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Background: Self-management support is a key component of effective chronic care management, yet in practice appears to be the least implemented and most challenging. This study explores whether and how self-management support is integrated into chronic care approaches in 13 European countries. In addition, it investigates the level of and barriers to implementation of support strategies in health care practice.
Methods: We conducted a review among the 13 participating countries, based on a common data template informed by the Chronic Care Model. Key informants presented a sample of representative chronic care approaches and related self-management support strategies. The cross-country review was complemented by a Dutch case study of health professionals’ views on the implementation of self-management support in practice.
Results: Self-management support for chronically ill patients remains relatively underdeveloped in Europe. Similarities between countries exist mostly in involved providers (nurses) and settings (primary care). Differences prevail in mode and format of support, and materials used. Support activities focus primarily on patients’ medical and behavioral management, and less on emotional management. According to Dutch providers, self-management support is not (yet) an integral part of daily practice; implementation is hampered by barriers related to, among others, funding, IT and medical culture.
Conclusions: Although collaborative care for chronic conditions is becoming more important in European health systems, adequate self-management support for patients with chronic disease is far from accomplished in most countries. There is a need for better understanding of how we can encourage both patients and health care providers to engage in productive interactions in daily chronic care practice, which can improve health and social outcomes.
Objectives: This study explores whether and how self-management support (SMS), a key element of well-coordinated chronic care, is integrated into existing chronic care approaches in 13 European countries.
Methodology: An expert review was conducted using a data template informed by the Chronic Care Model. Key informants (researchers and policymakers) from the 13 countries presented a sample of exemplary chronic care approaches and related SMS strategies. This was complemented by interviews with 27 Dutch care professionals investigating SMS implementation in practice.
Results: SMS remains relatively underdeveloped in Europe. Country-specific strategies are similar in involved providers (nurses) and settings (primary care), yet differ considerably in mode, format and materials used. SMS focuses mainly on patients medical and behavioral management, and less on emotional management. According to Dutch providers, barriers in financing and medical culture (e.g. length of consultation, patient-doctor communication) hamper implementation of SMS as an integral part of chronic care.
Conclusion: While Europe might increasingly be talking the talk of patient participation in chronic care, it appears far from walking the walk. Care professionals experience difficulties in operationalizing SMS in their daily routines. Stronger integration with the health promotion field may help patients and professionals to engage in productive partnerships.
Background: To improve and assess the effectiveness of disease management programs (DMPs), it is critical to understand how many people drop out of disease management programs and why.
Methods: We used routine data provided by a statutory health insurance fund from the regions North Rhine, North Wurttemberg and Hesse. As part of the German DMP for type 2 diabetes, the insurance fund received regular documentation of all members participating in the program. We followed 10,989 patients who enrolled in the DMP between July 2004 and December 2005 until the end of 2007 to study how many patients dropped out of the program. Dropout was defined based on the discontinuation of program documentation on a particular patient, excluding situations in which the patient died or left the insurance fund. Predictors of dropout, assessed at the time of program enrolment, were explored using logistic regression analysis.
Results: 5.5% of the patients dropped out of the disease management program within the observation period. Predictors of dropout at the time of enrolment were: region; retirement status; the number of secondary diseases; presence of a disabling secondary disease; doctors recommendations to stop smoking or to seek nutritional counselling; and the completion and outcome of the routine foot and eye exams. Different trends of dropout were observed among retired and employed patients: retired patients of old age, who possibly drop out of the program due to other health care priorities and employed people of younger age who have not yet developed many secondary diseases, but were recommended to change their lifestyle.
Conclusions: Overall, dropout rates for the German disease management programs for type 2 diabetes were low compared to other studies. Factors assessed at the time of program enrolment were predictive of later dropout and should be further studied to provide information for future program improvements.
Meeting Abstract : 10. Deutscher Kongress für Versorgungsforschung, 18. GAA-Jahrestagung. Deutsches Netzwerk Versorgungsforschung e. V. ; Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie e. V. 20.-22.10.2011, Köln
Hintergrund: Multimedikation als Folge von Multimorbidität ist ein zentrales Problem der Hausarztpraxis und erhöht das Risiko für unangemessene Arzneimittel-Verordnungen (VO). Um die Medikation bei älteren, multimorbiden Patienten zu optimieren und zu priorisieren, wurde eine computergestützte, durch Medizinische Fachangestellte (MFA) assistierte, komplexe Intervention (checklistengestütztes Vorbereitungsgespräch sowie Überprüfung eingenommener Medikamente durch MFA, Einsatz des web-basierten ArzneimittelinformationsDienstes AiD, spezifisches Arzt-Patienten-Gespräch) entwickelt und in einer 12-monatigen Pilotstudie auf Machbarkeit getestet. Ein auf 9 Items reduzierter MAI [1] wurde eingesetzt, um dessen Eignung als potentielles primäres Outcome der Hauptstudie zu prüfen.
Material und Methoden: In die Pilotstudie in 20 Hausarztpraxen mit Cluster-Randomisation auf Praxisebene in Kontrollgruppe (Regelversorgung b. empfohlenem Standard) vs. Interventionsgruppe (komplexe Intervention b. empfohlenem Standard) wurden 5 Pat./Praxis eingeschlossen (≥65 Jahre, ≥3 chron. Erkrankungen, ≥5 Dauermedikamente, MMSE ≥26, Lebenserwartung ≥6 Monate). Zur Bewertung des MAI wurden an Baseline (T0), 6 Wo. (T1) & 3 Mon. (T2) nach Intervention erhoben: VO, Diagnosen, Natrium, Kalium & Kreatinin i.S., Größe, Gewicht, Geschlecht, Cumulative Illness Rating Scale (CIRS) [2] durch die Hausarztpraxis; Symptome für unerwünschte Arzneimittelwirkungen im Patienten-Telefoninterview.
Für den MAI wurde die Angemessenheit jeder VO in den 9 Kategorien Indikation, Effektivität, Dosierung, korrekter & praktikabler Applikationsweg, Arzneimittelwechselwirkung, Drug-disease-Interaktion, Doppelverordnung, Anwendungsdauer 3-stufig bewertet (1 = korrekt - 3 = unkorrekt) und für die Auswertung auf Patientenebene summiert. Die Bewertung erfolgte ohne Kenntnis der Gruppenzugehörigkeit. Deskriptive Statistiken und Reliabilitätsanalysen, ungewichtete Auswertung und Gewichtung n. Bregnhoj [3].
Ergebnisse: Es wurden N=100 Patienten in die Studie eingeschlossen, im Mittel 76 Jahre (Standardabweichung, SD 6; Range, R: 64-93) , 52% Frauen, durchschnittlich 9 VO/Pat. (SD 2; R 4-16), mittlerer CIRS-Score 10 (SD 4; R 0-23). Basierend auf N=851 VO (100 Pat.) zu T0 betrug der Reliabilitätskoeffizient (RK, Cronbachs Alpha) der ungewichteten 9 Items 0,70. Items 1-5 wiesen akzeptable Trennschärfen auf (0,52-0,64), die der Items 6, 7 & 9 fielen mit 0,21-0,29 niedriger aus, die des Item 8 betrug 0,06. Auf der Basis der 9 gewichteten Items fiel die interne Konsistenz des MAI erwartet höher aus (0,75). Die Reliabilitätsanalysen auf VO-Ebene zeigten einen RK von 0,67 (ungewichtet) vs. 0,75 (gewichtet), die Trennschärfen waren vergleichbar. Zur Zwischenauswertung betrug der MAI (T1-T0) in der Interventionsgruppe (5 Praxen, 24 Pat.) -0,9 (SD 5,6), in der Kontrollgruppe (7 Praxen, 35 Pat.) -0,5 (SD 4,9); die Differenz zwischen beiden Gruppen Mi–Mk -0,4 [95% Konfidenzintervall: -3,4;2,6].
Schlussfolgerung: Der MAI ist als potentielles primäres Outcome in der Hauptstudie geeignet: wenige fehlende Werte, Darstellung von Unterschieden prä-post und zwischen den Gruppen, akzeptable interne Konsistenz. Der niedrige Trennschärfekoeffizient des Items 8 weist darauf hin, dass dieses Item nicht mit dem Gesamt-Skalenwert korreliert, auch die Items 6, 7 und 9 korrelieren wesentlich schwächer mit dem Gesamt-Skalenwert als die Items 1 bis 5. Eine Wichtung z.B. der Items 2, 5, 6 und 9 könnte erwogen werden, um den Fokus der Intervention in der Hauptzielgröße angemessen abzubilden.