Psychosomatics 42:106-109, April 2001
© 2001 The Academy of Psychosomatic Medicine
INTERMED—A Clinical Instrument for Biopsychosocial Assessment
Peter de Jonge, Ph.D.,
Frits J. Huyse, M.D.,
Friedrich C. Stiefel, M.D.,
Joris PJ Slaets, M.D., and
Rijk OB Gans, M.D.
Received March 3, 2000; revised June 30, 2000; accepted October 25, 2000. From Psychiatrische Consultatieve Dienst, Ziekenhuis der Vrije Universiteit, Amsterdam, The Netherlands; Service de Psychiatrie de Liaison, University Hospital, Lausanne, Switzerland; and Algemene Inwendige Geneeskunde, Academisch Ziekenhuis Groningen, Groningen, The Netherlands. Address correspondence and reprint requests to Dr. de Jonge, Psychiatrische Consultatieve Dienst, Ziekenhuis der Vrije Universiteit, De Boelelaan 1117, P.O. Box 7057, 1007 MB Amsterdam, The Netherlands.
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ABSTRACT
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Using the INTERMED, a system for classifying case complexity, the authors evaluated patients admitted to a general internal medicine ward on length of stay (LOS), number of medicines prescribed during the hospital stay, and whether they had received specialist medical consults. Using the patients' INTERMED scores, the authors divided the patients into three clusters of patients: standard (n=41), chronic (n=26), and complex (n=18). A comparison of the three clusters indicated that patients who had scored within the complex cluster were at risk of requiring complex care and an increased LOS. The findings suggest that the INTERMED detects complex patients at admission and may, therefore, be used for early integral case management.
Key Words: Case Complexity • Health Care Utilization
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INTRODUCTION
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Research suggests that a comprehensive understanding of patients increases the effectiveness of health care delivery in medical inpatients.1 Still, there is not a method of integrating biological-, psychological-, social-, and health care-related aspects of disease. Therefore, we developed an instrument, the INTERMED, that synthesizes patient information using a clinical interview. The development, selection of variables, reliability, and validity of the instrument have been published elsewhere.2,3,10 In two studies, one based on a joint interview of 14 patients admitted to a general internal medicine ward and another based on a medical chart review of 16 patients referred to an outpatient psychiatric consultation service, we found that in 94.2% of all ratings no important differences were present between the ratings of a psychiatrist and an internist. A validity study3 that compared the scores of 100 low back pain patients on the INTERMED with their scores on comprehensive validated instruments (e.g., the SF36 and HADS), revealed significant concordance between the INTERMED and the other instruments. Applications in other populations of patients with somatic and psychosocial comorbidities, such as diabetes and advanced cancer have demonstrated the INTERMED's utility.5,9 The INTERMED can be used to describe care needs of patients with decreased response to standard biomedical treatment, design multimodal treatment, and control for confounding variables.12
In our present study, the INTERMED was used to detect medical inpatients who had an increased risk of requiring complex care within the first days of admission. A method for early detection is necessary for the implementation of integral treatment strategies for such patients, during the inpatient period and after discharge.
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METHODS
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Patients and Assessments
From May–July 1997, we assessed a sample of 89 consecutively admitted patients on a general internal medicine ward. The INTERMED was scored within the first 2 days of admission, resulting in 85 complete cases. To assess the generalizability of the results we compared the study sample with recently collected data on 274 patients from the same ward and did not find any major differences (study sample; age: mean±SD=65.4±19.0; gender: 57% male; length of stay (LOS): mean±SD=14.5±13.7; recent data; age: mean±SD=60.5±20.3; gender: 51% male; LOS: mean±SD=13.2±16.0).
The patients were evaluated for their LOS, the number of medications prescribed during their hospital stay, and whether they received specialist (para-)medical consults. In addition, if applicable, a list of the following 13 indicators for the nursing intensity was scored daily: patient fully bedridden, fully limited functional status, more than standard monitoring, neurological monitoring, intravenous lines, nasal tube feeding, total parenteral nutrition, nasal oxygen, drains, airway cleaning, special care for wounds and scars, artificial respiration, and hemofiltration technique). The number of nonstandard nurse care indicators were then summed over the total hospital stay.
INTERMED
The INTERMED is an observer-rated instrument that integrates information from four domains: biological, psychological, social and health care (Figure 1), and it is based on a structured medical history taking. The domains are assessed in the context of time (history, current state, and prognosis) and contain variables that influence the degree of case complexity. Within each cell, two variables are rated by means of a scoring system ranging variable from 0 to 3, with an increasing score indicating an increasing degree of disturbance. The INTERMED is designed to be used in inpatient and outpatient settings, and it takes about 20 min to score by a trained clinician. Because the reliability testing had been conducted in a very heterogeneous population, it is difficult to assess to what extent reliability has been affected in the present study. However, because part of the reliability study and our present study were conducted on the same ward, we expect only limited influence.
Statistical Analysis
Our goal was to identify and group patients with similar INTERMED profiles into clusters and to link these clusters to clinically relevant variables. We applied hierarchical cluster analysis, a nonparametric method to identify patients most similar to each other based on Euclidean distances on all specified variables.6 We used Ward's method, which is commonly applied in the social sciences, to optimize the cluster solution by minimizing the variance within clusters by taking into account the averaged similarity in the set of observations. Aldenderfer and Blashfield6 indicated that the number of appropriate clusters to describe the sample can be obtained by plotting the average distances between the clusters and the number of clusters. Such a plot often results in one (or more) discontinuity indicating the "ideal point" with a minimum of clusters and cluster distances. Following Aldenderfer and Blashfield's analysis, we plotted the average distances between clusters to the number of clusters found in order to reach the optimal balance of number of clusters and the distances among them.
We then linked the clusters to a series of indicators for hospital-based care utilization. We used analysis of variance (one-way and multivariate) and chi-square tests (for proportions) to test if significant differences among the patient clusters exist.
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RESULTS
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After hierarchical cluster analysis, we grouped the patients into three clusters. Using their INTERMED variables, the clusters were interpreted as consisting of standard, chronic, or complex patients. Patients in the standard cluster most frequently received low scores (0 or 1) on all variables, and the patients in the complex clusters most frequently received high scores (2 or 3). Only on the variables "chronicity" and "intensity of prior treatment" did most patients from the chronic cluster have high scores, the remaining variables were similar to the standard cluster. Four examples of the scoring system, one variable from each domain, are listed in (Figure 2).
Comparison of the patient clusters on care utilization (Table 1) indicated that patients from the complex cluster are at risk of requiring complex care and had an extended LOS. A multivariate test of the differences (Hotellings) also resulted in a significant difference among the clusters (P=0.02). Direct comparisons of the clusters using t-tests and chi-square tests resulted in differences between the complex cluster and the standard cluster on all variables and between the complex cluster and the chronic cluster on LOS and nursing care interventions. There were no significant differences between the chronic and standard cluster.
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DISCUSSION
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The INTERMED helped to detect an at-risk relevant group of internal medicine patients upon admission for whom integrated care or case management could be considered during their stay and for whom postdischarge management may be necessary to prevent readmissions unrelated to somatic parameters. In the literature, we found support for the effectiveness of several types of interdisciplinary interventions in medical inpatients.1,7,8 The INTERMED may be a valuable tool for enhancing the effectiveness and efficacy of such interventions as it allows for targeting a relevant intervention group.11 Our study stresses the importance of comprehensive assessment and draws attention to complex factors influencing outcomes of biomedical treatments in medical patients.
An illustration of how INTERMED patient clusters differ on other variables is shown in a study conducted in patients with chronic low back pain.4 Response to treatment, as defined by return to work or a decrease of at least 50% in medical consultations at 6 months follow-up, was significantly related to INTERMED clusters. In the noncomplex cluster of patients, a positive response occurred in 63%, and in the complex cluster, a positive response occurred in 18%.
Because there is no available information on whether using the INTERMED actually improves patient care for complex patients, intervention studies comparing care given with the INTERMED to care without using the INTERMED in a randomized setting should be conducted. If biopsychosocial interventions, for example, in medical patients, geriatric patients, patients with diabetes, or patients with chronic low back pain, prove to be successful, then these interventions will have major implications for medical education and health care delivery.
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ACKNOWLEDGMENTS
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The authors would like to thank Anja M Willems, Bell Moerlie, and Arno HC van der Meys for their valuable work in the data collection.
This study was Supported by the European Union Biomed 1 Grant BMH1-CT93–1180 and the Swiss National Foundation Grant 3232–42162.95.
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REFERENCES
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Curley C, McEachern JE, Speroff T: A firm trial of interdisciplinary rounds on the inpatient medical wards. Medical Care 1998; 36:AS4-AS12
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Huyse FJ, Lyons JS, Stiefel FC, et al: INTERMED: a method to assess health service needs: I. Development and reliability. Gen Hosp Psychiatry 1999; 21:39–48[CrossRef][Medline]
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Stiefel FC, de Jonge P, Huyse FJ, et al: INTERMED: a method to assess health service needs: II. Results on its validity and clinical use. Gen Hosp Psychiatry 1999; 21:49–56[CrossRef][Medline]
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Stiefel FC, de Jonge P, Huyse FJ, et al: INTERMED: an assessment and classification system for case complexity: Results in patients with low back pain. Spine 1999; 24:378–385[CrossRef][Medline]
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Mazzocato C, Stiefel FC, de Jonge P, et al: Comprehensive assessment of patients in palliative care: a descriptive study utilizing the INTERMED. Journal of Pain and Symptom Management 2000; 19:83–90[CrossRef][Medline]
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Aldenderfer MS, Blashfield RK (eds): Cluster Analysis. Series: Quantitative Applications in the Social Sciences. Beverly Hills, CA, Sage Publications, 1984
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Slaets JPJ, Kauffmann RH, Duivenvoorden HJ, et al: A randomized trial of geriatric liaison intervention in elderly medical inpatients. Psychosom Med 1997; 59:585–591[Abstract/Free Full Text]
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Strain JJ, Lyons JS, Hammer JS, et al: Cost offset from a psychiatric consultation-liaison intervention with elderly hip fracture patients. Am J Psychiatry 1991; 148:1044–1048
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Fischer CJ, Stiefel FC, de Jonge P, et al: Case complexity and clinical outcome in diabetes mellitus: a prospective study using the INTERMED. Diabetes and Metabolism 2000; 26:295–302
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Huyse FJ, Lyons JS, Stiefel F, et al: Operationalizing the biopsychosocial model: The INTERMED. Psychosomatics 2000; 42:5–13[Free Full Text]
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de Jonge P, Huyse FJ, Ruinemans GMF, et al: The timing of psychiatric consultation: the impact of social vulnerability and level of psychiatric dysfunction. Psychosomatics 2000; 41:505-511[Abstract/Free Full Text]
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Huyse FJ, de Jonge P, Lyons JS, et al: INTERMED: a tool for controlling for confounding variables and designing multimodal treatment. J Psychosom Med 1999; 46:401–402
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ABSTRACT
INTRODUCTION
