Issues in Coding Hospital Intensive Care for Data Analysis 

Adeline J. Wilcox 

Department of Veterans Affairs 

810 Vermont Ave NW, Washington, DC 

Proceedings of the 2013 Federal Committee on Statistical Methodology (FCSM) Research Conference 

Abstract

Part of the American Recovery and Reinvestment Act (ARRA) of 2009, the Health Information Technology for Economic and Clinical Health Act (HITECH), offers incentive funding for Meaningful Use (MU) of electronic health

records (EHRs). Reporting of Clinical Quality Measures (CQMs) to the federal government is a MU of EHRs. For

the measure called ICU Venous Thromboembolism (VTE) Prophylaxis, Intensive Care Unit (ICU) admissions must

be identified. The Veterans Health Administration (VHA) Corporate Data Warehouse (CDW) holds several columns

describing Intensive Care. These data are loaded from its legacy EHR called the Veterans Health Information Systems

and Technology Architecture (VistA). Analysis of these unstructured free text data is all but impossible. 

Several ontologies with codes for Intensive Care are discussed, the Systematized Nomenclature of Medicine - Clinical

Terms (SNOMED CT) among them. Characteristics of hospitals, wards, rooms, beds, and physician specialty have

been used to classify Intensive Care. In contrast, the National Health Service Data Model and Dictionary for England

gives definitions describing the care provided to the patient. None of the ontologies lay out a ready means for coding

ICU data from the VHA CDW. 

VTE-1

I

Initial ICU Day = 0 days OR 

= 1 day

Initial ICU Day (in days) = 

ICU Admission Date – Admission Date

≥ 2 days

ICU 

Discharge 

Date 

Non-UTD Value

Missing

VTE-1

=UTD D

ICU LOS = ICU Discharge Date - ICU Admission Date

ICU LOS

= 0 days 

>= 1 day 

VTE-1

< 0 days B

VTE-1

X

VTE-1

X

< 0 days

VTE-1

B

Figure 1: Part of TJC’s Specification for VTE-1: Venous Thromboembolism Prophylaxis 

1 
Contents

1 Hospital Performance Measurement 2 

2 Hospital Wards, Rooms and Beds 3 

3 NHS Data Definitions and Data Standards Defining Intensive Care 4 

4 Variety and Change in Healthcare Operations 6 

5 Analysis of Free Text 6 

6 Catalog of Healthcare Ontologies 8 

6.1 Medicare UB-04 Revenue Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 

6.2 United States Health Information Knowledgebase (USHIK) . . . . . . . . . . . . . . . . . . . . . . . . . 8 

6.3 3M Healthcare Data Dictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 

6.4 Public Health Information Network Vocabulary Access and Distribution System . . . . . . . . . . . . . 10 

6.5 The Federal Health Information Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

6.6 Value Set Authority Center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 

6.7 International Health Terminology Standards Development Organisation . . . . . . . . . . . . . . . . . . 11 

6.8 Other Healthcare Classification Efforts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 

7 NHS Data Model and Data Dictionary Compared to SNOMED CT 11 

8 A Better Data Model for VA Intensive Care 13 

9 Corporate Data Warehouse (CDW) 13 

9.1 Extract, Transform, Load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 

10 VistA Metadata Repository 14 

11 Admission Discharge Transfer 15 

12 Administration 15 

12.1 Inpatient Evaluation Center (IPEC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 

12.2 Clinical Inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 

12.3 VHA National Bed Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 

12.4 Decision Support Systems Product Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 

12.5 Nursing Unit Mapping Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 

12.6 Seasonal Weekly Flu Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 

13 Summary 17 

1 Hospital Performance Measurement

With the aim of quality improvement, the Joint Commission (TJC) began the first national program of hospital

performance measurement in 1998 [11]. Not only TJC but the Centers for Medicare and Medicaid Services (CMS)

[41] and the Veterans Health Administration [54] use quality measures. 

For performance measurement, TJC provides detailed algorithms for the 6 measures in the Venous Thromboembolism

measure set[13]. Along with healthcare data documentation, SAS®programmers can use these algorithms to program

computation of performance measures. 

Figure 1, showing part of 1 of these algorithms, comes from the NQF-Endorsed Voluntary Consensus Standards for

Hospital Care [13]. Among the populations excluded by TJC specifications is the population of 

Patients who are direct admits to intensive care unit (ICU), or transferred to ICU the day of or the day after

hospital admission with ICU LOS greater than or equal to one day 

In identification of cases eligible for the denominator of the performance measure, the algorithm tests the Intensive

Care Unit Length of Stay, abbreviated ICU LOS in Figure 1. You can see this test in the diamond labeled ICU LOS.

For the purposes of this quality measure, TJC defines ICU as a Location, not a Level of Care [14]. 

2 
2 Hospital Wards, Rooms and Beds

Because VTE1 is an inpatient clinical quality measure, my analysis began with data in the Inpat schema. The diagram

shown in Figure 2 makes clear that our CDW does not follow the star schema described by Lupetin [38] . 

Dim.RoomBed

RoomBedSID

RoomBedDescription

Inpat.Inpatient

AdmitRoomBedSID

AdmitWardLocationSID

Inpat.PatientTransfer

RoomBedSID

WardLocationSID

Inpat.Lodger

RoomBedSID

WardLocationSID

Dim.WardLocation

WardLocationSID

BedSection

WardLocationName

Figure 2: CDW Tables Where I Started Looking for ICU Records 

In Figure 2, each rectangle represents a database table with the table name near the top and selected column names

listed below the table name. Following advice given in the document titled Best Practices to Query the VA Data

Warehouse, [37] I know that data users are expected to join selected rows from dimension tables to the rows in fact

tables containing patient data. As the Metadata report on the CDW Sharepoint site did not list Data Definitions for

any of the columns, I looked at the Entity Relationship Diagram (ERD) for the Inpatient schema and looked for tables

with names that indicated they might identify ICU stays. After choosing the tables named Dim.RoomBed and

Dim.WardLocation, I looked at the names of the columns within these tables and chose some that appeared as if they

might identify ICUs. At this point I ran into a problem encountered by other VA data users. As Kathleen Schutte

posted [61] 

We also struggled with the decision of which piece of available DSS pharmacy data to rely on. 

When I joined columns from Dim.RoomBed and Dim.WardLocation to 1 of their common Fact tables,

Inpat.PatientTransfer, values of the columns named RoomBed, BedSection, WardLocation and RoomBedDescription

did not clearly identify ICUs. In Table 1, I have listed selected rows from 2 of these columns. On some of the joined

rows, only a single value in 1 of the 4 columns identified the Location as an ICU. Some values of RoomBed and

WardLocation identified ICUs but not always consistently. A WardLocationName value of IMICU joined with a

RoomBedDescription value of STEPDOWN. Providing a level of care intermediate between Intensive Care and that

given on a general ward, step-down units differ from ICUs [3]. On other records joined through the table named

Inpat.PatientTransfer, 1 column value was blank while the value of another column joined was nonmissing. 

I abandoned this line of analysis. 

In the VistA Metadata Repository, the statement 

Select from those ACTIVE beds in the ROOM-BED file which are assigned to the WARD LOCATION

chosen for this movement. 

tells me that whatever relationship existed between ROOM-BED values and WARD LOCATION values was

destroyed when the dimension tables named Dim.RoomBed and Dim.WardLocation were created in the CDW. These

2 tables cannot be joined directly on any keys; they can only be joined through the fact table named

Inpat.PatientTransfer. 

For room identification, only values of the column named RoomBedDescription can be found in the CDW; room

numbers are not available. Within the VistA package named ENGINEERING, a free text field named SYNONYM in

the file named ENG SPACE gives colloquial names often used instead of room numbers. However, data from the

Engineering package have not been extracted, transformed and loaded into the CDW. 

3 
3

The field named SYNONYM is described as: 

Alternative (informal) designations of physical locations. For example, room 214A-137 may be better

known as room 1 of the MICU. In this case, you could use MICU-1 as a synonym for room 214A-137

[22]. 

WardLocationName RoomBedDescription 

1N HOSPICE ICU 

ICUM OVERFLOW 

ZZ2BSICU PRIVATE ROOM 

SICU 

WCICU PRIVATE 

3 ICU (OBS) NO DESCRIPTION 

IMICU STEPDOWN 

4MICU PSEUDO BED 

OBS-MICU PRIVATE 

1N MED ICU 

2D-SICU NONE 

Table 1: WardLocationName Joined to RoomBedDescription through Fact Table 

We need a data model. C. J. Date gives 2 definitions of the term data model, [17] his second is 

A data model is a model of the persistent data of some particular enterprise. 

He tells us a data model is a database design and 

...we might speak of the data model for some bank, or some hospital, or some government department. 

Now, we can see that the CDW is not a ”faithful model of reality” [17]. Looking at the Inpatient schema of the CDW,

it appears the CDW architecture does not benefit from any data model, having been built without one.

Although hospital beds typically have a one-to-one or many-to-one relationship to hospital rooms and hospital rooms

are nested within wards, other arrangements exist. Wards may be synonomous with rooms. Like the wards shown in

Civil War images, a ward may comprise many beds in a single large room. Some hospital architecture features a

”decentralized nursing station design” where a desk for a single nurse is situated near 1 or more rooms. 

Within the metadata for the VistA File named WARD(S) WHICH CAN ASSIGN are 2 statements implying that the

relationship of wards to beds is not simply one-to-one. 

Enter the ward (a pointer to the WARD LOCATION file) which can utilize this bed. Choose, from the

available listing, the ward(s) which are permitted to place a patient in this bed. [48] 

As a VHA Directive states [57], temporary bed Locations in the Emergency Department or elsewhere in the hospital

may be used. Finally, the number of ICU beds in a hospital may be dynamic, continuously adjusted to meet demand

within available resources [43]. 

NHS Data Definitions and Data Standards Defining Intensive Care

The National Health Service (NHS) Data Model and Dictionary for England [40] explicitly nests Hospital Beds

within Wards, describing a Ward as 

• ”A group of Hospital Beds with associated treatment facilities managed as a single unit for the purposes of

staffing and treatment responsibilities.” 

• ”A critical care unit will comprise one WARD if the Hospital Beds and associated treatment facilities are managed as a single unit.” 

4 
Definitions reproduced below tell us that NHS uses the concepts of both Location and Level of Care, listed in Table

2, to define Intensive Care [40], also known as Critical Care. 

• A new CRITICAL CARE PERIOD starts when the PATIENT is admitted to a critical care location regardless

of CRITICAL CARE LEVEL . 

• A CRITICAL CARE PERIOD ends when the PATIENT is discharged from the critical care location, or dies, 

or the care that is being delivered in a non-standard location is CRITICAL CARE LEVEL National Code 00 

Level 0 or National Code 01 Level 1. 

• A CRITICAL CARE PERIOD does not include the following: 

a. Surgical and anaesthetic intra-operative care 

b. Post-operative care within an operating department except where level 2 or level 3 care are provided for more

than 4 hours 

c. Care delivered in a cardiac or coronary care unit 

d. Imaging procedures 

e. Endoscopy procedures 

f. Care delivered in an Accident and Emergency Department 

Helpful to data analysts, the NHS Data Model and Dictionary gives very precise definitions. High dependency units,

also called step-down units, provide a level of care intermediate between an ICU and a general ward [3]. Stepdown

care can be more precisely classified with the NHS National Codes. 

A recent VHA Directive makes clear that Location alone does not determine Level of Care [57]. 

When a patient requires admission to a critical care unit and no Intensive Care Unit (ICU) bed is

available, it is an absolute requirement that the patient receive ICU-level care in an alternative location

including monitoring, staffing, and treatment consistent with ICU standards. 

and 

b. Temporary Bed Location. A temporary bed location is a designated place where a patient awaiting

inpatient care can be cared for until a bed in the destination unit is available. Temporary bed locations

may include but are not limited to the Post Anesthesia Care Unit for ICU overflow patients; the

Observation Unit; and the ED or UCC for newly admitted patients. It may also include short-term use of

a higher level of care (for example an ICU bed for a telemetry inpatient admission) while awaiting the

appropriate location. 

This Directive clearly states that patients may receive a higher level of care than they need. Patients who do not need

Intensive Care may be admitted to an ICU, waiting there until another bed becomes available. The NHS Data Model

and Data Dictionary names concepts of Location and Level of Care exactly as this Directive. 

Vance et al. [69] briefly describe a VHA patient acuity classification system by which Intensive Care patients can be

distinguished from others. I did not find these data in the CDW. 

National Code Description 

00 Level 0 (Patients whose needs can be met through normal ward care in an acute hospital) 

01 Level 1 (PATIENTS at risk of their condition deteriorating, or those recently relocated from 

higher levels of care, whose needs can be met on an acute ward with additional advice and 

support from the critical care team.) 

02 Level 2 (PATIENTS requiring more detailed observation or intervention including support 

for a single failing organ system or post-operative care and those ’stepping down’ from 

higher levels of care.) 

03 Level 3 (PATIENTS requiring advanced respiratory support alone or monitoring and support 

for two or more organ systems. This level includes all complex PATIENTS requiring 

support for multi-organ failure.) 

Table 2: NHS National Codes for Critical Care Levels [40] 

5 
4 Variety and Change in Healthcare Operations

Classification of ICUs present a challenge. 

Intensive care units (ICUs) vary significantly from hospital to hospital with respect to structure, services

provided, personnel and their level of expertise, and organizational characteristics [29]. 

Hospitals have been described as unique in the way each is run [36]. Trotter and Uhlman open their second chapter of

their book with the epigraph [68] 

When you’ve seen one medical practice, you’ve seen one medical practice. 

Touting VistA’s adaptability, Tom Munnecke [33], a VistA developer, gives us the old saying 

If you’ve seen one VA hospital, you’ve seen one VA hospital. 

The Veterans Administration (VA) developed VistA decades ago. Unconcerned with the utility of data coming from

multiple and diverse VistAs, Munnecke dismissed criticism raised when the VA accepted VistA for use in all VA

hospitals [33]. Hammond warned that, given the ”highly individualized nature” of healthcare, no single EHR would

meet universal acceptance. [30] More than 30 years later, the quest for ”interoperability” continues [1]. Even among

VistAs, no complete semantic interoperability exists. In my second analysis, described in Section 5, I found that data

from different hospitals don’t describe the same concept. 

VA cautions researchers that this means VistA data are not standardized across all VistA sites [50]. From the time VistA

development began, VA staff considered each VA healthcare facility unique and in need of EHR customization [31].

Apparently, extensive VistA customization at many VistA sites prevented the authors of the Bed Control Menu User

Manual from clearly describing data entry steps for recording patient transfer. VistA customization is the antithesis of

standards. For example, the Patient Information Management System (PIMS), Admission, Discharge, Transfer (ADT)

instructions under the heading Transfer a Patient [42] state 

Action in this function varies depending upon what transfer types have been defined as active at your

facility. 

and 

The actions in this option will vary depending upon your site’s definition of each bed control movement 

type. 

Recently, Elizabeth Alpern reported finding EHRs not only highly customized to each site’s work flow but mutable at

each site as well [2]. 

Again, researchers are advised that ”VistA files are maintained in 128 centers and data may not be compatible among

sites because of local modifications.” [50]. The query listed below gave a result set of 130 VistA sites. 

s e l e c t d i s t i n c t Sta3n , a c t i v e 

from [ cdwwork ] . [ dim ] . [ v i s t a s i t e ] 

where Sta3n > 0 and a c t i v e = ’Y’ 

When VA’s standardization initiative closes about 2 years hence, there will ”133 production VistA instances” [72]. 

Further challenging CDW data users, ”Of the 57,000 data fields in VistA, 34% contain data stored as unstructured, free

text.” [50]. Trotter and Uhlman remind us that normalized data cannot be produced from free text [68]. Many columns

in the CDW come from VistA fields that allow users to enter any text they choose. Cervo and Allen tell us data entry

personnel are imaginative and ”capable of entering the very same information in may different ways, especially when

utilizing free-form entry fields” [10]. That perfectly explains the variability of values entered into many VistA fields. 

5 Analysis of Free Text

To study the variety of names used for ICUs, I analyzed free text values. I limited my analysis to searching for only the

2 strings ’ICU’ and ’Intensive Care’, as shown in the Structured Query Language (SQL) listed here. 

s e l e c t d i s t i n c t T r e a t i n g S p e c i a l t y N a m e } 

from [CDWWork ] . [ Dim ] . [ T r e a t i n g S p e c i a l t y ] }

where T r e a t i n g S p e c i a l t y N a m e l i k e ’ \%[ i I ] [ cC ] [ uU]\% ’ } 

or T r e a t i n g S p e c i a l t y N a m e l i k e }

’ \%[ i I ] [ nN ] [ tT ] [ eE ] [ nN ] [ sS ] [ i I ] [ vV ] [ eE ] ␣ [ cC ] [ aA ] [ rR ] [ eE ]\% ’ } 

6 
Consequently, where CCU means Critical Care Distinct Values 

Unit, not Coronary Care Unit, and ’INT CARE’ Table Column in Division 648 means Intensive Care, not Intermediate Care, I 

excluded those values from my analysis. I used

the 10 columns from 4 tables listed in Table 3. Dim.WardLocation WardLocationName 12 

After unduplicating all 10 columns I found 1,956 Dim.WardLocation BedSection 0 

unique values. Dim.RoomBed RoomBed 28 

Dim.RoomBed RoomBedDescription 1 Next, I looked for the VA Hospital, identified by Dim.Location WardLocation 12 Division, with the greatest variety of text strings

identifying ICUs. For the column named Ward- Dim.Location LocationName 118 

LocationName, I found the Portland, OR VA Dim.Location LocationAbbreviation 5 

Hospital used 12 different strings in Fiscal Year Dim.Location PhysicalLocation 2 

(FY) 2013. These strings are listed in the sec- Dim.Location TreatingSpecialtyName 2 ond column of Table 16. In terms of variety of Dim.Specialty Specialty 2

values, in this division WardLocationName was 

redundant to WardLocation. The 12 values of 

WardLocationName reappeared among the 118 Table 3: Columns Joined to Inpatient Records From FY13 

unique values of column named LocationName 

recorded by the Portland, OR VA Hospital during FY13. 

This query gave a result set of only 1 row, having a BedSection value equal to ”HEMO ICU”. 

s e l e c t B e d S e c t i o n 

from [ cdwwork ] . [ dim ] . [ WardLocation ] 

where Sta3n =648 and B e d S e c t i o n l i k e ’%ICU%’ 

Because I did not find this value among FY13 inpatient records, it could be an outdated record. 

For the Portland, OR VA Hospital, Values of the column named RoomBed ranged from 3DICU-1 to 3DICU-28. 

Table 4 shows the unique values of 5 more columns found in FY13 inpatient records from the Portland, OR VA Hospital. 

TreatingSpecialtyName Specialty PhysicalLocation RoomBedDescription LocationAbbreviation 

MEDICAL ICU 

SURGICAL ICU 

MEDICAL ICU 

SURGICAL ICU 

MICU 

SICU 

ICU ICU 

MICU 

SICU 

W MICU 

W SICU 

Table 4: Unique Values from 5 Columns. Data from the Portland, OR VAMC Hospital 

Values from the column named TreatingSpecialty listed

in Table 5 do not clearly describe Intensive Care. Because construction of the CDW is not complete, the column named TreatingSpecialty could not be joined to patient records. 

Table 6 lists some of the problematical values found by

querying 10 CDW columns for Intensive Care Units. All

contain the string ICU but none unambiguously represent ICUs. 

With 3 different meanings, the acronym PICU illustrates

the lack of healthcare data standards. First, On VA’s 

Acronym Lookup site, PICU is given as Patient Intensive Care Unit. Second, a VHA document spells out

PICU as Psychiatric Intensive Care Unit [19]. Third, according to SNOMED CT, PICU means Pediatric ICU. 

ICU UROLOGY 

NEUROLOGY ICU 

OPHTHALMOLOGY SICU 

PLASTIC SURGERY ICU 

POST ICU 

PSYCHIATRY(PICU) 

CARDIOLOGY TELEMETRY ICU 

HEAD AND NECK ICU 

MEDICAL ICU/OBSERVATION 

ORTHO (INCL HAND) SICU 

L-MED MICU HOSPICE 

MICU/PALLIATIVE CARE 

JC-ICU/CCU 

Table 5: Some Ambiguous TreatingSpecialty Name Values 

7 
Figure 3: VA Acronym Lookup 

PACU is an acronym with duplicate meanings. Figure 3 gives 2 definitions for this acronym and advises attending to

the document’s context. Unlike EHR users, data users cannot get any context from the columns in the CDW. 

BU-8A/MICU,CCU,CVU NUR 2B2 PAICU 

NUR NICU/6B BO NUR 4 EAST MICU/CCU 

ELP-NSG-TESTICULAR SELF EXAM NUR PICU 

AL-ICU READING ROOM NUR PSY-WEST (PICU) 

NOL ORTHOTIC LAB HUNNICUTT NUR WCICU 

PHL INSOMNIA CVT CONNECTICUT SMICU virtual bed 

HRB-SMOKE CESS PRACTICUUM NUR MICU STEP 

L/NURSING PEDICURE CCU/MICU/TELE/ISO 

O/PICU RECREATION THERAPY GP pickup ICU 

ZZMU-AURICULAR/ACCUPUNCTURE TLOVICU 

TO-REC TX-HORTICULTURE ICUBOARDER 

BU-MEDICAL/SURGICAL ICU NUR RICU 

Table 6: Problematical Values from 10 CDW columns 

Render et al. [60] tabled 3 problems they encountered using the physician-centered concept of Treating Specialty to

identify ICU patients. The reported wide variation in ICU use among VA hospitals [12], may be due, at least in part, to

problems with the classification of ICU beds. 

BedSection, a concept unique to VA hospitals, is perhaps best defined in VHA Handbook 1907.04, Patient Treatment

File (PTF) Coding Instructions [20]. 

Involving concepts of both a medical or surgical specialty and Location, VHA defines Bedsection as

A bedsection is the general treatment type (specialty) being provided to a patient on an officially designated

(or ”VA-approved”) ward or bed location. 

As we saw earlier, relying on the column named BedSection to identify ICUs would have missed those in the Portland,

OR VA Hospital. 

6 Catalog of Healthcare Ontologies

6.1 Medicare UB-04 Revenue Codes

Table 7 lists selected Medicare Revenue Codes [18]. Evidently, Medicare deems Pulmonary care less intensive than

Psychiatric Intensive Care [18]. Coding problems could be a reason for unexplained variability in use of Intensive Care

[64]. Unlike the SNOMED CT classification, Medicare does not include a Respiratory Intensive Care Unit in their

classification. 

6.2 United States Health Information Knowledgebase (USHIK)

The Consolidated Health Informatics (CHI) website offers a collection of standards [25], including SNOMED CT.

None of their Data Elements describe Intensive Care as either a Level of Care or a Location. 

8 
020X Intensive Care

0 General Classification INTENSIVE CARE or (ICU) 

1 Surgical ICU/SURGICAL 

2 Medical ICU/MEDICAL 

3 Pediatric ICU/PEDS 

4 Psychiatric ICU/PSTAY 

6 Intermediate–ICU ICU/INTERMEDIATE 

7 Burn Care ICU/BURN CARE 

8 Trauma ICU/TRAUMA 

9 Other Sub-acute Care ICU/OTHER 

021X Coronary Care

0 General Classification CORONARY CARE or (CCU) 

1 Myocardial infarction CCU/MYO INFARC 

2 Pulmonary care CCU/PULMONARY 

3 Heart Transplant CCU/TRANSPLANT 

4 Intermediate–CCU CCU/OTHER 

9 Other 

023X Incremental Nursing Charge Rate

0 General Classification NURSING INCREM 

1 Nursery NUR INCR/NURSERY 

2 OB NUR INCR/OB 

3 ICU (includes transitional NUR INCR/ICU care) 

4 CCU (includes transitional NUR INCR/CCU care) 

5 Hospice NUR INCR/HOSPICE 

9 Other NUR INCR/OTHER 

Table 7: Medicare UB-04 Revenue Codes 

6.3 3M Healthcare Data Dictionary

The proprietary 3M Healthcare Data Dictionary (HDD)

promises semantic interoperability [66]. The Healthcare Data Value Search Date Dictionary (HDD) is a controlled clinical vocabulary server

that 3M has continuously expanded and maintained for over

15 years. The 3M HDD makes it possible to map and man- TICU 11Oct2013 

age medical terminologies, integrate content and standardize NICU 15Oct2013 

healthcare data. STICU 17Oct2013 

DICU 24Oct2013 Holding over 2.6 million concepts, 3M claims the HDD can HICU 24Oct2013 be used to map terms used in an EHR to concepts in HDD,

thereby standardizing the data fromm the source EHR. As the RICU 24Oct2013 

HDD is proprietary, how it might be used to process data ex- CICU 24Oct2013 

tracted from VistA is unclear. How will the HDD handle un- PAICU 25Oct2013 

defined acronmyns like those listed in Table 10? How will QICU 30Oct2013 the HDD handle acronyms such as PICU that have multiple

meanings? VICU 30Oct2013

ORICU 30Oct2013 

Hammond foresaw that even the largest data dictionaries will PHICU 30Oct2013 

omit terms used by some [30]. 

Table 10: 

Not Found in the VA Acronym Lookup 

9 
Concept Name 1 Relationship Type Concept Name 2 

Critical care unit Parent/Child Trauma critical care unit 

Critical care unit Parent/Child Surgical critical care unit 

Critical care unit Parent/Child Neurosurgical critical care unit 

Critical care unit Parent/Child Surgical cardiothoracic critical care unit 

Critical care unit Parent/Child Respiratory critical care unit 

Critical care unit Parent/Child Prenatal critical care unit 

Critical care unit Parent/Child Neurology critical care and stroke unit 

Critical care unit Parent/Child Pediatric critical care unit 

Trauma critical care unit Parent/Child Pediatric trauma critical care unit 

Burn critical care unit Parent/Child Pediatric burn critical care unit 

Medical critical care unit Parent/Child Medical cardiac critical care unit

Medical critical care unit Parent/Child Medical/Surgical critical care unit 

Medical critical care unit Parent/Child Pediatric medical critical care unit 

Surgical critical care uni Parent/Child Pediatric surgical critical care unit 

Neurosurgical critical care unit Parent/Child Pediatric neurosurgical critical care unit 

Surgical cardiothoracic critical care unit Parent/Child Pediatric surgical cardiothoracic critical care unit 

Respiratory critical care unit Parent/Child Pediatric respiratory critical care unit

Pediatric critical care unit Parent/Child Neonatal critical care unit [Level II/III] 

Pediatric critical care unit Parent/Child Neonatal critical care unit [Level III] 

Pediatric critical care unit Parent/Child Pediatric burn critical care unit 

Pediatric critical care unit Parent/Child Pediatric surgical cardiothoracic critical care unit 

Pediatric critical care unit Parent/Child Pediatric medical critical care unit 

Pediatric critical care unit Parent/Child Pediatric medical/surgical critical care unit 

Pediatric critical care unit Parent/Child Pediatric neurosurgical critical care unit 

Pediatric critical care unit Parent/Child Pediatric respiratory critical care unit 

Pediatric critical care unit Parent/Child Pediatric surgical critical care unit 

Pediatric critical care unit Parent/Child Pediatric trauma critical care unit 

Medical/Surgical critical care unit Parent/Child Pediatric medical/surgical critical care unit 

Table 8: PHIN VADS 

6.4 Public Health Information Network

Vocabulary Access and Distribution System

Public Health Information Network (PHIN) Vocabulary Access and Distribution System (VADS) comes from the

Centers for Disease Control (CDC). All concepts listed in Table 8 have their Code System named Healthcare Service

Location (HL7) and a publication date of 2010-08-12 [24]. The artificial hierarchical relationships listed in Table 8

are useless for classifying VHA ICUs. 

6.5 The Federal Health Information Model

For an Encounter, the Federal Health Information Model gives an attribute named admissionLevelOfCare, described 

as 

”Indicates the acuity level assigned to the patient at the time of admission.” - HL7 Version 2.8, PV2-40.

Possible values include (from HL7 Table 432): Acute; Chronic; Comatose; Critical; Improved; Moribund 

[23] 

10 
Metathesaurus Browser 

Concept Semantic Types 

Level of Care – Critical Care Idea or Concept 

care by level of intensity Qualitative Concept 

transfer to intensive care unit Health Care Activity 

Intensive care Health Care Activity 

intensive care unit Health Care Related Organization 

Manufactured Object 

Table 9: Unified Medical Language System ® Terminology Services 

309904001 Intensive care unit (environment) 

397821002 Patient transfer to intensive care unit (procedure) 

297945004 Unexpected admission to intensive care unit (procedure) 

305351004 Admisssion to intensive care unit (procedure) 

Table 11: Result of search for ICU in SNOMED CT Browser on 30 October 2013 

6.6 Value Set Authority Center

SNOMED CT codes for ICU Admission or Transfer can be found at the Value Set Authority Center [44]. Keith

Boone reports a lack of satisfaction with currently available value sets [6]. 

6.7 International Health Terminology Standards Development Organisation

Along with geographic entities such as countries and states, the International Health Terminology Standards

Development Organisation views the ”Intensive care unit” as an example of an environment concept [55]. In Table 

11, I listed the result set from my search of the Unified Medical Language System ® Terminology Services [62] for

ICU. The records listed in Table 11 can also be found in PHIN VADS [63]. SNOMED CT gives us multiple concepts

for Intensive Care. See Table 11 and Figure 13. How can all these different concepts describe Intensive Care?

Compared with the NHS Data Model, SNOMED CT shows a lack of agreement in the US on the concept of Intensive

Care. Results from searching the Metathesaurus Browser, shown in Table 9, will not help us classify ICUs. We need a

better controlled clinical vocabulary for Intensive Care than SNOMED CT gives us. 

6.8 Other Healthcare Classification Efforts

At this writing, ONC’s S&J Framework has not produced yet another ontology [27]. Neither has the Clinical

Information Modeling Initiative [32]. 

7 NHS Data Model and Data Dictionary Compared to SNOMED CT

On the next page, Table 12 gives a comparison I made in October 2012, showing how little overlap in ICU varieties

exists between the NHS Data Model and Data Dictionary and SNOMED CT. 

11 
12 

Table 12: CRITICAL CARE UNIT FUNCTION Description from the NHS Data Model and Dictionary f

Compared with SNOMED CT Intensive Care unit (environment) Concept

NHS Data Model and Dictionary for England 

Adult Facilities (Patients more than 19 years old on admission predominate)

01 Non-specific, general adult critical care patients predominate 02 Surgical adult patients (unspecified specialty) 03 Medical adult patients (unspecified specialty) 05 Neurosciences adult patients predominate 

06 Cardiac surgical adult patients predominate 

07 Thoracic surgical adult patients predominate08 Burns and plastic surgery adult patients predominate 09 Spinal adult patients predominate10 Renal adult patients predominate

11 Liver adult patients predominate

12 Obstetric and gynaecology critical care patients predominate 90 non standard location using a ward area 

Children and Young People Facilities (Patients aged greater than or equal to 29 days to less than 19 years predominat

04 Paediatric Intensive Care Unit (Paediatric critical care patients predominate) 16 Ward for children and young people 17 High Dependency Unit for children and young people 18 Renal Unit for children and young people 19 Burns Unit for children and young people 92 Non standard location using the operating department for children and young people 

Neonatal Facilities (Patients aged less than 29 days on admission predominate) 

13 Neonatal Intensive Care Unit (Neonatal critical care patients predominate) 14 Facility for Babies on a Neonatal Transitional Care Ward 15 Facility for Babies on a Maternity Ward 

Other settings 

91 non standard location using the operating department 
309908003 Metabolic intensive care unit (environment) 

309909006 Neurological intensive care unit (environment) 

309911002 Respiratory intensive care unit (environment) 

404821007 Psychiatric intensive care unit (environment) 

418433008 Surgical intensive care unit (environment) 

426439001 Burns intensive care unit (environment) 

441994008 Medical intensive care unit (environment) 

133903000 Care of intensive care unit patient (regime/therapy) 

305353001 Admission to pediatric intensive care unit (procedure) 

397945004 Unexpected admission to intensive care unit (procedure) 

183446009 Admission to cardiac intensive care unit (procedure) 

183447000 Admission to respiratory intensive care unit (procedure) 

183448005 Admission to neurological intensive care unit (procedure) 

183449002 Admission to metabolic intensive care unit (procedure) 

Table 13: Partial result of search on Intensive Care Unit in SNOMED CT Browser 

8 A Better Data Model for VA Intensive Care

The concepts of Location and Level of Care used in the NHS Data Dictionary and Data Model to model Intensive

Care can, if data are collected, be used in data analysis. It seems that developing a healthcare ontology without data

analysis in mind can produce an ontology of uncertain utility. We have learned 

The location of patients does not determine their care. [21] 

Supporting the statement made by The Intensive Care Society, Veterans Health Administration standards require

provision of Intensive Care in alternate Locations when no ICU bed is available [57]. That VHA patients can receive

Intensive Care in Locations such as the Post Anesthesia Care Unit, the Emergency Department or an Urgent Care

Clinic proves that the Location of VHA ”patients does not determine their care”. 

I propose modeling Intensive Care with a state-transition model [39]. A patient in hospital is a state-dependent object.

A hospital stay can be described as a sequence of chronological events that may bring about change(s) in the patient’s

state. States would be Levels of Care whether in an ICU or another hospital Location, such as Post Anesthesia Care

(PAC), Surgery or Hospice. Birth, Death, Admitted and Discharged would be Terminators. In the following

hypothetical scenario I illustrate a conceptual data model of Intensive Care using a state-transition model. 

Admitted to the Emergency Department, a patient receives a level of care described by Level 2 to support his life. He

is admitted to hospital and goes to surgery. Following surgery his gurney is rolled into a Post Anesthesia Care Unit

where he continues to receive Level 2 care. Some hours later, hospital staff push his gurney into an ICU where Level

2 care continues. His condition improves and he is sent to a stepdown unit providing care corresponding to Level 1.

His condition deteriorates, he is returned to the ICU for Level 3 care. Within hours, his provider writes a Do Not

Resuscitate order. During this patient’s last hours in this ICU, he receives Level 0 care. His care plan is changed to

discharge him to a Hospice facility. 

My recommendation to add Level of Care to an open source EHR received no response [74]. That change would

likely mean extensive modification of much Massachusetts General Hospital Utility Multi-Programming System

(MUMPS) code. Of course, data on the patient’s Location would still need to be collected. 

9 Corporate Data Warehouse (CDW)

The CDW holds unaggregated transaction level data from the entire nation. It is built with Microsoft SQL Server and

SQL Server Integration Services [47], also known as SSIS packages. In the production CDW, all tables are designated

as either dimension or fact tables. 

9.1 Extract, Transform, Load

After extraction and transformation, VistA data are loaded into the Veterans Administration Corporate Data

Warehouse (CDW) [49]. No documentation of data transformation during extract, transform, load (ETL) processes

turned up. Figure 4 gives a representation of data extraction from VistA. As it is my understanding that data are not 

13 
Figure 4: Data Extraction from VistA 

stored in Oracle between extraction from VistA and load into the CDW, I believe the diagram shows processes at an

early point in building the CDW [45]. ”Field descriptions that are available within the VistA system are extracted to

the CDW Metadata... Unfortunately, VistA field descriptions tend to be cryptic and/or technical.” [9]. 

10 VistA Metadata Repository

Developed in secret [59], VistA has no manual [8]. Some documentation prepared after VistA implementation may be

found in the VA Software Document Library [51]. Within the Department of Veterans Affairs Data Architecture

Repository, under the tab labeled VHA, data users can find the VistA Metadata Repository. The VistA Metadata

Repository gives metadata for the ”VistA Platinum image”. 

VistA Platinum is the master version of the complete VistA system maintained by the Standards and

Compliance Office within the Office of Information and Technology. [52] 

It is not clear if VistA Platinum is one of the ”production VistA instances” in actual use. Descriptions given for VistA

Field Names can read as if they are outdated or incorrect. For example, the description for the VistA Field named

SERVICE CATEGORY in the File Named VISIT and Numbered 9000010 tells us 

This service category field represents what kind of service was provided. The IHS definition is

represented by a set of codes. This field can be used by IHS to screen on service categories. The VA

continues to populate this field to be backward compatible with IHS utilities depending on this field. The

VA uses the Service Provided file (#150.1) to define a more detailed file of services provided. When a

VA user identifies the Service Provided (field 15001), a trigger will automatically populate this service

category based on relationships between entries in File 150.1 and the set of codes for this field. The

population of this field is dependent upon entries provided by applicaions as well as default values and

caculated codes. [sic] [52] 

However, searching the VistA Metadata Repository for a file named Service Provided uncovers only a file named File

Num 150.1, named ANCILLARY DSS ID. Moreover, the file named Service Provided is numbered 162.03. As the 

Indian Health Service uses a VistA variant, [68] I interpret the undefined abbreviation IHS to mean Indian Health

Service. The final sentence in the VistA Field description above suggests that not only are inputs to this field

numerous but their traceability within the various VistAs and VistA packages has been lost to mankind. No wonder

data users choose to rely on word of mouth instead of the available data documentation. 

Unintentionally, the Heath Services Research Data Discussion listserv promotes this word of mouth. For example,

when a data user found an empty cell instead of the metadata he sought, he posted an inquiry to the listserv [56]. Data

documentation is so lacking that posting an ”anecdotal” story based on a conversation with an individual outside of

VHA data processing is accepted as a valid contribution to knowledge of VistA and the CDW [65]. The lack of

documentation and metadata coupled with reliance on word of mouth means different data users choose different

columns from the CDW for analysis. 

Bentley [5] cites work by Barry Devlin in which Devlin likened using a data warehouse without metadata to exploring

the Wild West without maps. Without the metadata they need, data users will be as lost as pioneers without maps. 

14 
IPEC Clinical Inventory National Bed Control System 

MICU Medical ICU MICU 

MICU/CCU Specialty ICU 

CCU CCU (Cardiac Care) 

SICU Surgical ICU SICU 

Mixed Mixed ICU 

Table 14: Classifications Used by IPEC and the Clinical Inventory 

11 Admission Discharge Transfer

VistA’s Admission/Discharge/Transfer (ADT) package built upon the Medical Administration System package [59].

Vista includes not only the VA’s electronic health record [50], but administrative functions such as ADT [42]. HL7

standard messages represent the hospital healthcare events known as Admission, Discharge and Transfer [30]. VA

documented ADT HL7 messages in 1995 [67]. A re-engineering of the ADT package, called ADT Encapsulation

began in early 2005 but was terminated by the end of that year [71]. An undated intranet site headed BMS Project

Center gives the Bed Management Solution’s status as ”BMS is currently in Class II Limited Deployment” [46].

Apparently, automated reporting on beds cannot be achieved without changing VistA or building software on top of

VistA. 

Nowhere in the document titled Bed Management Solution (BMS) User Guide does the term Bed Section appear [15].

Instead, the BMS User Guide uses the term ”Bed Groupings”, an apparently identical concept. Further confusing the

reader, the BMS User Guide also uses the terms ”ward group” and ”type group”. In a screenshot purporting to

illustrate use of the application, redundancy among the fields named ”VISTA WARD NAMES”, ”VISTA WARD

SPECIALTY”, ”BMS TYPE GROUP”, and ”BMS WARD GROUP TEXT” is evident, as are consequent

inefficiencies. Beginning without a clear understanding of concepts and an information model, BMS design lacked

vision. This situation supports the assertion that an enterprise architecture framework requires domain ontologies [4]. 

The Bed Management Solution (BMS) User Guide describes its column named ”Type of Bed/Ward Required” as ”The

type of bed/ward required for the particular ailment of that patient. Because it has a unique and more flexible means

of providing beds to meet patients’ needs, the Portland VAMC has found it cannot use the BMS [16]. 

Although data from all VistA sites are poured together into the CDW, listing data by Division value can reveal a

pattern unique to a single hospital. See Table 16. At the Portland, OR VA hospital, values of WardLocationName

contain not only text identifying the Location by hospital floor, e.g. 3D, but text suffixed to these Location identifiers.

The suffixed text appear to be abbreviations for Treating Specialties. It appears that the Portland, OR VA Hospital has

repurposed this VistA field in an undocumented manner. I requested descriptions spelling out the text of these

apparent abbreviations from the Portland, OR, VA Hospital, but did not get them. Without a code set, the meanings of

the values listed in the second column of Table 16 remain unknown. 

12 Administration

Kolodner makes clear VistA was not designed to be a management information system. Originally named the

Decentralized Hospital Computer System (DHCP), VistA was designed to meet the needs of individual healthcare

facilities [26]. Indeed, data for management was seen only as a byproduct of VistA. Never intended as a management

information system for central administration of more than 100 hospitals, free text values combined from VHA’s

many VistAs are not ready for identification of ICUs. So stovepiped are VHA reporting systems, no 2 of the 6

discovered use the same classification of ICUs. Apparently, willing programmers individually papered over the ICU

classification problem but collectively failed to conceal it. 

12.1 Inpatient Evaluation Center (IPEC)

IPEC classifies ICUs based on ”VistA Ward Treating Specialty” [34] as shown in Table 14. In an e-mail message

addressed to me, Richard Pham wrote 

IPEC also has the same difficulties in Ward Identification as we do [58]. 

12.2 Clinical Inventory

The Clinical Inventory v2.0 application can be found on the Reports and Measures Portal (RAMP). It can be run to

produce reports of ICUs by ICU Type at each VA hospital [70]. These ICU Types are tabled in Table 14. 

15 
DSS Production Unit Code Name 

E1 Surg ICU 

E2 Telemetry ICU 

E3 Medical ICU 1 

E4 Medical ICU 2 

E5 Neuro ICU 

E8 Med/Surg ICU Combined 

Table 15: Production Unit Names Containing String ”ICU” 

MAS Wards Listed in the Values of WardLocationName That 

Nursing Unit Mapping Application Joined to Inpatient Records From FY13 

3DANS-ICU 

√

3DCARD-ICU 

3DCIT-ICU 

√

3DEN-ICU 

√

3DGS1-ICU 

√

3DGU-ICU 

√

3DINTS-ICU 

√

3DKID-ICU 

√

3DNS-ICU 

3DOP-ICU 

√

3DOR-ICU 

√

3DPLS-ICU 

√

3DTRA-ICU 

√

3DTS-ICU 

√

3DVAS-ICU 

Table 16: Critical Care Wards Listed for 648 Portland, OR 

12.3 VHA National Bed Control System

The VHA Support Service Center (VSSC) supports the National Bed Control System. On 10 June 2013, I ran this

point and click application. From the pulldown menu labeled ”Bed Service”, I selected MICU and SICU. The

resulting report listed counts for both MICUs and SICUs. Clicking on the Data Definition button did not reveal any

data definitions. 

12.4 Decision Support Systems Product Units

The Decision Support Office, now named the Managerial Cost Accounting Office, published the Decision Support

Systems (DSS) classification of hospital wards [53] including those listed in Table 15. They did not share a mapping

of VA hospital wards to Production Unit codes with me when I requested one [73]. 

12.5 Nursing Unit Mapping Application

On 12 December 2013, I ran the Nursing Unit Mapping Application (NUMA) for Facility 648, Portland, OR,

requesting a report listing all Unit Types. For Unit Type ”Acute - Critical Care” and DSS Production Units ”241E81 -

UE81 - Med/Surg ICU combined” the ”MAS Wards” were listed as shown in the first column of Table 16. 

12.6 Seasonal Weekly Flu Report

During flu season, VHA publishes an internal weekly Influenza Activity report based on stay level data on ICU

utilization [35]. How data are collapsed into the 3 categories of ”Medical ICU and Step Down”, ”Surgical ICU and

Step Down”, and ”Cardiac ICU and Step Down” is not explained. 

16 
13 Summary

Of ICUs, Atul Gawande [28] gave his qualitative assessment: ”Nowhere in health care do we expend more

resources.” Spending on Intensive Care has been estimated at one percent of the U.S. Gross Domestic Product [64].

To better answer questions about the amount of resources expended on Intensive Care, we need a more

comprehensive classification of ICUs. 

VHA Directive 1009 uses the concepts of Location and Level of Care to describe Intensive Care [57]. So does the

NHS Data Model and Dictionary for England. These concepts, already in use, are superior to the concepts given by

SNOMED CT. Only in the United Kingdom, with development of the NHS Data Model and Dictionary and its

national codes has there been progress on useful classification of ICUs. No progress toward meaningful analysis of its

diverse VistAs and many free text fields within them has been made in the VHA since Hammond, writing nearly 20

years ago, [30] advocated controlled clinical vocabularies. 

The NHS Data Model for Intensive Care, using concepts of both Level of Care and Location can be used in an

incremental approach to modeling healthcare data [7]. Modeling the hospitalized patient as a state-dependent object

could bring order to data capture, storage, and analysis of inpatient care records. The next ontology of Intensive Care

should be designed to meet requirements for data analysis and administration. 

Credit Notices

SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS 

Institute Inc. in the USA and other countries. ®indicates USA registration. 

Microsoft and SQL Server are either registered trademarks or trademarks of Microsoft Corporation in the United 

States and/or other countries. 

Acknowledgements

I am grateful to Dan Gillman, Research Mathematical Statistician, Mathematical Statistics Research Center, Bureau of

Labor Statistics for helpful suggestions. David Reed, BlueCross BlueShield of Tennessee, Inc. told me about

Medicare Revenue Codes. 

Disclaimer of Endorsement

The views and opinions of the author expressed herein do not necessarily reflect those of the Office of Informatics and

Analytics, the Veterans Health Administration or the Department of Veterans Affairs. Reference herein to any specific

commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily

constitute or imply its endorsement, recommendation, or favoring by the Department of Veterans Affairs. 

17 
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[74] Adeline Wilcox. Two ideas from the NHS Data Model and Dictionary for England. E-mail, 31 December 2012. 

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