Background
Electronic health record standardization
DBMS solutions for storing standardized EHRs
Monitoring systems for elderly
Proposed framework for monitoring frailty
Methods
OpenEHR
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Observations: they correspond to raw information, as they have been measured by devices, reported by patients or noticed by doctors (test findings, symptoms noticed etc.).
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Evaluations: they include interpreted clinical information (in contrast to Observations which are uninterpreted) which have been been assessed by doctors. In this subclass, the knowledge knowledge is extracted from the initial measurements leading to a conclusion. For instance, Problem/Diagnosis archetype belongs to this category.
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Instructions: they refer to commands about future that should be performed or steps that should be followed. A typical example of this archetype category is a medication order.
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Actions: they refer to activities that have already taken place. Actions are often used to record in what extent the Instructions have been followed.
FrailSafe virtual patient model research methodology
Requirements
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Identification details: This subclass involves all the requirements which are related to the identification of the person, such as his/her name.
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Demographics details: This subclass involves all the requirements concerning demographic information, such as gender and age.
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Contact details: This category relates to the documentation of contact details of the patients or older people.
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Physiological measurements, such as heart rate or respiratory rate.
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Physical/functional measurements, such as motor and strength condition.
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Psychological measurements, such as depression and anxiety.
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Social interaction and behavioral parameters, such as number of phone calls per week.
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Cognitive measurements, such as progress in VR/AR games and deficiencies in electronic written text.
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Lifestyle parameters, such as alcohol consumption and indoor/outdoor activity levels.
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Nutrition-dependent entities, such as body mass index (BMI) and body fat.
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General condition, such as unintentional weight loss.
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Wellness, such as self-rated health status.
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Environmental factors, such as number of steps to access house.
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Medical domain, such as number of co-morbidities.
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Frailty metric.
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Alerts related to detection or prediction of acute or sudden events, such as instability or fall.
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Notifications about change or critical evolution of a clinical metric.
Archetype-based entities representation
Integration of archetypes into a NoSQL database system
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Definition of a table schema, in which the column families are mapped to the labels contained into the archetype classes/subclasses described in the openEHR subsection and at the same time are used for the representation of concepts in a particular use-case scenario. If for example one should map only archetypes of the observation subclass to an HBase table, the column families would be “State”, “Events”, “Attribution”, “Data”, “Protocol”, and “Description” (see Fig. 1). However, the labels “Description” and “Attribution” should be excluded, as they contain common details related to the archetype and not to the concept they represent, such as original author, translators, purpose, use etc., and there is no point in storing such information for every record in the database. Hence, this information could be stored in a separate table.
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Population of the column families with columns for storage of the information represented by the archetype in a particular use-case scenario. Since usually the archetypes contain more fields than required for the representation of the corresponding concepts, columns have to be created only for the storage of data related to the used fields of the archetypes. Additionally, each archetype may require different table columns for its storage. If for example one would like to store the “Weight” and “Comment” fields of the label “Data” and “Any event” of the label “Events” for the representation of Body weight (Fig. 1), then three separate columns should be added to the HBase table as shown in Table 1. Similarly, if one would like to store additionally the “Systolic” and “Any event” fields for the representation of Blood pressure (Fig. 2), then more columns would be added to the HBase table.Table 1Example: Result of the archetypes - Hbase table schema mappingRowkeyDataEventsSystolicWeightCommentSynopsisAny Event1001_20180312_EHR-OBSERVATION.blood_pressure.v1143.0AbnormalHigh BP1001_20180312_EHR-OBSERVATION.body_weight.v285.2NormalNo1001_20180422_EHR-EVALUATION.clinical_synopsis.v1Synopsis text1001_20180613_EHR-OBSERVATION.body_weight.v292.3ObesityHigh weight gain⋯×
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Determination of the rowkey structure which will be common for all column families. The name of the archetype has to be included in the rowkey to avoid a possible overlap of data in cases where the fields of the used archetypes have common names. Thus, by including the archetype name into the rowkey, data related to different archetypes are stored in different rows. For example for the 4 sample entries of clinical data presented in Table 1, each row is identified by a unique rowkey which is composed by the patient identifier (eg. 1001), the date of the measurement in “YYYYMMDD” format (eg. 20180312), and the name of the archetype (eg. EHR-OBSERVATION.body_weight.v2) which describes the clinical entity. If we hadn’t included the archetype name into the rowkey, then the second row would have the same rowkey as the first one (1001_20180312), and thus the “Comment” as well as the “Any event” values of the first record would be overwritten.
Results
Entities included in the VPM
Parameters | Parameters Content | openEHR Archetypes Existing/New | Archetype items Used/Added |
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Personal details | Patient Identification | EHR-CLUSTER.individual_personal.v1 | Items.Identifier |
Demographic Details (Gender, Date of birth) | DEMOGRAPHIC-ITEM_TREE.person_details.v1 | Data.(Birth date, Gender) | |
Contact Details | DEMOGRAPHIC-ADDRESS.address.v1 | Details.(Country identifier,...) | |
EHR-CLUSTER.telecom_details.v0 | Items.(Email Address,...) |
Parameters | Parameters content | openEHR Archetypes Existing/New | Archetype items Used/Added |
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Heart rate | -Daily statistics (average,max, etc.) for: heart rate,rr-interval,heart rate variability when: 1)sitting/standing,2)walking,3)lying, 4)walking upstairs/downstairs, 5)in transition -Device used,-Abnormal Events | EHR-OBSERVATION.pulse.v1 EHR-CLUSTER.device.v1 | Data.Rate (beats per min), Data.Variability, State.Position, Events.Maximum, Events.Any event, Protocol.device |
EHR-OBSERVATION.ecg_test_result.v0 | Data.RR Rate | ||
EHR-CLUSTER.level_of_exertion.v1 | Items.Exercise.Description (e.g. walking) | ||
Respiration Rate | -Daily statistics (average,max, etc.) for: respiration rate, breathing amplitude when: 1)sitting/standing,2)walking,3)lying, 4)walking upstairs/downstairs, 5)in transition -Device used,-Abnormal Events | EHR-OBSERVATION.respiration.v1 EHR-CLUSTER.device.v1 | Data.Rate (beats per min), Data.Depth State.Position, Events.Any event, Protocol.device |
EHR-CLUSTER.level_of_exertion.v1 | Items.Exercise.Description (e.g. walking) | ||
Blood pressure | -Mean daily systolic/diastolic/pulse value -Device used, -Abnormal Events | EHR-OBSERVATION.blood_pressure.v1 EHR-CLUSTER.device.v1 | Data.(Systolic, Diastolic, Pulse Pressure)(mm[Hg]), Events.Any event, Protocol.device |
Arterial stiffness | -PulseAmplification, Augmentation Index75, Vascular Resistance, Cardiac Output, Stroke Volume Cardiac Index, Augmentation, Reflection Coefficient, Pulse Wave Velocity, Mean stiffness value daily, -Device used | EHR-OBSERVATION.arterial_stiffness.v0 EHR-CLUSTER.device.v1 | Data.(Pulse Amplification,..., Stiffness), Protocol.Device |
Indoor activities | Mean time spent daily: 1)in the living room, 2)in the restroom, 3)in the bedroom, 4)indoors in general, 5)walking inside, 6)sitting/standing, 7)lying | EHR-OBSERVATION.activities.v0 EHR-CLUSTER.device.v1 | Data.Duration (min), Data.Description, Data.Place, Protocol.Device |
Outdoor mobility pattern | Daily values for: 1) total distance covered, 2) total duration, 3) total number of steps, 4) radius covered, 5) area covered, 6) average walk speed, 7) total walk time, 8) total stop time, 9) total vehicle time, 10) walk time percentage, 11) vehicle time percentage, 12) stop time percentage, 13) number of tracks, 14) track average distance, 15) track average duration, 16) track maximum distance, 17) track maximum duration | EHR-OBSERVATION.outdoor_mobility_pattern.v0 EHR-CLUSTER.device.v1 | Data.(Distance, Duration,...,Track maximum duration), Protocol.Device |
Game1 | Daily values for: 1) Max force, 2) Average max force, 3) Average endurance, 4) Max endurance, 5) Average score, 6) Max score, 7) Average game duration, 8) Max game duration Daily statistics for: 9) Height over game duration, 10) Distance over game duration, 11) Speed over game duration, 12) Lives over game duration, 13) Force over game duration |
EHR-CLUSTER.red_wings_game.v0
| Data.(Max Force,...,Force over game duration) |
Parameters | Parameters Content | openEHR Archetypes Existing/New | Archetype items Used/Added |
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Nutrition | For each clinical assessment: Body Weight, Waist circumference, Body mass index, Body fat, Lean body mass, MNA [63] total score | EHR-OBSERVATION.body_weight.v2 EHR-OBSERVATION.waist_circumference.v1 EHR-OBSERVATION.body_mass_index.v1 EHR-OBSERVATION.body_composition.v1 EHR-OBSERVATION.body_mass_index.v1 EHR-OBSERVATION.mna_questionnaire.v0 | Data.Body Weight (kg or lb) Data.Waist circumference (cm) Data.Body mass index Data.Fat mass Data.Body free mass index Data.Total score |
Social interaction | 1)Number of phone calls, 2)Number of text messages 3)Time spent speaking at the phone, 4)Time spent on video-conference 6)Living Conditions, 7)Number of leisure activities, 8)Membership in a leisure club |
EHR-OBSERVATION.
generalities_questionnaire.v0
| Data.Phone Calls Data.Text Messages Data.Speaking Duration (min) Data.Video Conference Duration (min) Data.Living conditions, Data.Leisure activities, Data.Leisure club) |
Cognitive State |
EHR-OBSERVATION.moca_questionnaire.v0
EHR-OBSERVATION.mmse_questionnaire.v0
EHR-OBSERVATION.
cognitive_mood_sleep_questionnaire.v0
| Data.Total Score Data.Total score Data.Memory complain | |
Psychological State | GDS-15 [66] questionnaire score, self rated anxiety |
EHR-OBSERVATION.gds-15_questionnaire.v0
EHR-OBSERVATION.
visual_analogue_scale.v0
| Data.Total score Data.Score |
Physical condition | Single foot standing), Time Get up and Go, Gait-speed (4m), Lower Limb strength, Low physical activity |
EHR-EVALUATION.
gait_balance_evaluation.v0
EHR-OBSERVATION.fried_criteria.v0
| Data.(Single foot standing, Time Get up and go, Gait speed 4m, Raise from the chair 5 times) Data.Low physical activity |
Functional capacity |
EHR-OBSERVATION.katz_index_questionnaire.v0
EHR-OBSERVATION.lawdon_adl_questionnaire.v0
| Data.Total Score Data.Total score | |
General condition | Unintentional weight loss, Self-reported Exhaustion |
EHR-OBSERVATION.fried_criteria.v0
| Data.Exhaustion, Weight loss |
Wellness | Self-rated: Quality of life, pain, health status, change since last year |
EHR-OBSERVATION.
visual_analogue_scale.v0
| Data.Score |
EHR-OBSERVATION.health_self_rating.v0
| Data.(Health status, Change) | ||
Lifestyle | Smoking, Alcohol Consuption, Physical activity | EHR-EVALUATION.tobacco_smoking_summary.v1 EHR-OBSERVATION.substance_use-alcohol.v1 EHR-OBSERVATION.physical_activity.v1 | Data.Overall Status Data.(Frequency, Amount) Data.Physical activity level |
Housing condition | Subjective suitability of the housing environment according to participant’s evaluation and investigator’s evaluation Number of steps to access the house |
EHR-OBSERVATION.housing_condition.v0
| Data.(Suitability (participant), Suitability (investigator), Number of steps) |
Medical domain | Number of co-morbidities, Number of significant co-morbidities, Number of medications, Orthostatic hypotension, Vision, Hearing | EHR-EVALUATION.problem_diagnosis.v1 EHR-INSTRUCTION.medication_order.v2 EHR-COMPOSITION.report-result.v1 | Data.(Problem/Diagnosis name, Severity) Activities.Order.Medication Context.Status |
Frailty | Frailty phenotype categorization by Fried [62] |
EHR-OBSERVATION.fried_criteria.v0
| Data.Index |
Parameters | Parameters Content | openEHR Archetypes Existing/New | Archetype items Used/Added |
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Events | 1) Events related to health issues 2)Notifications delivered to clinicians | EHR-CLUSTER-health-event.v0 EHR-INSTRUCTION.notification.v0 | Items.Event Name, Items.Description Notification.Data.Category.T |
Interventions | Health and lifestyle related interventions | EHR-EVALUATION.recommendation.v1 | Data.Recommendation |
Description of the archetypes utilized
Produced NoSQL database
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The first table is used for storing the personal details of the participants/patients, which are static and hence, rarely altered.
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The second table stores all the dynamically changing parameters, which are aggregated daily or at regular intervals.