ASTM E2171 - 02(2013) Standard Practice for Rating-Scale Measures Relevant to the Electronic Health Record (Withdrawn 2017).pdf

1、Designation: E2171 02 (Reapproved 2013) An American National StandardStandard Practice forRating-Scale Measures Relevant to the Electronic HealthRecord1This standard is issued under the fixed designation E2171; the number immediately following the designation indicates the year oforiginal adoption o

2、r, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.1. Scope1.1 This standard addresses the identification of data ele-ments from the EHR defi

3、nitions in Practice E1384 that haveordinal scale value sets and which can be further defined tohave scale-free measurement properties. It is applicable to datarecorded for the Electronic Health Record and its papercounterparts. It is also applicable to abstracted data from thepatient record that ori

4、ginates from these same data elements. Itis applicable to identifying the location within the EHR wherethe observed measurements shall be stored and what is themeaning of the stored data. It does not address either the usesor the interpretations of the stored measurements.2. Referenced Documents2.1

5、ASTM Standards:2E177 Practice for Use of the Terms Precision and Bias inASTM Test MethodsE456 Terminology Relating to Quality and StatisticsE691 Practice for Conducting an Interlaboratory Study toDetermine the Precision of a Test MethodE1169 Practice for Conducting Ruggedness TestsE1384 Practice for

6、 Content and Structure of the ElectronicHealth Record (EHR)3. Terminology3.1 DefinitionsFull definitions and discussion of Scale-Free Measurement Terms are given in Annex A1.3.2 Definitions of Terms Specific to This Standard:3.2.1 adaptive measurementadvantage of measurement toaccount for missing da

7、ta.3.2.2 additivityrating scale adherence to associativity andcommutability.3.2.3 bias analysisinvestigation of considerations relativeto subject or area of performance.3.2.4 calibrationprocess of establishing additivity andreproducability of a data set.3.2.5 concatenationprocess of measurement uses

8、 enumer-ated physical unit quantities equal to the magnitude of themeasured item.3.2.6 constructname of the conceptual domain measured.3.2.7 convergenceclosing of the differences in sequentialmeasure estimates.3.2.8 countingbasic activity upon which measurement isbased and utilizes enumeration.3.2.9

9、 dataobservation made in such a way that they leadto generalization.3.2.10 data quality/ statistical consistency/ model fitestablishment of whether the measuring instrument is affectedby the object of measurement.3.2.11 determinismmeasurement model that requirescounts to be sufficient for reproducin

10、g the pattern of theresponses over the length of the instrument.3.2.12 dimensionalityproperty of having multiple compo-nents of a measured value.3.2.13 equality/cocalibrationprocess of ensuring that dif-ferent instruments measure the same property.3.2.14 erroruncertainty of measured properties.3.2.1

11、5 estimation algorithmsmathematical specificationof an observational framework.3.2.16 incommensurable/commensurablemeasure valueof the same quantity does/does not depend upon rating/responses of the rating construct and does not/does remainconstant.3.2.17 instrumentsensing device having a defined sc

12、ale.3.2.18 intra and inter-laboratory testingvariability testingusing the same setting/measure/operator as opposed to differentsetting/measure/operators.3.2.19 item response/latent trait theoryanalytic modelsthat forego prescriptive parameter separation, sufficiency andscale and sample free data sta

13、ndards for additional descriptiveparameters.1This practice is under the jurisdiction of ASTM Committee E31 on HealthcareInformatics and is the direct responsibility of Subcommittee E31.25 on HealthcareData Management, Security, Confidentiality, and Privacy.Current edition approved March 1, 2013. Pub

14、lished March 2013. Originallyapproved in 2002. Last previous edition approved in 2008 as E2171 02(2008).DOI: 10.1520/E2171-02R13.2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org. For Annual Book of ASTMStandards volume informati

15、on, refer to the standards Document Summary page onthe ASTM website.Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United StatesNOTICE: This standard has either been superseded and replaced by a new version or withdrawn.Contact ASTM International

16、www.astm.org) for the latest information13.2.20 items/item-bankpart of survey statements/testquestions for adaptive administration.3.2.21 levels of measurementnature of scale of measure-ment.3.2.22 logitscale unit using logarithms of odds ratios.(P/1P).3.2.23 mathematical entitiesconcepts that can

17、be taught orlearned through what is already known.3.2.24 measurementdetermining in units the value of aproperty in a scale having magnitude (that is, ratio or differ-ence).3.2.25 metaphor in measurementsuspension of disbeliefof some areas or properties in the name of estimating magni-tude.3.2.26 met

18、ricmeasure of a property in defined units.3.2.27 missing datause of uncalibrated data in instru-ments with varying numbers of items.3.2.28 multi-faceted measurementuse of measurementmodels that have more than two basic parameters.3.2.29 ordinal dataone scale for measurement.3.2.30 populationuniverse

19、 of elements relevant to mea-surement of a particular construct.3.2.31 probabilistic conjoint measurementframework fordemonstrating data quality, statistical consistency, and modelfit of non-deterministic measures with a stable order of facets.3.2.32 quantificationcocalibration of different construc

20、tswith respect to the same property (variable) in a commonmetric.3.2.33 Rasch analysis measurement and modelsanalyticmodel specifying the observational framework and data qualitymeasures for quantification.3.2.34 raw scoresum of ratings or count of direct re-sponses in a given measurement event.3.2.

21、35 reliabilityratio of variation to error or signal tonoise.3.2.36 repeatabilityvariability of measurements in asingle setting by a single operator using the same measuringinstrument.3.2.37 reproducibilityvariability of measurements in dif-ferent settings.3.2.38 root mean square errormathematical al

22、gorithm fordetermining the variation due to error of the estimates.3.2.39 samplesubset of measured population.3.2.40 sample sizemagnitude of the measured population.3.2.41 scale-free/scale-dependentmeasures not affectedby the instrument employed as opposed to measures that are soaffected.3.2.42 sepa

23、rability theorem/parameter separationabilityof measures to be independent of the instrument selected andability of the instruments item calibrations to be independentof the sample measured.3.2.43 softwarepackages of machine code used for dataanalysis.3.2.44 specific objectivitydata satisfying the se

24、parabilitytheorem.3.2.45 standardizedcommon conventions for instruments,reference measurement material, scales and units of measurefor a measurement process.3.2.46 suffciencystatistics that extract all available infor-mation from the data.3.2.47 targetinglack of floor an/or ceiling effects in mea-su

25、rement.3.2.48 transparencyability to “look through” raw scoresto the composite ratings producing that score (see also suff-ciency).3.2.49 unit of measurementcommon conventions for theappropriate smallest basic measures for a given construct.3.2.50 validity/construct/contentboth content and con-struc

26、t must make sound theoretical sense to be consideredvalid.3.2.51 variableattribute of the property being measured.4. Significance and Use4.1 The simplicity and practicality of Raschs probabilisticscale-free measurement models have brought within reachuniversal metrics for educational and psychologic

27、al tests, andfor rating scale-based instruments in general. There are at least3 implications to the application of Raschs models to thehealth-related calibration of universal metrics for each of thevariables relevant to the Electronic Health Record (EHR) thatare typically measured using rating scale

28、 instruments.4.1.1 First, establishing a single metric standard with adefined range and unit will arrest the burgeoning proliferationof new scale-dependent metrics.4.1.2 Second, the communication of the information per-taining to patient status represented by these measures(physical, cognitive, and

29、psychosocial health status, quality oflife, satisfaction with services, etc.) will be simplified.4.1.3 Third, common standards of data quality will be usedto evaluate and improve instrument performance. The vastmajority of test and survey data quality is currently almostcompletely unknown, and when

30、quality is evaluated, it is viamany different methods that are often insufficient to the task,misapplied, misinterpreted, or even contradictory in their aims.4.1.4 Fourth, currently unavailable economic benefits willaccrue from the implementation of measurement methodsbased on quality-assessed data

31、and widely accepted referencestandard metrics. The potential magnitude of these benefits canbe seen in an assessment of 12 different metrological improve-ment studies conducted by the National Science and Technol-ogy Council (Subcommittee on Research, 1996). The averagereturn on investment associate

32、d with these twelve studies was147 %. Is there any reason to suppose that similar instrumentimprovement efforts in the psychosocial sciences will result inmarkedly lower returns?4.2 Until now, it has been assumed that the Practice E1384would necessarily have to stipulate fields for the EHR thatE2171

33、 02 (2013)2would contain summary scores from commonly used func-tional assessment, health status, quality of life, and satisfactioninstruments. This is because standards for rating scale instru-ments to date have been entirely content-based. Those whohave sought “gold” or criterion standards that wo

34、uld commanduniversal respect and relevance have been stymied by theimpossibility of identifying content (survey questions andrating categories) capable of satisfying all users needs. Com-munication of patient statistics between managers andclinicians, or payors and providers, may require one kind of

35、information; between providers and referral sources, otherkinds; between providers and accreditors, yet another; amongclinicians themselves, still another; and even more kinds ofinformation may be required for research applications.4.2.1 For instance, payors may want to know outcomeinformation that

36、tells them what percentage of patients dis-charged can function independently at home. A hospitalmanager, referral source, or accreditor might want to knowmore detail, such as percentages of patients discharged whocan dress, bathe, walk, and eat independently. Clinicians willwant to know still more

37、detail about amounts of independence,such as whether there are safety issues, needs for assistivedevices, or specific areas in which functionality could beimproved. Researchers may seek even more detail yet, as theyevaluate differences in outcomes across treatment programs,diagnostic groups, facilit

38、ies, levels of care, etc.4.2.1.1 Members of each of these groups have, at sometime, felt that their particular information needs have not beenmet by the tools designed and developed by members ofanother group. Despite the fact that the information providedby these different tools appears in many dif

39、ferent forms and atdifferent levels of detail, to the extent that they can be shownto measure the same thing, they can do so in the same metric.This is the primary result of the introduction of Raschsprobabilistic scale-free measurement models. The differentpurposes guiding the design of the instrum

40、ents will stillcontinue to impact the two fundamental statistics associatedwith every measure: the error and model fit. More general, andalso less well-designed instruments, will measure with moreerror than those that make more detailed and consistentdistinctions. Data consistency is the key to scal

41、e-free measure-ment.4.3 The remainder of this document (1) identifies, in Section5, the fields in the current Practice E1384 targeted for changefrom a scale-dependent to a scale-free measurement orienta-tion; (2) lists referenced ASTM documents; (3) defines scale-free measurement terms, often contra

42、sting them with theirscale-dependent counterparts; (4) addresses the significanceand use of scale-free measures in the context of the EHR; (5)lists, in AnnexA2, scientific publications documenting relevantinstrument calibrations; (6) briefly presents some basic opera-tional considerations; (7) lists

43、 minimum and comprehensivearrays of EHR database fields; and (8) lists, in Annex A3, thereferences made in presentation of the measurement theory,estimation methods, etc.4.4 Publications of calibration studies referencing this prac-tice and the associated standard practice should require:4.4.1 The u

44、se of measures, not scores, in all capture of datafrom the EHR for statistical comparisons;4.4.2 The reporting of both the traditional reliability statis-tics (Cronbachs alpha or the KR20) and the additive, linearseparation statistics (Wright & Masters, 1982), along with theirerror and variation com

45、ponents, for both the measures and thecalibrations;4.4.3 Aqualitative elaboration of the variable defined by theorder of the survey questions or test items on the measurementcontinuum, preferably in association with a figure displayingthe variable;4.4.4 Reporting of means and standard deviations for

46、 eachof the three essential measurement statistics, the measure, theerror, and the model fit;4.4.5 Statement of the full text of at least a significantsample of the questions included on the instrument;4.4.6 Specification of the mathematical model employed,with a justification for its use;4.4.7 Spec

47、ification of the error estimation and model fitestimation algorithms employed, with mathematical details andjustification provided when they differ from those routinelyused;4.4.8 Evaluation of overall model fit, elaborated in a reporton the details of one or more of the least and most consistentresp

48、onse patterns observed;4.4.9 Graphical comparison of at least two calibrations ofnew instruments from different samples of the same populationto establish the invariance of the item calibration order acrosssamples;4.4.10 Graphical comparison of measures produced by atleast two subsets of items on ne

49、w instruments to establish theinvariance of the person measure order across scales (collec-tions of items);4.4.11 Graphical comparison of new instrument calibrationswith the calibrations produced by other instruments intended tomeasure the same variable in the same population, to establishthe potential for sample-free equating of the instruments andestablishment of reference standards;4.4.12 At least a useable prototype of the instrumentemployed, with the worksheet laid out to produce informativequantitative meas