tags for hospital & healthcare - various different Attributes

[tagtheworld]

tags for hospital & healthcare - various different Attributes

hi dear community,

i have several datasets - datasets derived from the living atlas AT ESRI

see https://livingatlas.arcgis.com/en/home/

datasets from hospitals

canada
usa
germany

**canada
**https://www.arcgis.com/home/item.html?id=bf1249f3c9a64bcc809a6912c19c966d

**US-Hospitals:
**map: https://hub.arcgis.com/datasets/53b8031b906e43c4a4dbcf2250022ca0_0?geometry=-88.345%2C-8.577%2C-139.322%2C74.544
data: https://www.arcgis.com/home/item.html?id=53b8031b906e43c4a4dbcf2250022ca0

**german hospitals:
**- Map: https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/348b643c8b234cdc8b1b345210975b87_0

• Daten: https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/348b643c8b234cdc8b1b345210975b87_0/data

note: these datasets have different Attributes .

why is this so?

see below some details

canada
https://www.arcgis.com/home/item.html?id=bf1249f3c9a64bcc809a6912c19c966d

The Open Database of Healthcare Facilities (ODHF) is a collection of open data containing the names, types, and locations of health facilities across Canada. It is released under the Open Government License - Canada.
The ODHF compiles open, publicly available, and directly-provided data on health facilities across Canada. Data sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. This database aims to provide enhanced access to a harmonized listing of health facilities across Canada by making them available as open data. This database is a component of the Linkable Open Data Environment (LODE).

Data sources and methodology
The inputs for the ODHF are datasets whose sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. These datasets were available either under one of the various types of open data licences, e.g., in an open government portal, or as publicly available data. In certain cases, data were obtained directly from administrative sources. Details of the sources used are available in the ODHF metadata.

The data sources used do not deploy a uniform classification system. The ODHF harmonizes facility type by assigning one of three types to each health facility. This was done based on the facility type provided in the source data as well as using other research carried out for the purpose. The facility types used in the ODHF are: ambulatory health care services, hospitals, and nursing and residential care facilities. However, alternative medicine (e.g., herbalists) and specialist areas (e.g., chiropractors, dentists, mental health specialists, etc.) are not in scope for the current ODHF version (version 1.0).

The ODHF does not assert having exhaustive coverage and may not contain all facilities in scope for the current version. While efforts have been made to minimize these, facility type classification and geolocation errors are also possible. While all data are released on the same date, the dates as of which data are current depends on the update dates of the sources used.

A subset of geo-coordinates available in the source data were validated using the internet and updated as needed. When latitude and longitude were not available, geocoding was performed for some sources using address data in the source. Some coordinates were also removed from the original sources when it was determined they were derived from postal codes or other aggregate geographic areas as opposed to street address.

Deduplication was done to remove duplicates for cases where sources overlapped in coverage.

This first version of the database (version 1.0) contains approximately 9,000 records. Data were collected by accessing sources between November 2019 and March 2020.

The variables included in the ODHF are as follows:

Index
Facility Name
Source Facility Type
ODHF Facility Type
Provider
Unit
Street Number
Street Name
Postal Code
City
Province or Territory
Source-Format Street Address
Census Subdivision Name
Census Subdivision Unique Identifier
Province or Territory Unique Identifier
Latitude
Longitude
For more information on how the addresses and variables were compiled, see the metadata that accompanies the ODHF.

This is a republishing of the data that is freely available from Statistics Canada at https://www.statcan.gc.ca/eng/lode/databases/odhf. Records that did not have a latitude and longitude value (about 1,500) were geocoded using the Esri World Geocoder. For more information on this data set please review the Statistics Canada metadata document.

the datasets:

Canada 1
Canada 2
USA
Deutschland

+------------------------------+------------------------------+------------------------+----------------------------------------------+--------------------------------+
| Canada 1 (ca 10000 recprds)  | Canada 2 (ca 10000 recprds)  | US  (ca.4400 records)  |                                              | Deutschland (ca. 2800 records) |
+------------------------------+------------------------------+------------------------+----------------------------------------------+--------------------------------+
|                              |                              |                        | example                                      |                                |
| index                        | Address_1                    | FID                    |                                              | OBJECTID                       |
| Name                         | City                         | OBJECTID               |                                              | Name                           |
| provider                     | County_Nam                   | Provider_N             |                                              | Einrichtung                    |
| ODHF facility type           | Emergency_                   | State_1                |                                              | Gesundheitsattribut            |
| unit                         | FID                          | County_Nam             |                                              | Gesundheitsattribut            |
| Street                       | Hospital_O                   | Hospital_T             | Acute Care  / Critical Access                | Spezialrichtung                |
| CSDname                      | Hospital_T                   | Hospital_O             | Proprietary  / Voluntary non-profit – Church | Betreiber                      |
| Prov                         | Name_new                     | Emergency_             |                                              | Betreibertyp                   |
| postal code                  | OBJECTID                     | ZipCode                |                                              | Telefon                        |
| CSDuid                       | PhoneNum                     | PhoneNum               |                                              | Website                        |
| Pruid                        | Provider_N                   | Address_1              |                                              | email                          |
| latitude                     | State_1                      | City                   |                                              | Fas                            |
| longitude                    | ZipCode                      | Name_new               | Howard Memorial Hospital                     | Adresse                        |
|                              |                              |                        |                                              | Adresse voll                   |
|                              |                              |                        |                                              | Straße                         |
|                              |                              |                        |                                              | Hausnummer                     |
|                              |                              |                        |                                              | Postleitzahl                   |
|                              |                              |                        |                                              | Stadt                          |
|                              |                              |                        |                                              | Hausname                       |
|                              |                              |                        |                                              | Stadtteil                      |
|                              |                              |                        |                                              | Unterbezirk                    |
|                              |                              |                        |                                              | Bezirk                         |
|                              |                              |                        |                                              | Provinz                        |
|                              |                              |                        |                                              | Bundesland                     |
|                              |                              |                        |                                              | Konfession                     |
|                              |                              |                        |                                              | Religion                       |
|                              |                              |                        |                                              | Notaufnahme                    |
|                              |                              |                        |                                              | Räume                          |
|                              |                              |                        |                                              | Betten                         |
|                              |                              |                        |                                              | Kapazität                      |
|                              |                              |                        |                                              | Rollstuhlgerecht               |
|                              |                              |                        |                                              | wikidata                       |
|                              |                              |                        |                                              | wikipedia                      |
|                              |                              |                        |                                              | ORIG_FID                       |
|                              |                              |                        |                                              | GlobalID                       |
+------------------------------+------------------------------+------------------------+----------------------------------------------+--------------------------------+

why do we have such different attributes?

[eteb3]

I’m not sure I understand the question (but I’m not a techie).

Do you mean, why do the different datasets have different information?

eg,

Canada 1 gives lat-long but Canada 2 does not?
Deutschland includes Konfession, but the others don’t?

[tagtheworld]

hello dear eteb3,

many thanks for the quick reply. Great to hear from you. Exactly - this is the interesting thing.

btw. **canada ** - if we have a closer look at canada:

see here the **description and the methodology: **
https://www.arcgis.com/home/item.html?id=bf1249f3c9a64bcc809a6912c19c966d

The Open Database of Healthcare Facilities (ODHF) is a collection of open data containing the names, types, and locations of health facilities across Canada. It is released under the Open Government License - Canada.

The ODHF compiles open, publicly available, and directly-provided data on health facilities across Canada. Data sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. This database aims to provide enhanced access to a harmonized listing of health facilities across Canada by making them available as open data. This database is a component of the Linkable Open Data Environment (LODE).

**Data sources and methodology
**The inputs for the ODHF are datasets whose sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. These datasets were available either under one of the various types of open data licences, e.g., in an open government portal, or as publicly available data. In certain cases, data were obtained directly from administrative sources. Details of the sources used are available in the ODHF metadata.

with the description of attributes

A subset of geo-coordinates available in the source data were validated using the internet and updated as needed. When latitude and longitude were not available, geocoding was performed for some sources using address data in the source. Some coordinates were also removed from the original sources when it was determined they were derived from postal codes or other aggregate geographic areas as opposed to street address.

Deduplication was done to remove duplicates for cases where sources overlapped in coverage.

This first version of the database (version 1.0) contains approximately 9,000 records. Data were collected by accessing sources between November 2019 and March 2020.

The variables included in the ODHF are as follows:

Index
Facility Name
Source Facility Type
ODHF Facility Type
Provider
Unit
Street Number
Street Name
Postal Code
City
Province or Territory
Source-Format Street Address
Census Subdivision Name
Census Subdivision Unique Identifier
Province or Territory Unique Identifier
Latitude
Longitude
For more information on how the addresses and variables were compiled, see the metadata that accompanies the ODHF.
This is a republishing of the data that is freely available from Statistics Canada at https://www.statcan.gc.ca/eng/lode/databases/odhf.

and see here the dataset of the 9900 records

https://www.arcgis.com/home/item.html?id=bf1249f3c9a64bcc809a6912c19c966d#data

well some differences - at least at the canadian-dataset.

and if we compare all to the german dataset and set of attributes - there is a big difference.

i wonder which dataset one should take - if he wants to present data from various countries

USA
Canada
Germany
Spain
Brazil
Italy
etc. etx.

is there a set that is used generally…!?

which datafields you would leave out in such a final data-set?! Which fileds you would add !?

look forward to hear from you

regards

[eteb3]

I think this is simply a question of different governments having a need for different data: in Germany ‘Konfession’ is presumably of importance to the health system to know; I don’t know, but I’m guessing in the US this sort of cross-over between religion and the state is something the state is supposed to be indifferent to.

It’s just different people building different databases for their different needs - there’s no obvious reason why they should be consistent.

As for how to compare them, that is a data science question that’s beyond me! If it were me (I don’t even have a science degree…) I’d be looking for the shared or similar-enough attributes (by semantic content, not by the name of the attribute) and discarding any attribute that doesn’t have a close counterpart in the other datasets.

The lesson of our school grades debacle in the UK this summer was that you can’t get out of a dataset information that was never there. So unsophisticated as my solution is, I can’t see a better one: you can’t (from that data) discover what the ‘Konfession’ is of the US hospitals. Though perhaps you could translate a postal address into a very rough lat/long.

[tagtheworld]

dear eteb3

many many thanks for the reply and all your help.

have a great day.

greetings