentrated on the dead, simply because we spend more on sicker individuals who have higher mortality—even if we never spent money on those certain to die within the year. Empirically, this suggests using predicted mortality, rather than ex post mortality, to assess end of-life spending.

Less than 5% of spending is accounted for by individuals with predicted mortality above 50%. The simple fact that we spend more on the sick—both on those who recover and those who die—accounts for 30 to 50% of the concentration of spending on the dead. 

Crucial conclusion:

In sum, although spending on the ex post dead is very high, we find there are only a few individuals for whom, ex ante, death is near certain. Moreover, a substantial component of the concentration of spending at the end of life is mechanically driven by the fact that those who end up dying are sicker, and spending, naturally, is higher for sicker individuals. Of course, we do not— and cannot—rule out individual cases where treatment is performed on an individal for whom death is near certain. But our findings indicate that such individuals are not a meaningful share of decedents. These findings suggest that a focus on end-of life spending is not, by itself, a useful way to identify wasteful spending. Instead, researchers must focus on quality of care for very sick patients.

Good article.

PS Eight years ago I made this presentation on estimates of costs of late life. The summary in this post (in catalan)

Club des Belugas - Never think twice

Profiling complex patients

 Use of Latent Class Analysis and k-Means Clustering to Identify Complex Patient Profiles

Instead of predictive modeling using costs, this is the right approach from a clinical point of view:

This cohort study analyzed the most medically complex patients within Kaiser Permanente Northern California, a large integrated health care delivery system, based on comorbidity score, prior emergency department admissions, and predicted likelihood of hospitalization, from July 18, 2018, to July 15, 2019. From a starting point of over 5000 clinical variables, we used both clinical judgment and analytic methods to reduce to the 97 most informative covariates. Patients were then grouped using 2 methods (latent class analysis, generalized low-rank models, with k-means clustering). Results were interpreted by a panel of clinical stakeholders to define clinically meaningful patient profiles.

And the figures below reflect these results.

Great article.

Figure 1.  Seven Patient Profiles Derived From Latent Class Analysis

 

Figure 2.  Comparison of k-Means Clustering With Latent Class Analysis (LCA)

Table 1.  Baseline and 1-Year Follow-up Characteristics of the Overall Population and by Patient Profile

Table 2.  Key Defining Features and Suggested Management Strategies for the 7 Clinical Profiles of Medically Complex Patients

El disseny de sistemes de pagament

 A Framework for the Design of Risk-Adjustment Models in Health Care Provider Payment Systems

A partir d'avui aquest blog es trasllada a Substack. Durant unes setmanes serà accessible simultàniament per blogger i per substack. Anoteu l'adreça: econsalut.substack.com

Article resumit amb IA.

Aquest article presenta un marc conceptual integral per al disseny de models d'ajust de risc (RA) en el context de models de pagament prospectiu a proveïdors d'assistència sanitària. L'objectiu és desenvolupar un marc que expliciti les opcions de disseny i les compensacions associades per tal de personalitzar el disseny de l'RA als sistemes de pagament a proveïdors, tenint en compte els objectius i les característiques del context d'interès.

Introducció (1-3): Durant les últimes dècades, els reguladors i els responsables polítics de la salut han fet esforços per millorar l'eficiència de la prestació d'assistència sanitària mitjançant la reforma dels sistemes de pagament a proveïdors. Específicament, l'eficiència s'ha perseguit mitjançant la introducció d'elements prospectius en els models de pagament, donant lloc a diversos Models de Pagament Alternatius (MPA) com els acords de qualitat alternatius i els pagaments agrupats. Aquests MPA tenen com a objectiu incentivar l'eficiència traslladant (part de) la responsabilitat financera dels pagadors als proveïdors. Una característica típica dels pagaments prospectius a proveïdors és que es basen en un "nivell de despesa normatiu" per a la prestació d'un conjunt predefinit de serveis a una determinada població de pacients. El nivell de despesa normatiu es refereix al nivell de despesa que "hauria de ser" depenent de la població de pacients d'un proveïdor, en lloc de la despesa observada. Un element clau en la determinació dels nivells de despesa normatius és la correcció de les diferències sistemàtiques en les necessitats d'assistència sanitària de les poblacions de pacients dels proveïdors, comunament coneguda com a ajust de risc (RA). L'RA és crucial per garantir un terreny de joc igualitari per als proveïdors i per evitar incentius per a comportaments no desitjats, com la selecció de riscos.

Nova Contribució (8-10): Tot i les contribucions conceptuals existents sobre el disseny de l'RA, actualment no hi ha un marc integral per adaptar el disseny de l'RA al pagament de proveïdors i a les característiques essencials del context. Aquest article desenvolupa aquest marc sintetitzant, ampliant i aplicant coneixements de la literatura existent. La metodologia va incloure una revisió de la literatura combinada amb consultes a experts en el camp de l'RA i els sistemes de pagament. La informació recopilada es va sintetitzar per desenvolupar el marc, del qual van sorgir tres criteris per al disseny de models d'RA i es van agrupar les opcions i les compensacions en dues dimensions principals: (a) la tria dels ajustadors de risc i (b) la tria de les ponderacions de pagament.

Definicions de Conceptes Clau (11-13): Els models de pagament prospectiu i els MPA traslladen la responsabilitat financera dels pagadors als proveïdors per tal d'incentivar el control de costos i l'eficiència. Qualsevol trasllat de responsabilitat financera requereix que el pagador determini el nivell de despesa normatiu, que reflecteix el nivell de despesa apropiat donades les necessitats d'assistència sanitària d'una població i els objectius dels MPA. El nivell de despesa normatiu no fa referència necessàriament al nivell de despesa absolut o òptim, sinó al nivell considerat apropiat donat el nivell/objectius d'eficiència perseguits pel MPA.

Fonts de Variació de la Despesa i el Paper de l'RA i la Mancomunació de Riscos (14-19): Quan s'estableixen nivells de despesa normatius, és important considerar tres fonts de variació de la despesa: (a) variació sistemàtica impulsada per factors fora del control dels proveïdors (variables C o "factors de compensació"), (b) variació sistemàtica impulsada per factors que els proveïdors poden influir (variables R o "factors de responsabilitat"), i (c) variació aleatòria. Per evitar que els proveïdors assumeixin riscos excessius que no poden influir, els MPA solen aplicar alguna forma de mancomunació de riscos. L'RA prospectiu s'utilitza per compensar la variació de la despesa deguda a les variables C. La naturalesa i el grau en què s'ha de compensar la variació de la despesa resultant de les variables C forma el punt de partida d'un model d'RA.

Tres Criteris per al Disseny de Models d'RA (19-26): L'objectiu general de l'RA en els MPA és compensar els proveïdors per la variació de la despesa deguda a les variables C, alhora que els manté responsables de la variació de la despesa deguda a les variables R. Això implica dos criteris clau: (a) compensació adequada per a les variables C i (b) cap compensació per a les variables R. Un tercer criteri important és la viabilitat.

• Criteri 1: Compensació Adequada per a les Variables C (20-26): Per evitar problemes de selecció, l'RA hauria de compensar adequadament les variables C que són rellevants a la llum de les possibles accions de selecció de riscos per part dels proveïdors (atraure/dissuadir pacients sans/no sans). També hauria de compensar les variables C que varien entre les poblacions de proveïdors per evitar la participació selectiva en el MPA.
• Criteri 2: Cap Compensació per a les Variables R (26-29): Per evitar ineficiències, l'RA no hauria de compensar les variables R. La compensació per la variació de la despesa de les variables R pot donar lloc a problemes d'eficiència, com la perpetuació de les ineficiències existents ("biaix d'status quo") i la creació d'incentius per a noves ineficiències (reducció dels incentius per al control de volum i preu, codificació ascendent).
• Criteri 3: Viabilitat (29-30): Un tercer criteri crucial és la viabilitat, que inclou la disponibilitat de dades i l'acceptació per part de totes les parts interessades (pacients, proveïdors, pagadors, reguladors).

Un Marc per al Disseny de Models d'RA (30-31): Aquest marc distingeix entre preguntes de disseny, opcions associades i consideracions i compensacions clau pel que fa a (a) la tria dels ajustadors de risc i (b) la tria de les ponderacions de pagament.

La Tria dels Ajustadors de Risc (31-47): Aquesta secció aborda tres preguntes principals de disseny:

• Quin tipus d'informació es basa els ajustadors de risc? (32-38): Les opcions inclouen informació demogràfica, socioeconòmica, subjectiva (de salut), diagnòstica, d'utilització, clínica, de despesa (retardada) i del costat de l'oferta. L'ús d'informació endògena (diagnòstics, utilització, despesa) és altament predictiu de la despesa de tipus C, però pot perpetuar ineficiències i introduir nous incentius perversos per al volum i el preu. L'ús d'informació exògena (demogràfica, socioeconòmica) no manté ni introdueix incentius perversos relacionats amb el volum o el preu, però el seu poder predictiu és generalment baix.
• A quin període de temps (període base) pertany la informació? (38-45): Es pot distingir entre ajustadors concurrrents i prospectius. Els efectes d'incentiu relatius d'aquestes opcions no estan clars a priori.
• Com dissenyar els ajustadors de risc? (46-47): Això inclou l'especificació de l'escala de mesura, l'operacionalització dels ajustadors (considerant condicions, jerarquies, restriccions) i les interaccions entre ajustadors.

La Tria de les Ponderacions de Pagament (48-60): Per trobar ponderacions de pagament apropiades, els responsables de la presa de decisions s'enfronten a tres decisions principals de disseny:

• Quina mostra d'estimació? (49-52): Es requereix una mostra d'estimació representativa de la població d'interès i dels nivells de despesa normatius. En la pràctica, sovint s'utilitzen dades històriques i poblacions de pacients similars.
• Quines intervencions de dades? (52-58): Quan la mostra d'estimació no és representativa, s'han de considerar intervencions de dades sobre la població de pacients i/o les dades de despesa per millorar la coincidència amb la població d'interès i el nivell de despesa normatiu. Això pot incloure correccions per biaixos i inequitats.
• Com derivar les ponderacions de pagament? (59-60): Això implica decidir quins ajustadors de risc incloure (considerant el biaix de la variable omesa) i quin criteri d'optimització utilitzar per estimar aquestes ponderacions. Les opcions van des de criteris d'optimització estàndard (OLS, GLM) fins a criteris personalitzats (regressió restringida, aprenentatge automàtic).

La Interconnexió Entre les Opcions de Disseny per als Ajustadors de Risc i les Ponderacions de Pagament (61-62): Les decisions de disseny dins i entre aquests dos temes estan altament interrelacionades. Per exemple, la tria de la informació en què es basen els ajustadors de risc afectarà la seva especificació i operacionalització. De la mateixa manera, les decisions sobre com es deriven les ponderacions de pagament depenen tant de la tria dels ajustadors de risc com de la tria de la mostra d'estimació (modificada).

Discussió (63-68): No hi ha un enfocament únic per al disseny de models d'RA, i el disseny adequat pot variar segons la configuració i les evolucions al llarg del temps. És crucial la decisió normativa sobre quines variables es consideren C i quines R. L'abast de la preocupació pels possibles incentius de selecció i control de costos pot variar segons el context. Les consideracions de viabilitat, com la disponibilitat de dades i l'acceptació de les parts interessades, també són importants.

Consideracions Més Amplies per al Disseny de l'RA en el Finançament de l'Assistència Sanitària (69-70): Tot i que aquest article se centra en el pagament a proveïdors, el marc proposat també podria beneficiar altres reformes de finançament, com les iniciatives de participació del consumidor, tot i que es necessita més recerca.

Conclusió (71): El disseny de models d'RA per a sistemes de pagament prospectiu a proveïdors és un exercici complex que requereix una consideració explícita de moltes preguntes, opcions i compensacions difícils. El procés de disseny ha de guiar-se per tres criteris clau: compensació adequada de les variables C, cap compensació de les variables R i viabilitat. Les diverses preguntes i opcions de disseny es poden classificar en la tria dels ajustadors de risc i la tria de les ponderacions de pagament. Es necessita més recerca per donar suport a les decisions normatives sobre les variables C i R, així com per desenvolupar mètriques d'avaluació integrals per a la valoració dels efectes dels incentius.

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