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2011 4th International Conference on Biomedical Engineering and Informatics (BMEI)

Integration of Medical Models in Personal HealthRecords Using the Example of Rehabilitation

Training for Cardiopulmonary PatientsAxel Helmer∗, Friedrich Kretschmer†, Frerk Muller∗, Marco Eichelberg∗, Riana Deparade‡,

Uwe Tegtbur‡, Michael Marschollek§, and Andreas Hein∗∗OFFIS-Institute for Information Technology, Escherweg 2, 26121 Oldenburg, Germany

†Computational Neuroscience, University of Oldenburg, Carl-von-Ossietzky-Straße 9-11, 26129 Oldenburg, Germany‡Institute of Sports Medicine, Medical School Hannover, Carl-Neuberg-Straße 1, 30625 Hannover, Germany

§Peter L. Reichertz Institute of Technology, Carl-Neuberg-Str. 1, 30625 Hannover, Germany

Abstract—This article presents a prediction model for thedevelopment of the heart rate during rehabilitation training inpatients suffering from cardiopulmonary diseases. The modelhelps to ensure a safe and effective training. Furthermore, theintegration of the model into a personal health record systemfacilitating interoperability among doctors, hospitals and otherhealthcare institutions is discussed.

keywords: personal health record, heart rate modeling, re-habilitation training

I. INTRODUCTION

During the last few years a number of new approacheshave been proposed to facilitate a fast and secure exchangeof patient records between healthcare professionals.

In “traditional” Electronic Patient Records (EPR) the datastored and the access rules are regulated by institutions act-ing in the different domains of the healthcare sector. Theconcept of the Personal Health Record (PHR) deflects theresponsibility for the handling of ones data to the patienthim/herself. The patient should hence decide which data hewants to share with his doctor, a hospital or other institution,depending on the patient’s own health status and privacyconcerns. While multiple EHRs containing data from onepatient are typically maintained in several different healthservice institutions, storing different data in different formatsand providing a variety of proprietary interfaces, the PHR ismeant to overcome these limitations by providing standardizedcommunication interfaces under control of the patient, thusenabling data exchange in the course of medical treatmentsamong various institutions.

In particular people with chronic diseases, who are requiredto take several different medications, while frequently visitingmultiple doctors, might strongly benefit from a controlled, butmore extensive data exchange. Not only could the PHR beused to share data, thus connecting experts, it could also besupplemented by data that can only be obtained in the patientshome area (e. g. activity monitoring) or by data that can onlybe provided by the patient himself (e. g. outdoor sports activity,over-the-counter medication).

An important factor that is often ignored is that severaldiseases also require the patient to follow a strict physicaltraining plan. An example are cardiopulmonary patients whooften show symptoms such as dyspnoea, physical inactivity,skeletal and muscle atrophy, thereby causing high costs inhealth systems worldwide. It has been shown that pulmonaryrehabilitation training improves physical capacity, reducesbreathlessness, reduces the number of hospitalizations andincreases the quality of life [1].

It is crucial to determine how much load a patient canundergo and to detect potentially abnormal developmentsduring a training session. Rehabilitation training schedulesunderly various limitations depending on a patient’s age,gender, medication, physical state and other factors like theenvironmental temperature. This factor plays an even moreimportant role when a patient exercises without supervision athome.

In this study we present an extended version of our pre-viously developed PHR (see [2]), which integrates a modelto determine the optimal training load for a bicycle ergometertraining based on various parameters retrievable from the PHR.Based on PHR data cardiopulmonary patients are identified bythe system and their training schedule is adapted accordingly.An established parameter to characterize the physical loadstate during trainings is the heart rate (HR), which is easilymeasurable and is regularly recorded during all sorts of sporttrainings.

Achten and Jeukendrup published a review paper in 2003summarizing the state of the art regarding HR monitoring[3]. They identified age, gender, environmental temperature,hydration, altitude and anti hypertensive medication such asbeta blockers [4] as important influence factors for the trainingHR. For an effective and safe training it is not only importantto know the optimal and maximum heart rate at which apatient should train, but it is also crucial to know how theheart rate will develop over time to predict and prevent criticalstates and to plan an optimal training schedule taking intoaccount as many of the influencing variables as possible. Forthis purpose several models have been developed. Velikic et

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al. created and compared Kalman filters, linear and nonlinearmodels for the prediction of HR of patients with congestiveheart failure (see [5]). They used a pedometer to measure thesubject’s activity and found that linear models delivered thebest results for a short term prediction. Their work shows thatphysical activity in everyday life also has an influence on HR.Other approaches modeled the HR response to provide bettertraining control [6]–[8]. These models have not been checkedfor their applicability to either cardiopulmonary patients, andno specialized HR models exist for these patients.

The performance of a model can generally be increased byadding the effect of influencing variables, but the acquisitionof this data can often be time demanding, expensive and tech-nically difficult. For many applications a pragmatic empiricalapproach might be sufficient

However, since the PHR offers a whole set of differentdata that is known to have an influence on the HR (e. g.medication), it is self-evident that a PHR-based HR predictivemodel offers new opportunities. Additionally we propose thatsuch a model itself could be integrated into the PHR.

II. CONCEPT AND REALIZATION

A. Overview

Figure 1 shows the desired workflow for the integrationof the HR prediction model in the rehabilitation process ofcardiopulmonary patients. The creation and application of themodel are described in section II-B. On the Y axis the figureis divided into three levels: “Process” is related to the standardrehabilitation process of cardiopulmonary patients. “System”subsumes the IT-systems involved into the process. “Model”depicts the model that is integrated into the process.

At the beginning of a treatment most patients have ahistory of previous medical consultations and examinationsthat might be stored in one or more EPR. The EPR mayreside in the IT system of the family doctor, in a hospital orother healthcare institution. Typically the data stored containmedication, diagnoses and other vital parameters (e. g. bloodpressure). By using medical standards as discussed in sectionII-C, the patient can import into his own PHR and therebyaccess the medical data from the various EPR.

A typical development of a cardiopulmonary disease oftenincludes a sudden change for the worse, e. g. an exacerbationin the case of chronic obstructive pulmonary disease (COPD).After being referred to a hospital or directly to a rehabilitationclinic the patient is examined during the admission process,while demographics and medical data are stored in the EPR ofthe rehabilitation clinic. The missing interoperability betweenthe doctor’s EPR and the one of the clinic makes an electronicdata exchange impossible. After the patient is stabilized, anexercise tolerance test, typically in form of a level test ona bicycle ergometer is performed to determine the patient’sphysical capacity. The ergometer load is increased in varioussteps until the patient is exhausted or the monitoring physicianstops the test. Typically the recorded parameters include themaximum heart rate, oxygen saturation, ergospirometry, andECG data. At the end of the assessment the physician creates

TABLE I: Influence of the predictors during different phasesof cardiopulmonary rehabiliation training. Values representingthe percental improvement of the models RMSE and arecalculated as average over the two datasets during crossvalidation. “dur.” abbreviates “duration”. The table is ordereddescending by the “Overall” column, which is calculated asthe average over a row for one predictor.

Predictor Phase 1 Phase 2 Phase 3 Phase 4 Overall

Age 0.284 0.521 8.342 1.885 2.758

Gender 0.000 0.053 0.619 0.082 0.188

Load 0.165 0.000 0.000 0.204 0.092

Training dur. 0.065 0.000 0.000 0.000 0.016

Phase dur. 0.040 0.000 0.000 0.000 0.010

Air Pressure 0.027 0.000 0.000 0.000 0.007

Temperature 0.000 0.000 0.000 0.000 0.000

Humidity 0.000 0.000 0.000 0.000 0.000

a training schedule. This document is stored in the trainingmanagement system and can be imported from there into thepatient’s PHR.

After several weeks of stationary rehabilitation, patientsare discharged into the outpatient rehabilitation phase. Thisgenerally includes physical training together with others ina lung sports group or unsupervised at home. At this pointthe HR prediction inside the PHR supports the rehabilitationprocess by supporting the physician in the creation of anupdated training schedule. The PHR identifies the patient’streatment and loads the model for COPD patients (for furtherdetails see section II-D). The customized model and the stan-dardized communication can also be used after an outpatientrehabilitation period (e. g. by the physician in charge) to givefurther information about the health state, training complianceand training performance of the patient for further treatments(see figure 1).

B. Heart Rate Modeling

To determine the relevant predictors and to fit the functionparameters for the HR model we used a dataset that wascollected during outpatient rehabilitation between July andSeptember 2009 in the exercise training center of the MedicalSchool Hannover. HR was obtained on the basis of electrocar-diogram (ECG) data. After filtering the dataset, 668 (325 F,343 M) training sessions from 115 patients (mean 5.7 ± 4.4sessions per patient) were available. The dataset was used forthe model and the application scenario. It included:

• Patient demographics: age, sex• Training data: date and time, duration, loadBecause weather can influence training, we also included

data from the German weather service that was recorded by aweather station in Hannover (station ID: 2014) and includedtemperature, air pressure, and humidity.

Each session was devided in four training phases (see figure2). During the warm-up phase the load is set to a constantvalue (phase 1). This is followed by a stepwise increase of

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Fig. 1: Overview of the rehabilitation process of cardiopulmonary patients, the participating systems and the training model.Abbreviations used: Electronic Patient Record (EPR), Personal Health Record (PHR), Cross-enterprise Document ReliableInterchange (XDR), Cross-Enterprise Document Media Interchange (XDM), Exchange of Personal Health Record Content(XPHR), Personal Healthcare Monitoring Report (PHMR).

the load over time (phase 2). During the load phase (phase3) the load is set to a constant value and is finally reducedstepwise during the cool-down phase (phase 4). We used astepwise regression analysis [9] to build a hypothesis aboutwhich variables have a relevant influence on the HR andcan thereby be used as predictors. This algorithmic approachperforms a multilinear regression and determines a model bystepwisely adding or removing the variable with the highestor lowest correlation of the model’s F-statistics. This is doneuntil all variables with significant influence (predictors) havebeen added and all variables with non-significant influencehave been removed from the model. We used the standardentrance and exit tolerances of p ≤ 0.05 and p ≥ 0.10 forthis process. Additionally we performed a chi-square tests toconfirm the normal distribution of the HR dataset.

The resulting model consist of a set of coefficients (Bi) anda constant term (c). Together with a number of given predictorvalues (Xi) this leads to the following equation to calculatethe response variable (Y ):

Y = c+ b1x1 + b2x2...+ bixi (1)

As we sub-divided a training into four phases, we built foursubmodels (one for each phase) that were then concatenatedto a complete model for one training session.

Fig. 2: Sample training session with curves for the heart rateand load separated in four training phases.

To determine the quality of our model and to preventoverfitting, we performed a k×2 cross-validation. We dividedthe dataset into two parts d0 and d1. Both parts were of thesame size and contained randomly selected training sessions(N=334) from the dataset. We used d0 to train the model andvalidated it against the d1 dataset, then we performed thisprocedure vice versa.

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Fig. 3: Average Root Mean Square Error resulting from thecross-validation of the HR prediction model. Phases 1-4 denotethe four training phases: warm-up with constant load, warm-upplateau, load, and cool-down. The overall average and medianvalues are related to the complete training and are weightedby the length of the phases.

During the cross-validation we calculated the Root MeanSquare Error (RMSE), which quantifies the deviation betweenmeasured and predicted HR over the time of a training sessionin beats per minute (bpm). We reached a precision of 11 bpmin median (see figure 3).

As each added predictor depends on the former one, itis not easy to determine, which predictor of the resultingmodel explains which part of the response variable. To make astatement about the influence of the predictors, we calculatedthe percental improvement of the RMSE when a predictor isadded to the model in relation to the former one. We foundthat the available weather data only has a minor influence (seetable I), whereas age, gender, and the training load contributemost.

C. Interoperability

Because the PHR requires interoperability with differentsystems, it is important to choose appropriate standards fordata storage and communication. Two important non-profitinitiatives promote interoperability of health IT systems:

The “Integrating the Healthcare Enterprise” (IHE) initiativeworks on improving the way computer systems in healthcareshare information [10]. The Continua Health Alliance, anindustry coalition with more than 200 member companies, wasfounded to establish a system of interoperable personal healthsolutions. Both organizations do not define new standards butuse and combine existing ones in support of specific usecases. Continua mainly focuses on the end user market andhas participated in the development of a document templatethat is based on the Clinical Document Architecture (CDA)called Personal Healthcare Monitoring Report (PHMR). Thisspecification has been published by HL7 as a Draft Stan-dard for Trial Use (see [11]). IHE is more involved withthe professional side, but has defined an integration profile

also based on CDA that describes the information exchangebetween EPRs and PHRs, named Exchange of Personal HealthRecord Content (XPHR) [10]. We have implemented XPHRand we are currently working on the PHMR implementation.

Both (XPHR and PHMR) document types can be bound toone of IHE’s transport profiles describing how a standards-based communication between EPR and PHR is actuallyimplemented. There are three such IHE profiles for theexchange of documents: Cross-enterprise Document Sharing(XDS) uses a central registry and repository infrastructure,which is not available yet in most countries. Cross-enterpriseDocument Reliable Interchange (XDR) describes a point-to-point document exchange using secure Web Services and E-Mail. Cross-Enterprise Document Media Interchange (XDM)allows users to exchange data through media such as USBsticks or CD-Rs (see [12]). We have implemented XDM astransport profile for the document exchange, because it can beused with a PHR that is not always available online.

CDA seems to be the best choice for this specific use casebecause it is the basis for the XPHR and PHMR documenttemplates, which we use for the exchange of medical data andvital signs. It is also supported by most relevant organizationsinvolved with interoperability of IT systems in the medicaldomain (see [13]). Furthermore CDA provides important char-acteristics like stewardship, authentication, context, wholeness,and human readability (see [14] for further details), whichare important for every clinical document and also for everyelectronic health record. For these reasons the PHR providesmechanisms for the import, creation and management of CDAdocuments.

D. Model Integration and PHR Architecture

Once the PHR has received a summary of the patient’sdiagnoses in form of a standardized document or by enteringthe medical information manually, the PHR determines theright model(s). In our case, the PHR-inherent logic identifiesthe patient’s COPD treatment and loads the adapted cardiopul-monary training model for COPD patients. Relevant predictorshave been predetermined in the model creation process and themodel acquires the patient’s specific values from the PHR topredict training HR. The prediction algorithm includes valuesstored in the PHR to provide a self-adapting HR prediction. Ifthe medication is changed by another physician or the patientperforms other sport activities during a training day, the modelcan take this data into account and adapts the HR predictionaccordingly (see figure 4).

To support a general concept for the integration of medicalmodels the PHRs, software architecture has to be flexibleenough to take configurable and loadable modules/componentsinto account. Our PHR is running inside an “Open ServiceGateway initiative” (OSGi) Framework (see [15], [16]). ThisJava-based runtime framework allows for a component-baseddevelopment and a component-wise provisioning and updateduring runtime. It is, therefore, possible that new medical mod-ules can be downloaded (e. g. via secure Internet connections)

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Fig. 4: Screenshots (edited) of the Personal Health Record: (a)Patient selection, (b) Medication history, (c) Rights manage-ment, (d) Exercise Plan creation, (e) HR prediction.

and executed in the framework without the PHR ever beingrestarted.

To provide a data source that is both highly available forthe patient and always under his full control, our approachlocates the PHR in the user’s home environment where itcan be installed on normal PCs as well as on selected settop boxes. We believe that it is important to allow the userto disable any access to the PHR from outside (e. g. byappropriately configuring the system or by simply keeping itoff the Internet connection) to achieve higher acceptance, thana fully Internet-based system with remote data storage at an“unknown” location outside the control of the user would.

To involve physicians, family members and other personswho participate in the treatment process on one hand and toassure the patient’s privacy on the other hand, we built a rightsmanagement system (see figure 4) that enables the patient tomaintain a list of medical contacts in the PHR and to sharedocuments with these medical contacts.

III. DISCUSSION & OUTLOOK

Our current HR model can still be improved in severalaspects. First of all the predictors were limited to the dataavailable for the study. Due to this limitation, e. g. medicationcould not be taken into account. Several other predictors willbe examined and optionally integrated as soon as further databecomes available. The (modular) modeling approach chosenmakes such additional integration easy.

The model shows that weather only had a minor influenceon the HR. However, weather might nevertheless influenceother important parameters within a training unit, such as theBorg value. Furthermore, the temperature within the trainingenvironment may also directly influence the HR. Both aspectswill be examined in a future version of our model and we planto integrate a prediction for Borg values into the PHR trainingplanner.

Unfortunately until now neither XPHR nor PHMR havemanaged to establish themselves widely as standards in med-ical institutions, at least not in Germany as the country wherethis study was performed. Nevertheless, IHE is getting moreimportant. Switzerland for example is in the process of settingup a nation-wide XDS infrastructure. As several ContinuaHealth Alliance member companies are actively supportingPHMR, we expect significant improvements in the availabilityof implementations during the next years and look out for anextensive exchange among several medical domains in future.

The PHR with its HR prediction model will also be inte-grated into the OSAMI tele-rehabilitation system [17], whereit will be used for training schedule planning as well asduring a live training session to monitor and supervise thepatient. We hope to increase the precision of our model inthis environment, as more predictors like e. g. blood pressureduring the training become available.

The integration of predictive models into PHRs could lead tomore effective and safe training conditions for COPD patients.While we only covered one specific application scenario,an additional integration of models for other diseases ortreatments into the PHR is planned.

IV. CONCLUSION

We have developed a model for COPD patients whichpredicts the HR course during a training with a precisionof 11 bpm in median. The model has been integrated intothe training schedule planner of our Personal Health Recordand is the basis for improvements of training conditions forcardiopulmonary patients in terms of efficiency and safety.

Additionally the PHR includes a rights management to shareselected medical data and supports several medical standardsto facilitate the exchange among different institutions withinthe health domain and beyond. Hence the model could safelybe used to train and supervise rehabilitation training at homein hospitals or wherever it suits.

ACKNOWLEDGMENT

This work was funded in part by the Ministry for Sci-ence and Culture of Lower Saxony within the Research Net-

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work “Design of Environments for Ageing” (grant VWZN2420/2524).

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