Mapping Medical Records of Gas trectomy Patients to SNOMED C T - - PowerPoint PPT Presentation

Mapping Medical Records of Gas trectomy Patients to SNOMED C T

Hyeoun-Ae Park College of Nursing, Seoul National University Seoul, Korea

Background

• Electronic medical records (EMR) improves the ac

cessibility of medical information and contribute to the readability and completeness of information, al lowing users to search for and use information wit h more ease through greater integration of informa tion.

• Structured data entry is one way of collecting more

reliable ad more complete data in electronic medi cal records.

• Thus, the design of the structured data input interf

ace plays a major role in a successful implementat ion of electronic medical records

Background (Cont’d)

• An electronic nursing record system based o

n ICNP was introduced in early 2003 in Korea and went so far as to use the data collected by this system in decision making and resear ch.

• Unfortunately, a great deal of medical records

, such as admission notes, progress notes, a nd summary discharge notes are still left in u nstructured free text format.

Purpose of the Study

• To explore the ability of SNOMED CT to re

present narrative statements of medical re cord of gastrectomy patients.

Methods – Data collection

• Reviewed and collected narrative medical rec
• rds (admission notes, progress notes, and di

scharge notes) of every three patients with g astrectomy per month from September 2009 until no more narrative statements with new meaning are found.

• In total, 36 patients’ medical records with 281

hospitalized days were reviwed and collecte d.

Methods – Data analysis

• Dissected narrative medical records into single-meaning state

ments.

• Extracted unique statements by removing semantically redund

ant statements

• Classified these narrative statements into three groups

– Statements describing medical conditions of the patient (includin g signs/symptoms, lab results, diagnoses, and etc) – Statements describing procedures performed on the patients (inc luding medication, care plan, and etc.) – Other statements

• Extracted concepts from the unique statements describing me

dical conditions of the patients and medical procedures perfor med on the patients

Methods - Mapping

• Mapped extracted concepts to SNOMED CT

(2009-07-31 international edition) concepts u sing CliniClue Xlore

• Mapping of statements were classified into ful

ly mapped, partially mapped and not mapped .

• Mapping of concepts were classified into lexic

ally mapped, semantically mapped, mapped t

• a broader concept, mapped to a narrower c
• ncept, mapped to more than one concept, a

nd not mapped

Method - Validation

• The results of extracting concepts from statements

and mapping them to SNOMED CT concepts wer e verified by a group of domain experts.

• The experts consisted of a surgeon who performs

gastrectomies, a nurse with a Ph.D degree in nursi ng informatics with experience in SNOMED CT ma pping research, a doctoral student with experience in SNOMED CT and nursing informatics research, and a student with a master’s degree who maintai ns electronic medical records using the SNOMED CT.

Result

• In total, 4,717 single and 858 unique stateme

nts were collected from 281 days’ of medical records of 36 patients documented by 19 doc tors

• Out of 858 unique statements, 431 (50.2%) w

ere statements describing patients’ conditions , 246 (28.7%) describing procedures provide d to patients and 181 (21.7%) describing othe r than patients’ conditions and procedures.

Result (Cont’d)

! ! Patient conditions ! ! Treatments given ! ! Total ! ! ! !

• No. of

total st atements (%) !

• No. of

unique st atements (%) !

• No. of

total st atements (%) !

• No. of

unique s tatements (%) !

• No. of

total st atements (%) !

• No. of

unique st atements (%) ! Fully- mapped ! ! 3071 (96.8) ! 396 (91.9) ! 669 (80.0) ! 183 (74.4) ! 3740 (93.3) ! 579 (85.5) ! Partially- m apped ! ! 101 (3.2) ! 35 (8.1) ! 167 (20.0) ! 63 (25.6) ! 268 (6.7) ! 98 (14.5) ! Total ! ! 3172 (100.0) ! 431 (100.0) ! 836 (100.0) ! 246 (100.0) ! 4008 (100.0) ! 677 (100.0) !

Table 1. Mapping of Statements by SNOMED CT

Result (Cont’d)

! !

• No. of

unique concept(%) ! !

• No. of

total concept(%) ! !

Mapped to SNOMED CT 660(93.6) ! 9135(97.0) ! Lexically mapped ! ! 213(30.2) ! 1611(17.1) ! Semantically mapped ! ! 152(21.5) ! 4390(46.6) ! Mapped to a broader concept ! ! 97(13.8) ! 1204(12.8) ! Mapped to a narrower concept ! ! 8( 1.1) ! 53( 0.6) ! Mapped to more than one concept ! ! 190(27.0) ! 1877(19.9) ! Not mapped to SNOMED CT ! ! 45( 6.4) ! 280( 3.0) ! Total ! ! 705(100.0) ! 9415(100.0) !

Table 2. Mapping of Concepts by SNOMED CT

Discussion

• Higher mapping rate for narrative statements desc

ribing patients conditions than narrative statement s describing procedures (<- most procedures were already coded and documented in a structured wa y) => value of structured data entry for collecting in formation on patient conditions

• Lack of concept in SNOMED CT (ex, GOT(yes) an

d GPT(no))

• Pre-coordination versus post-coordination (ex, no

sputum versus no dsypnea)

Conclusion

• Almost 85% of narrative statements and 83%
• f concepts describing patient conditions an

d procedures were fully mapped to SNOMED

• CT. (higher than maping rate of narrative nur

sing statements ranging 47.6% to 79.2%)

• This high mapping rate implies that physician

s’ narrative medical records can be structured using SNOMED CT and used for structured data entry in electronic medical record syste m