This article has Open Peer Review reports available.
Decision support at home (DS@HOME) – system architectures and requirements
© Marschollek.; licensee BioMed Central Ltd. 2012
Received: 16 September 2011
Accepted: 28 May 2012
Published: 28 May 2012
Demographic change with its consequences of an aging society and an increase in the demand for care in the home environment has triggered intensive research activities in sensor devices and smart home technologies. While many advanced technologies are already available, there is still a lack of decision support systems (DSS) for the interpretation of data generated in home environments. The aim of the research for this paper is to present the state-of-the-art in DSS for these data, to define characteristic properties of such systems, and to define the requirements for successful home care DSS implementations.
A literature review was performed along with the analysis of cross-references. Characteristic properties are proposed and requirements are derived from the available body of literature.
79 papers were identified and analyzed, of which 20 describe implementations of decision components. Most authors mention server-based decision support components, but only few papers provide details about the system architecture or the knowledge base. A list of requirements derived from the analysis is presented. Among the primary drawbacks of current systems are the missing integration of DSS in current health information system architectures including interfaces, the missing agreement among developers with regard to the formalization and customization of medical knowledge and a lack of intelligent algorithms to interpret data from multiple sources including clinical application systems.
Future research needs to address these issues in order to provide useful information – and not only large amounts of data – for both the patient and the caregiver. Furthermore, there is a need for outcome studies allowing for identifying successful implementation concepts.
Demographic change is induced and influenced by many factors. One of them is the desirable rising life expectancy, which is in turn due to e.g. better nutrition and improved medical care. The shift towards an elderly population with a significant proportion of people aged above 65 however also goes along with the challenge to maintain a stable level of care and care giving, knowing that the older a person becomes, the more likely she or he is to suffer from multiple chronic diseases. The Berlin Aging Study e.g. has shown that the prevalence rate of five or more somatic diseases was 88% in the group of persons aged 70 years and above . At the same time, the primary – and well comprehensible – wish of elderly persons is to remain in their homes as long as possible despite a possible need for professional care.
Bearing this in mind, health-enabling technologies, and smart home technologies in particular, have been identified as potential measures to alleviate the consequences that demographic change will effect on societies. These technologies offer support in terms of ‘patient empowerment', i.e. in a person’s own health management , making more health-related data available than ever before. By handing over a certain amount of responsibility to the person, she or he is supposed to be put in a position to make informed decisions in health care matters. On the other hand, care givers and physicians may profit from smart home technologies by getting more long-term information about a patient, enabling him to make his decisions on a broader basis of information.
Despite several large-scale funding schemes and a multitude of very successful projects that have been conducted, trial-based evidence about the long-term effects of smart home technologies on the quality of care and the quality of life is still scarce [3–5]. One reason for this lack of evidence may be that the multitude of valuable data about a person’s state of health cannot be analyzed and aggregated in a way that relevant information for caregiver, physician or patient is extracted, and that technology is not fully integrated in care workflows .
In clinical settings, data analytics and decision support systems (DSS) have been established in several data-rich environments, most notably e.g. in ECG interpretation  or in mechanical ventilation in intensive care units .
Considering the rising relevance of home-based, health-enabling technologies, the overall aim of the research for this paper is to investigate and present the state-of-the-art in decision support systems for health care purposes or patient support in home environments, and in particular
· to define characterizing properties for these systems with regard to their dispersion, architectures and integration into home care (aim#1) and apply these to the results of a literature analysis, and
· to define the requirements for home care decision support systems with regard to architectural and functional aspects (aim#2).
The rest of this paper is arranged as follows: The “Methods” section describes the methods used in the literature search and analysis. Within the “Results” section, the literature review results are presented, followed by a sub-section proposing a categorization scheme for the systems identified. Subsequently, the identified systems are described in more detail with regard to their properties and specific features. The “Results” section concludes with a sub-section defining requirements for decision support systems in the context of home environments. The results are discussed critically and the paper concludes with a brief summary and a suggestion for future lines of research.
The author has conducted a literature analysis in the PubMed/Medline database on June 17, 2011, using the search term
(‘decision support’ or ‘self-management’) and
(‘sensors’ or ‘smart’ or ‘tele’ or ‘telecare’ or ‘telemedicine’).
The abstracts of all research papers found during the search were reviewed with regard to their relevance for the aims of the study and using the following inclusion criteria:
· description of a dedicated decision support component,
· comprehensible implementation of a DS component, and
· application in a home environment.
Three main properties of the decision support systems identified
Autonomy of decision component
fully autonomous, real-time
HIS integration plans mentioned
cf. Joshi, Finkelstein
cf. Cross, Joshi
HIS integration plans mentioned
cf. Cross, Finkelstein
HIS integration plans mentioned, cf. Song, Helmer
HIS integration plans mentioned, cf. Helmer, Marschollek
Complying with the rules of Good Scientific Practice , all search results may be requested from the author.
The literature search in the PubMed/Medline database yielded 79 hits, of which 18 met the inclusion criteria. Two further papers were identified by analyzing cross-references, and seven review papers were used for the results and discussion sections (Figure 1). The systems identified showed a wide variation in terms of autonomy, integration into existing health information system (HIS) structures, ability to provide patient feedback, number of parameters considered in decision making, and complexity (Table 1).
All of the articles identified as relevant describe an approach of data forwarding, sometimes after preprocessing/ preparation. Despite several published ideas concerning the integration of home decision support with health information system components (e.g. [14, 23, 25]) and a partial implementation [9, 10], the author could not identify a fully integrated system.
13 out of 20 papers present multi-parametric data analysis components, six a single-parameter approach and one a descriptive analysis. No system performing autonomous decision-making in real-time was identified. Seven articles mention the deployment of at least a part of decision component in the actual home environment, yet the majority (16) places it on a central server unit (duplicate entries included).
Categories of home care DSS
Location of the DSS component: The analysis modules containing the medical logic, formally represented e.g. by rules [9, 13, 21, 27] or a Bayesian network , may be located on a central server system to which the data are transferred [12, 15] – maybe even in a clinical environment – or at the patient’s home . The location has implications with regard to the possibility to update or change the modules , for example if new knowledge/ guidelines are released or if a doctor wants to adjust default modules in order to individualize treatment.
Autonomy of the DSS: The system’s tasks may range from descriptive analysis (for example graphical presentation of raw data) via various steps of data reduction and information extraction to support experts in their decision making to nearly autonomous real-time decision systems .
Degree of HIS integration and adaptiveness:The DSS may be isolated from other application systems, if e.g. to analyze blood glucose values or trends at home, but it may also be connected to clinical information systems or a GP practice , tapping EHR data to refine analyses and recommendations based on additional medical context knowledge.
These three properties were used to assess and categorize the current range of DSS in home care, but other categories might be equally helpful. Table 1 shows the results of this categorization.
Relevance of DSS in home care
The importance of DSSs in the context of home care is stressed by five of the papers identified. Speedie et al. state that home-based physiological data can only be used to its full potential if clinical information from electronic health records (EHRs) is incorporated in the decision support process . This point is also made by Lymberis  and Koch, who states that research in ‘better integration of new knowledge about treatment into evidence-based decision support tools at the point of care’ (, p. 572) is critical in the context of home-telehealth (see also ). Hermens and Vollenbroek-Hutten furthermore stress that – by employing methods of artificial intelligence – patient feedback can be provided, but that this area still lacks more intensive research .
Only very few authors have actually published (parts of) their knowledge base, mostly in the form of production rules. Examples of published knowledge bases can be found in [12, 13, 30, 37]. The author has found even fewer reports aiming to standardize the decision logic, for example by using the Arden Syntax for Medical Logic Modules [21, 37, 38].
Single- and multi-parameter analysis systems
Bellazzi et al. provide a good example of how advanced analysis methods (time series analysis/ decomposition, Bayesian methods, temporal abstraction) can be used to interpret blood glucose data series for decision support in diabetes patients .
Among the six articles presenting systems for decision-making based on single parameter data in the context of specific diseases, the ‘Home Automated Telemanagement system’ (HAT) described by Finkelstein et al.  provides a good example. It consists of one or more home units, a HAT server and a clinician unit. The patient data are collected from home-based devices for self-testing, for example pulmonary parameters relating to asthma management , coagulation times for anticoagulation therapy  or assessment of symptoms of ulcerative colitis [15, 16], and then forwarded to a server system which analyzes the data. This is performed by a decision support module located on the server. The system also provides data presentation and tailored feedback for the patient. A similar system design is described for the HeartCycle project by Reiter and Maglaveras  and by Biddiss et al . The application domains are heart failure and coronary artery disease, and tailored advice is part of the approach Zheng et al. present the system structure of their Personalised Self Management System (PSMS) including a decision support ‘expert system’ [27, 28]. The system is used e.g. for patients suffering from stroke, chronic pain and heart failure. The DS module supports two services: alerting and feedback, for example for exercises of the upper limb using inertial sensors .
Goldsmith et al. describe the Pediatric Cancer CareLink system and stress the importance of its decision support component for on-time assessment of symptoms and early detection of unfavorable or dangerous situations (e.g. medication side effects),but – as e.g. Abraham and Rosenthal  – do not focus on the DSS architecture in detail .
Inclusion of context data and integration with existing HIS components
The above-mentioned HeartCycle project aims to record not only vital signs related to cardiovascular conditions, but also considers biochemical markers, questionnaires and other context information  for its decision support system. Bosworth et al. describe the design of an intervention trial which – amongst other interventions – employs a validated and already available DSS (ATHENA-HTN, http://www.openclinical.org/aisp_athena.html) to manage medication treatment in patients suffering from hypertension . The DSS not only considers home-based blood pressure measurement data, but also additional clinical information from the Veteran’s Administrations electronic health record system, including laboratory results, diagnoses and previous blood pressure values.
Our research group has developed a DSS architecture that merges data from intelligent environments such as smart homes and vital signs data using the Arden Syntax for Medical Logic Systems, a standardized medical logic language [23, 24]. The modular DSS has been implemented in a prototype for a scenario for exercise training of patients suffering from chronic obstructive pulmonary disease (COPD) using an OSGi middleware approach . Within this context, we have developed a decision support module based on a dynamic Bayesian network that controls exercise training autonomously based on vital signs data , so that it may be used in an unsupervised training situation at home. A primary focus of our research is the integration of the DSS in existing or new health information system infrastructures, thus enabling to consider clinical context information such as diagnoses, laboratory findings or past test results as well as caregiver and patient information for the purpose of decision making .
The design as well as the implementation of an advanced DSS infrastructure are presented by Basilakis et al. in  and later in . The system architecture integrates vital signs data with medications, patient questionnaire data and clinical data, and employs several different technologies for data analysis. ‘Input submodules’ perform data preprocessing and information extraction (e.g. using R scripts), followed by a rule-based interpretation using a professional production rules engine (JBoss) . Established clinical guidelines serve as the basis for the production rules, which are formalized using XML technologies. The system can generate health status reports and alarms and has been tested for patients suffering from COPD and chronic heart failure .
knowledge base (KB)/ medical logic:
o adaptiveness, self-learning algorithms
o KB/ decision algorithms should be (easily) comprehensible; system should provide a user interface for editing, versioning and auditing
should provide comprehensible explanations for the decisions taken
possibility to translate/ incorporate clinical guidelines 
With the advent of health-enabling technologies, especially sensor technologies and advanced data analysis methods , their gradual integration as supportive technologies in current care scenarios on the one hand , and on the other hand by enabling new care services , a gradual permeation of these technologies into actual health care and patients’ homes beyond purely scientific limits can be observed. The properties identified above may serve to arrange current decision support systems into different categories with regard to autonomy, localization and integration in current health information system structures (aim#1). First studies have been published, proving the effectiveness of technology usage in specific disease management scenarios using home-recorded data, for example in patients with heart failure [50, 51].
If the additional data that can be recorded using home-based or wearable technologies shall be used to generate more information resp. knowledge about a patient – and not lead to a data overload – the development of appropriate methods for analysis in terms of data reduction and information extraction are of paramount importance . Too much unprocessed data may lead to confusion and even disregard of data, and not to shedding light into areas relevant for decision making in health matters. This counts not only for health care providers (physicians, nurses) but also for the patient herself or himself and her or his relatives . Thus, ‘intelligent’ decision support systems or DS modules are necessary, and in fact are part of many system designs in the projects identified.
While the integration of solutions in health care settings is often ensured, details about the design of the decision support component, its contents and the methods used for decision making in most cases remain unclear. Furthermore, there is a lack of reports about the details of the DSS design infrastructure and about the formalization of the medical decision logic . On a more general scale this also holds true for the architectures of so-called sensor-enhanced health information systems, of which decision supports systems are only one – albeit a crucial one – among many components . Only few authors mention the integration of clinical knowledge sources such as institutional EHRs [10, 21], which should be regarded as a prerequisite for ‘intelligent’ and individualized decision making , in analogy to the decision making process of a good general practitioner, who will always interpret data in the context of co-morbidities and co-medications, not to forget social context and personality. While the final decision about health-related measures should be taken by both the patient and the doctor as a consultant, a decision support module should be ‘intelligent’ enough to support this kind of decision process.
Many issues remain open considering decision support at home (DS@HOME). As mentioned before, there currently is no clear understanding about the way medical knowledge should be formalized for home care decision support. Most authors seem to use production rules. International standards for such logic, for example the Arden Syntax for Medical Logic Modules – for which an Open-Source compiler has recently been made available  – or GELLO , seem to be rarely – if at all – used. Without standardization, the exchange of decision components and their use beyond the specific scenario of a scientific project becomes very difficult. A similar development may be observed in clinical DSS which are often focused on specific problem areas and feature proprietary knowledge bases . While the general system architecture of a DSS is obvious, many variations exist in terms of where the actual decision logic is located – in the personal resp. home environment [21, 23] or in a centralized system (e.g. [12, 17, 18]). This has implications with regard to the updateability and customization of medical logic components in analogy to a physician’s prescription as proposed by Bott et al. , for example if the adaption of a medication dosage is necessary in a patient with chronic renal failure. Finally, the interfaces (if existent) to current clinical application systems containing the information (such as the diagnosis ICD-10 code N18.2, ‘the patient suffers from chronic renal failure’) which is necessary to interpret home-based data or to make decisions on this basis, currently seem neither widely accepted nor used.
The assembled requirements (aim#2) may serve developers of DSS as helpful guiding criteria for successful implementations, yet the author does not claim them to be complete or ranked according to their importance.
The author cannot rule out that the literature analysis is to some extent subjective. Many of the identified articles do not focus on the decision support components but rather on the overall system design and evaluation issues. Therefore, important facts about the DSS’s features and its implementation might not have been reported there and therefore may have been failed to be gathered in the review process. The results of the literature search as presented in Table 1 are ordered according to the first authors, yet some variations of a system have been described by different authors, for example by Finkelstein et al., Cross et al. and Joshi et al. While the basic system architecture remains the same, different application areas are addressed, and thus different decision components are used. Furthermore, the presentation does not make a difference between systems actually used in clinical practice and lab prototypes as e.g. in . In addition to this, as the use of decision support systems in home environments has not found its way into large clinical trials so far, the author was not able to make a sound analysis of system architectures in terms of an outcome evaluation on the basis of this analysis.
This paper focuses on decision support systems in home environments and presents the current state-of-the-art. Among the predominant challenges for current systems, integration with health information systems resp. clinical application systems have been stressed along with the need for the standardization of knowledge bases. The author has also identified a set of requirements for the successful implementation of DSS in home environments. In the future, these requirements will have to be met by system developers if the use of home-based health-enabling technologies shall be employed not only to gather large amounts of data, but also to provide a benefit for both the patient and the doctor by providing additional information that serves to enhance the knowledge basis on which decisions about health matters are finally made.
Further research is necessary with regard to the outcome of using decision support components at home respectively for home care. The research topics to be addressed should include cost-benefit-analyses, acceptance of DSS by patients as well as medical professionals, standardization of decision logic, pros and cons of different system architectures (centralized vs. locally distributed/ mobile) and methods of advanced individualized data analysis (data fusion, multi-parametric analyses).
This publication is supported by the project “Open Access Publizieren” by the Deutsche Forschungsgemeinschaft (DFG). I wish to thank all my colleagues for the frequent discussions about decision support system in home environments, most notably Klaus-Hendrik Wolf, Matthias Gietzelt and Bianying Song.
- Steinhagen-Thiessen E, Borchelt M: Morbidität, Medikation und Funktionalität im Alter [Morbidity, medication and functional status in the elderly]. Die Berliner Altersstudie [The Berlin Aging Study]. Edited by: Mayer KU, Baltes PB. 1996, Berlin: Akademie Verlag, 151-184.Google Scholar
- Arnrich B, Mayora O, Bardram J, Troster G: Pervasive healthcare: paving the way for a pervasive, user-centered and preventive healthcare model. Methods Inf Med. 2010, 49: 67-73.PubMedGoogle Scholar
- Martin S, Kelly G, Kernohan WG, McCreight B, Nugent C: Smart home technologies for health and social care support. Cochrane Database Syst Rev. 2008, 8 (4): CD006412-Google Scholar
- Brandt A, Samuelsson K, Toytari O, Salminen AL: Activity and participation, quality of life and user satisfaction outcomes of environmental control systems and smart home technology: a systematic review. Disabil Rehabil Assist Technol. 2011, 6: 189-206. 10.3109/17483107.2010.532286.View ArticlePubMedGoogle Scholar
- Haux R: Medical informatics: past, present, future. Int J Med Inform. 2010, 79: 599-610. 10.1016/j.ijmedinf.2010.06.003.View ArticlePubMedGoogle Scholar
- Hackl WO, Hoerbst A, Ammenwerth E: “Why the hell do we need electronic health records?” EHR acceptance among physicians in private practice in Austria: a qualitative study. Methods Inf Med. 2011, 50: 53-61.View ArticlePubMedGoogle Scholar
- van Bemmel JH, Zywietz C, Kors JA: Signal analysis for ECG interpretation. Methods Inf Med. 1990, 29: 317-329.PubMedGoogle Scholar
- Tehrani FT, Roum JH: Intelligent decision support systems for mechanical ventilation. Artif Intell Med. 2008, 44: 171-182. 10.1016/j.artmed.2008.07.006.View ArticlePubMedGoogle Scholar
- Basilakis J, Lovell NH, Celler BG: A decision support architecture for telecare patient management of chronic and complex disease. Conf Proc IEEE Eng Med Biol Soc. 2007, 2007: 4335-4338.PubMedGoogle Scholar
- Basilakis J, Lovell NH, Redmond SJ, Celler BG: Design of a decision-support architecture for management of remotely monitored patients. IEEE Trans Inf Technol Biomed. 2010, 14: 1216-1226.View ArticlePubMedGoogle Scholar
- Bellazzi R, Magni P, Larizza C, De Nicolao G, Riva A, Stefanelli M: Mining biomedical time series by combining structural analysis and temporal abstractions. Proc AMIA Symp. 1998, 1: 160-164.Google Scholar
- Biddiss E, Brownsell S, Hawley MS: Predicting need for intervention in individuals with congestive heart failure using a home-based telecare system. J Telemed Telecare. 2009, 15: 226-231. 10.1258/jtt.2009.081203.View ArticlePubMedGoogle Scholar
- Black LA, McMeel C, McTear M, Black N, Harper R, Lemon M: Implementing autonomy in a diabetes management system. J Telemed Telecare. 2005, 11 (Suppl 1): 6-8.View ArticlePubMedGoogle Scholar
- Bosworth HB, Olsen MK, McCant F, Harrelson M, Gentry P, Rose C, Goldstein MK, Hoffman BB, Powers B, Oddone EZ: Hypertension Intervention Nurse Telemedicine Study (HINTS): testing a multifactorial tailored behavioral/educational and a medication management intervention for blood pressure control. Am Heart J. 2007, 153: 918-924. 10.1016/j.ahj.2007.03.004.View ArticlePubMedGoogle Scholar
- Cross RK, Cheevers N, Finkelstein J: Home telemanagement for patients with ulcerative colitis (UC HAT). Dig Dis Sci. 2009, 54: 2463-2472. 10.1007/s10620-008-0640-0.View ArticlePubMedGoogle Scholar
- Cross RK, Finkelstein J: Challenges in the design of a Home Telemanagement trial for patients with ulcerative colitis. Clin Trials. 2009, 6: 649-657. 10.1177/1740774509346978.View ArticlePubMedGoogle Scholar
- Finkelstein J, O’Connor G, Friedmann RH: Development and implementation of the home asthma telemonitoring (HAT) system to facilitate asthma self-care. Stud Health Technol Inform. 2001, 84: 810-814.PubMedGoogle Scholar
- Finkelstein J, Khare R, Ansell J: Feasibility and patients’ acceptance of Home Automated Telemanagement of oral anticoagulation therapy. AMIA Annu Symp Proc. 2003, 1: 230-234.Google Scholar
- Finkelstein J, Hripcsak G, Cabrera M: Telematic system for monitoring of asthma severity in patients’ homes. Stud Health Technol Inform. 1998, 52 (Pt 1): 272-276.PubMedGoogle Scholar
- Finkelstein J, Hripcsak G, Cabrera MR: Patients’ acceptance of Internet-based home asthma telemonitoring. Proc AMIA Symp. 1998, 336-340.Google Scholar
- Helmer A, Song B, Ludwig W, Schulze M, Eichelberg M, Hein A, Tegtbur U, Kayser R, Haux R, Marschollek M: A sensor-enhanced health information system to support automatically controlled exercise training of COPD patients. 4th International Conference on Pervasive Computing Technologies for Healthcare. 2010, Munich: IEEE, 1-6.Google Scholar
- Joshi A, Amelung P, Arora M, Finkelstein J: Clinical impact of home automated telemanagement in asthma. AMIA Annu Symp Proc. 2005, 1: 1000-Google Scholar
- Marschollek M, Wolf KH, Bott OJ, Geisler M, Plischke M, Ludwig W, Hornberger A, Haux R: Sustainable ubiquitous home health care–architectural considerations and first practical experiences. Stud Health Technol Inform. 2007, 129: 8-12.PubMedGoogle Scholar
- Marschollek M, Bott OJ, Wolf KH, Gietzelt M, Plischke M, Madiesh M, Song B, Haux R: Home care decision support using an Arden engine - merging smart home and vital signs data. Stud Health Technol Inform. 2009, 146: 483-487.PubMedGoogle Scholar
- Reiter H, Maglaveras N: HeartCycle: compliance and effectiveness in HF and CAD closed-loop management. Conf Proc IEEE Eng Med Biol Soc. 2009, 2009: 299-302.PubMedGoogle Scholar
- Song B, Marschollek M, Wolf KH, Gietzelt M, Franken T, Haux R: A home-centered ICT architecture for health-enabling technologies. Stud Health Technol Inform. 2010, 160: 48-52.PubMedGoogle Scholar
- Zheng H, Nugent C, McCullagh P, Huang Y, Zhang S, Burns W, Davies R, Black N, Wright P, Mawson S: Smart self management: assistive technology to support people with chronic disease. J Telemed Telecare. 2010, 16: 224-227. 10.1258/jtt.2010.004017.View ArticlePubMedGoogle Scholar
- Zheng H, Nugent CD, McCullagh PJ, Black ND, Eccleston C, Bradley D, Hawley M, Brownahell S, Mawson SJ, Wright P, Mountain GA: Towards a Decision Support Personalised Self Management System for Chronic Conditions. Networking, Sensing and Control, 2008 ICNSC 2008 IEEE International Conference on; 6–8 April 2008. 2008, 1521-View ArticleGoogle Scholar
- Proposals for Safeguarding Good Scientific Practice. . 1998, [http://www.dfg.de/aktuelles_presse/reden_stellungnahmen/download/self_regulation_98.pdf]
- Schulze M, Song B, Gietzelt M, Wolf KH, Kayser R, Tegtbur U, Marschollek M: Supporting rehabilitation training of COPD patients through multivariate sensor-based monitoring and autonomous control using a Bayesian network: prototype and results of a feasibility study. Inform Health Soc Care. 2010, 35: 144-156. 10.3109/17538157.2010.528659.View ArticlePubMedGoogle Scholar
- Bott OJ, Marschollek M, Wolf KH, Haux R: Towards new scopes: sensor-enhanced regional health information systems - part 1: architectural challenges. Methods Inf Med. 2007, 46: 476-483.PubMedGoogle Scholar
- Speedie SM, Ferguson AS, Sanders J, Doarn CR: Telehealth: the promise of new care delivery models. Telemed J E Health. 2008, 14: 964-967. 10.1089/tmj.2008.0114.View ArticlePubMedGoogle Scholar
- Lymberis A: Research and development of smart wearable health applications: the challenge ahead. Stud Health Technol Inform. 2004, 108: 155-161.PubMedGoogle Scholar
- Koch S: Home telehealth–current state and future trends. Int J Med Inform. 2006, 75: 565-576. 10.1016/j.ijmedinf.2005.09.002.View ArticlePubMedGoogle Scholar
- Koch S, Hagglund M: Health informatics and the delivery of care to older people. Maturitas. 2009, 63: 195-199. 10.1016/j.maturitas.2009.03.023.View ArticlePubMedGoogle Scholar
- Hermens HJ, Vollenbroek-Hutten MM: Towards remote monitoring and remotely supervised training. J Electromyogr Kinesiol. 2008, 18: 908-919. 10.1016/j.jelekin.2008.10.004.View ArticlePubMedGoogle Scholar
- Song B, Wolf KH, Gietzelt M, Al Scharaa O, Tegtbur U, Haux R, Marschollek M: Decision support for teletraining of COPD patients. Methods Inf Med. 2010, 49: 96-102.PubMedGoogle Scholar
- HL7 Standards. [http://www.hl7.org/]
- Abraham C, Rosenthal DA: Merging home and health via contemporary care delivery: program management insights on a home telehealth project. Comput Inform Nurs. 2008, 26: 273-281. 10.1097/01.NCN.0000304837.70133.98.View ArticlePubMedGoogle Scholar
- Goldsmith DM, Silverman LB, Safran C: Pediatric Cancer CareLink--supporting home management of childhood leukemia. Proc AMIA Symp. 2002, 1: 290-294.Google Scholar
- Falas T, Papadopoulos G, Stafylopatis A: A review of decision support systems in telecare. J Med Syst. 2003, 27: 347-356. 10.1023/A:1023705320471.View ArticlePubMedGoogle Scholar
- Redmond SJ, Basilakis J, Xie Y, Celler BG, Lovell NH: Piecewise-linear trend detection in longitudinal physiological measurements. Conf Proc IEEE Eng Med Biol Soc. 2009, 2009: 3413-3416.PubMedGoogle Scholar
- Lovell NH, Celler BG: Information technology in primary health care. Int J Med Inform. 1999, 55: 9-22. 10.1016/S1386-5056(99)00016-7.View ArticlePubMedGoogle Scholar
- Marschollek M, Rehwald A, Wolf KH, Gietzelt M, Nemitz G, Meyer Zu Schwabedissen H, Haux R: Sensor-based Fall Risk Assessment - an Expert ‘to go’. Methods Inf Med. 2011, 50 (5): 420-426. 10.3414/ME10-01-0040. Epub 2011 Jan 5View ArticlePubMedGoogle Scholar
- Marschollek M, Rehwald A, Wolf KH, Gietzelt M, Nemitz G, zu Schwabedissen HM, Schulze M: Sensors vs. experts - a performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients. BMC Med Inform Decis Mak. 2011, 11: 48-10.1186/1472-6947-11-48.View ArticlePubMedPubMed CentralGoogle Scholar
- Mancini F, Sousa FS, Hummel AD, Falcao AE, Yi LC, Ortolani CF, Sigulem D, Pisa IT: Classification of postural profiles among mouth-breathing children by learning vector quantization. Methods Inf Med. 2011, 50 (4): 349-357. Epub 2010 Sep 22View ArticlePubMedGoogle Scholar
- Marschollek M, Wolf KH, Bott OJ, Geisler M, Plischke M, Ludwig W, Hornberger A, Haux R: Sustainable ubiquitous home health care–architectural considerations and first practical experiences. Medinfo. 2007, 12: 8-12.Google Scholar
- Wolf KH, Schirmer S, Marschollek M, Haux R: Representing sensor data using the HL7 CDA personal healthcare monitoring report draft. Stud Health Technol Inform. 2009, 150: 480-484.PubMedGoogle Scholar
- Van Hoecke S, Volckaert B, Dhoedt B, De Turck F: Large-scale performance evaluation of e-homecare architectures using the WS-NS simulator. Methods Inf Med. 2011, 50 (5): 408-419. Epub 2010 Oct 6View ArticlePubMedGoogle Scholar
- Cleland JG, Louis AA, Rigby AS, Janssens U, Balk AH: Noninvasive home telemonitoring for patients with heart failure at high risk of recurrent admission and death: the Trans-European Network-Home-Care Management System (TEN-HMS) study. J Am Coll Cardiol. 2005, 45: 1654-1664. 10.1016/j.jacc.2005.01.050.View ArticlePubMedGoogle Scholar
- Zhang J, Goode KM, Cuddihy PE, Cleland JG: Predicting hospitalization due to worsening heart failure using daily weight measurement: analysis of the Trans-European Network-Home-Care Management System (TEN-HMS) study. Eur J Heart Fail. 2009, 11: 420-427. 10.1093/eurjhf/hfp033.View ArticlePubMedGoogle Scholar
- Nangalia V, Prytherch DR, Smith GB: Health technology assessment review: remote monitoring of vital signs–current status and future challenges. Crit Care. 2010, 14: 233-10.1186/cc9208.View ArticlePubMedPubMed CentralGoogle Scholar
- Alexander GL, Wakefield BJ, Rantz M, Skubic M, Aud MA, Erdelez S, Ghenaimi SA: Passive sensor technology interface to assess elder activity in independent living. Nurs Res. 2011, 60: 318-325.PubMedPubMed CentralGoogle Scholar
- Marschollek M: Recent progress in sensor-enhanced health information systems - slowly but sustainably. Inform Health Soc Care. 2009, 34: 225-230. 10.3109/17538150903358610.View ArticlePubMedGoogle Scholar
- Gietzelt M, Goltz U, Grunwald D, Lochau M, Marschollek M, Song B, Wolf KH: Arden2ByteCode: a one-pass Arden Syntax compiler for service-oriented decision support systems based on the OSGi platform. Comput Methods Programs Biomed. 2012, 106 (2): 114-125. 10.1016/j.cmpb.2011.11.003. Epub 2011 Dec 27View ArticlePubMedGoogle Scholar
- Sordo M, Ogunyemi O, Boxwala AA, Greenes RA: GELLO: an object-oriented query and expression language for clinical decision support. AMIA Annu Symp Proc. 2003, 1: 1012-Google Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6947/12/43/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.