Detection of drug interactions by using an automated Tool-A prospective study Article Swipe
Shammin Haque
,
Nazmun Nahar Alam
,
Moinuddin Ahmed
,
Nusrat Sultana
,
Sumaiya Mushroor
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.5958/0974-360x.2018.00435.3
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.5958/0974-360x.2018.00435.3
Objective: To determine the opinion of clinicians about using an automated online tool for instant detection of drug interactions. Material and methods: This descriptive type of prospective cross-sectional study was carried out among the general physicians and specialists involved in various hospitals in Dhaka from September to November 2017. A pre-designed and pre-tested questionnaire comprising of both closed and open ended questions was used. After taking informed consent, they voluntarily participated in the study. Data were analysed using Microsoft Excel 2003 and results were expressed using descriptive statistics such as frequency and percentages. Some questions had multiple options, therefore, the sum of percentage is not always 100%. Results: Among a total of 50 participants, most of them (52%) could not recall the correct definition of drug interaction, though 60% know its types. Only 36% know drug interactions can produce adverse effects. Most (46%) were not sure about common drug interactions occurring in all specialities. Fiftysix percent report patients with polypharmacy suffer maximum from drug interactions, followed by elderly and children, revealed by 28% and 24%, respectively. Majority (60%) stated steroids produce maximum drug interactions among all drug groups, followed by 48% and 18% who revealed antibiotics and beta blockers are also responsible. Fifty percent followed by 30% and 20% reported hypertension, diabetes mellitus and bronchial asthma are the disease conditions where drug interactions occur commonly. Seventyfour percent find searching information on drug interaction while prescribing is time consuming from various sources. Simultaneously, 54% think an online drug interaction checker can be an instant easy option, while 50% state it can also prevent drug interactions. Generic name of drugs, common adverse effects, dose and type of drug interaction were opted to be included in the checker by 58%, 62%, 40% and 38%, respectively. More than half (58%), think outdoor patients will be benefitted most. Majority (54%) prefer online checker to be presented in both English and Bangla language. Fifty percent encounter drug interactions every six months, of moderate intensity by 62%. Fourty percent always give priority to drug interactions while prescribing. Sixty percent depend on drug leaflets for drug interaction information while prescribing, followed by 44% on drug reference books and 34% recall from previous knowledge. Only 24% always check drug interaction information while prescribing. Fiftyfour percent find available information inadequate to avoid drug interaction. Maximum (84%) previously did not use any online checker. Conclusion: Online drug databases can reduce the time for information procurement, ease decision making while prescribing, improve prescribing potential and prevent drug related adverse events.
Related Topics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5958/0974-360x.2018.00435.3
- OA Status
- hybrid
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2886620593
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2886620593Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5958/0974-360x.2018.00435.3Digital Object Identifier
- Title
-
Detection of drug interactions by using an automated Tool-A prospective studyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-01-01Full publication date if available
- Authors
-
Shammin Haque, Nazmun Nahar Alam, Moinuddin Ahmed, Nusrat Sultana, Sumaiya MushroorList of authors in order
- Landing page
-
https://doi.org/10.5958/0974-360x.2018.00435.3Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5958/0974-360x.2018.00435.3Direct OA link when available
- Concepts
-
Drug, Computer science, Medical physics, Medicine, Biomedical engineering, PharmacologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2886620593 |
|---|---|
| doi | https://doi.org/10.5958/0974-360x.2018.00435.3 |
| ids.doi | https://doi.org/10.5958/0974-360x.2018.00435.3 |
| ids.mag | 2886620593 |
| ids.openalex | https://openalex.org/W2886620593 |
| fwci | 0.0 |
| type | article |
| title | Detection of drug interactions by using an automated Tool-A prospective study |
| biblio.issue | 6 |
| biblio.volume | 11 |
| biblio.last_page | 2345 |
| biblio.first_page | 2345 |
| topics[0].id | https://openalex.org/T10496 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9833999872207642 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2717 |
| topics[0].subfield.display_name | Geriatrics and Gerontology |
| topics[0].display_name | Pharmaceutical Practices and Patient Outcomes |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2780035454 |
| concepts[0].level | 2 |
| concepts[0].score | 0.4790990650653839 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q8386 |
| concepts[0].display_name | Drug |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.4450753629207611 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C19527891 |
| concepts[2].level | 1 |
| concepts[2].score | 0.42235293984413147 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1120908 |
| concepts[2].display_name | Medical physics |
| concepts[3].id | https://openalex.org/C71924100 |
| concepts[3].level | 0 |
| concepts[3].score | 0.412280797958374 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[3].display_name | Medicine |
| concepts[4].id | https://openalex.org/C136229726 |
| concepts[4].level | 1 |
| concepts[4].score | 0.3778058588504791 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q327092 |
| concepts[4].display_name | Biomedical engineering |
| concepts[5].id | https://openalex.org/C98274493 |
| concepts[5].level | 1 |
| concepts[5].score | 0.2518036365509033 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q128406 |
| concepts[5].display_name | Pharmacology |
| keywords[0].id | https://openalex.org/keywords/drug |
| keywords[0].score | 0.4790990650653839 |
| keywords[0].display_name | Drug |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.4450753629207611 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/medical-physics |
| keywords[2].score | 0.42235293984413147 |
| keywords[2].display_name | Medical physics |
| keywords[3].id | https://openalex.org/keywords/medicine |
| keywords[3].score | 0.412280797958374 |
| keywords[3].display_name | Medicine |
| keywords[4].id | https://openalex.org/keywords/biomedical-engineering |
| keywords[4].score | 0.3778058588504791 |
| keywords[4].display_name | Biomedical engineering |
| keywords[5].id | https://openalex.org/keywords/pharmacology |
| keywords[5].score | 0.2518036365509033 |
| keywords[5].display_name | Pharmacology |
| language | en |
| locations[0].id | doi:10.5958/0974-360x.2018.00435.3 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210228335 |
| locations[0].source.issn | 0974-360X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0974-360X |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Research Journal of Pharmacy and Technology |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Research Journal of Pharmacy and Technology |
| locations[0].landing_page_url | https://doi.org/10.5958/0974-360x.2018.00435.3 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5087093918 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Shammin Haque |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Shammin Haque |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5059756304 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-2408-7190 |
| authorships[1].author.display_name | Nazmun Nahar Alam |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Nazmun N. Alam |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5001276827 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-0728-6780 |
| authorships[2].author.display_name | Moinuddin Ahmed |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Moinuddin Ahmed |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5101632780 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-4761-1453 |
| authorships[3].author.display_name | Nusrat Sultana |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Nusrat Sultana |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5039209395 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Sumaiya Mushroor |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Sumaiya Mushroor |
| authorships[4].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.5958/0974-360x.2018.00435.3 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Detection of drug interactions by using an automated Tool-A prospective study |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10496 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9833999872207642 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2717 |
| primary_topic.subfield.display_name | Geriatrics and Gerontology |
| primary_topic.display_name | Pharmaceutical Practices and Patient Outcomes |
| related_works | https://openalex.org/W2899084033, https://openalex.org/W2748952813, https://openalex.org/W3031052312, https://openalex.org/W3032375762, https://openalex.org/W1995515455, https://openalex.org/W2080531066, https://openalex.org/W3108674512, https://openalex.org/W1506200166, https://openalex.org/W2048182022, https://openalex.org/W2604872355 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.5958/0974-360x.2018.00435.3 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210228335 |
| best_oa_location.source.issn | 0974-360X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0974-360X |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Research Journal of Pharmacy and Technology |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Research Journal of Pharmacy and Technology |
| best_oa_location.landing_page_url | https://doi.org/10.5958/0974-360x.2018.00435.3 |
| primary_location.id | doi:10.5958/0974-360x.2018.00435.3 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210228335 |
| primary_location.source.issn | 0974-360X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0974-360X |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Research Journal of Pharmacy and Technology |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Research Journal of Pharmacy and Technology |
| primary_location.landing_page_url | https://doi.org/10.5958/0974-360x.2018.00435.3 |
| publication_date | 2018-01-01 |
| publication_year | 2018 |
| referenced_works_count | 0 |
| abstract_inverted_index.A | 49 |
| abstract_inverted_index.a | 109 |
| abstract_inverted_index.50 | 112 |
| abstract_inverted_index.To | 1 |
| abstract_inverted_index.an | 9, 244, 251 |
| abstract_inverted_index.as | 89 |
| abstract_inverted_index.be | 250, 280, 300, 309 |
| abstract_inverted_index.by | 166, 171, 189, 205, 285, 328, 353 |
| abstract_inverted_index.in | 39, 42, 71, 151, 282, 311 |
| abstract_inverted_index.is | 103, 235 |
| abstract_inverted_index.it | 258 |
| abstract_inverted_index.of | 5, 16, 25, 55, 101, 111, 115, 124, 266, 274, 325 |
| abstract_inverted_index.on | 230, 343, 355 |
| abstract_inverted_index.to | 46, 279, 308, 335, 380 |
| abstract_inverted_index.18% | 192 |
| abstract_inverted_index.20% | 208 |
| abstract_inverted_index.24% | 366 |
| abstract_inverted_index.28% | 172 |
| abstract_inverted_index.30% | 206 |
| abstract_inverted_index.34% | 360 |
| abstract_inverted_index.36% | 133 |
| abstract_inverted_index.40% | 288 |
| abstract_inverted_index.44% | 354 |
| abstract_inverted_index.48% | 190 |
| abstract_inverted_index.50% | 256 |
| abstract_inverted_index.54% | 242 |
| abstract_inverted_index.60% | 128 |
| abstract_inverted_index.all | 152, 185 |
| abstract_inverted_index.and | 20, 36, 51, 58, 81, 91, 168, 173, 191, 196, 207, 213, 272, 289, 314, 359, 412 |
| abstract_inverted_index.any | 390 |
| abstract_inverted_index.are | 199, 216 |
| abstract_inverted_index.can | 137, 249, 259, 397 |
| abstract_inverted_index.did | 387 |
| abstract_inverted_index.for | 13, 346, 401 |
| abstract_inverted_index.had | 95 |
| abstract_inverted_index.its | 130 |
| abstract_inverted_index.not | 104, 119, 144, 388 |
| abstract_inverted_index.out | 31 |
| abstract_inverted_index.six | 323 |
| abstract_inverted_index.sum | 100 |
| abstract_inverted_index.the | 3, 33, 72, 99, 121, 217, 283, 399 |
| abstract_inverted_index.use | 389 |
| abstract_inverted_index.was | 29, 62 |
| abstract_inverted_index.who | 193 |
| abstract_inverted_index.2003 | 80 |
| abstract_inverted_index.24%, | 174 |
| abstract_inverted_index.38%, | 290 |
| abstract_inverted_index.58%, | 286 |
| abstract_inverted_index.62%, | 287 |
| abstract_inverted_index.62%. | 329 |
| abstract_inverted_index.Data | 74 |
| abstract_inverted_index.More | 292 |
| abstract_inverted_index.Most | 141 |
| abstract_inverted_index.Only | 132, 365 |
| abstract_inverted_index.Some | 93 |
| abstract_inverted_index.This | 22 |
| abstract_inverted_index.also | 200, 260 |
| abstract_inverted_index.beta | 197 |
| abstract_inverted_index.both | 56, 312 |
| abstract_inverted_index.dose | 271 |
| abstract_inverted_index.drug | 17, 125, 135, 148, 163, 182, 186, 221, 231, 246, 262, 275, 320, 336, 344, 347, 356, 369, 382, 395, 414 |
| abstract_inverted_index.ease | 404 |
| abstract_inverted_index.easy | 253 |
| abstract_inverted_index.find | 227, 376 |
| abstract_inverted_index.from | 44, 162, 238, 362 |
| abstract_inverted_index.give | 333 |
| abstract_inverted_index.half | 294 |
| abstract_inverted_index.know | 129, 134 |
| abstract_inverted_index.most | 114 |
| abstract_inverted_index.name | 265 |
| abstract_inverted_index.open | 59 |
| abstract_inverted_index.such | 88 |
| abstract_inverted_index.sure | 145 |
| abstract_inverted_index.than | 293 |
| abstract_inverted_index.them | 116 |
| abstract_inverted_index.they | 68 |
| abstract_inverted_index.time | 236, 400 |
| abstract_inverted_index.tool | 12 |
| abstract_inverted_index.type | 24, 273 |
| abstract_inverted_index.were | 75, 83, 143, 277 |
| abstract_inverted_index.will | 299 |
| abstract_inverted_index.with | 158 |
| abstract_inverted_index.(46%) | 142 |
| abstract_inverted_index.(52%) | 117 |
| abstract_inverted_index.(54%) | 304 |
| abstract_inverted_index.(60%) | 177 |
| abstract_inverted_index.(84%) | 385 |
| abstract_inverted_index.100%. | 106 |
| abstract_inverted_index.2017. | 48 |
| abstract_inverted_index.After | 64 |
| abstract_inverted_index.Among | 108 |
| abstract_inverted_index.Dhaka | 43 |
| abstract_inverted_index.Excel | 79 |
| abstract_inverted_index.Fifty | 202, 317 |
| abstract_inverted_index.Sixty | 340 |
| abstract_inverted_index.about | 7, 146 |
| abstract_inverted_index.among | 32, 184 |
| abstract_inverted_index.avoid | 381 |
| abstract_inverted_index.books | 358 |
| abstract_inverted_index.check | 368 |
| abstract_inverted_index.could | 118 |
| abstract_inverted_index.ended | 60 |
| abstract_inverted_index.every | 322 |
| abstract_inverted_index.most. | 302 |
| abstract_inverted_index.occur | 223 |
| abstract_inverted_index.opted | 278 |
| abstract_inverted_index.state | 257 |
| abstract_inverted_index.study | 28 |
| abstract_inverted_index.think | 243, 296 |
| abstract_inverted_index.total | 110 |
| abstract_inverted_index.used. | 63 |
| abstract_inverted_index.using | 8, 77, 85 |
| abstract_inverted_index.where | 220 |
| abstract_inverted_index.while | 233, 255, 338, 350, 372, 407 |
| abstract_inverted_index.(58%), | 295 |
| abstract_inverted_index.Bangla | 315 |
| abstract_inverted_index.Fourty | 330 |
| abstract_inverted_index.Online | 394 |
| abstract_inverted_index.always | 105, 332, 367 |
| abstract_inverted_index.asthma | 215 |
| abstract_inverted_index.closed | 57 |
| abstract_inverted_index.common | 147, 268 |
| abstract_inverted_index.depend | 342 |
| abstract_inverted_index.drugs, | 267 |
| abstract_inverted_index.making | 406 |
| abstract_inverted_index.online | 11, 245, 306, 391 |
| abstract_inverted_index.prefer | 305 |
| abstract_inverted_index.recall | 120, 361 |
| abstract_inverted_index.reduce | 398 |
| abstract_inverted_index.report | 156 |
| abstract_inverted_index.stated | 178 |
| abstract_inverted_index.study. | 73 |
| abstract_inverted_index.suffer | 160 |
| abstract_inverted_index.taking | 65 |
| abstract_inverted_index.though | 127 |
| abstract_inverted_index.types. | 131 |
| abstract_inverted_index.English | 313 |
| abstract_inverted_index.Generic | 264 |
| abstract_inverted_index.Maximum | 384 |
| abstract_inverted_index.adverse | 139, 269, 416 |
| abstract_inverted_index.carried | 30 |
| abstract_inverted_index.checker | 248, 284, 307 |
| abstract_inverted_index.correct | 122 |
| abstract_inverted_index.disease | 218 |
| abstract_inverted_index.elderly | 167 |
| abstract_inverted_index.events. | 417 |
| abstract_inverted_index.general | 34 |
| abstract_inverted_index.groups, | 187 |
| abstract_inverted_index.improve | 409 |
| abstract_inverted_index.instant | 14, 252 |
| abstract_inverted_index.maximum | 161, 181 |
| abstract_inverted_index.months, | 324 |
| abstract_inverted_index.opinion | 4 |
| abstract_inverted_index.option, | 254 |
| abstract_inverted_index.outdoor | 297 |
| abstract_inverted_index.percent | 155, 203, 226, 318, 331, 341, 375 |
| abstract_inverted_index.prevent | 261, 413 |
| abstract_inverted_index.produce | 138, 180 |
| abstract_inverted_index.related | 415 |
| abstract_inverted_index.results | 82 |
| abstract_inverted_index.various | 40, 239 |
| abstract_inverted_index.Fiftysix | 154 |
| abstract_inverted_index.Majority | 176, 303 |
| abstract_inverted_index.Material | 19 |
| abstract_inverted_index.November | 47 |
| abstract_inverted_index.Results: | 107 |
| abstract_inverted_index.analysed | 76 |
| abstract_inverted_index.blockers | 198 |
| abstract_inverted_index.checker. | 392 |
| abstract_inverted_index.consent, | 67 |
| abstract_inverted_index.decision | 405 |
| abstract_inverted_index.diabetes | 211 |
| abstract_inverted_index.effects, | 270 |
| abstract_inverted_index.effects. | 140 |
| abstract_inverted_index.followed | 165, 188, 204, 352 |
| abstract_inverted_index.included | 281 |
| abstract_inverted_index.informed | 66 |
| abstract_inverted_index.involved | 38 |
| abstract_inverted_index.leaflets | 345 |
| abstract_inverted_index.mellitus | 212 |
| abstract_inverted_index.methods: | 21 |
| abstract_inverted_index.moderate | 326 |
| abstract_inverted_index.multiple | 96 |
| abstract_inverted_index.options, | 97 |
| abstract_inverted_index.patients | 157, 298 |
| abstract_inverted_index.previous | 363 |
| abstract_inverted_index.priority | 334 |
| abstract_inverted_index.reported | 209 |
| abstract_inverted_index.revealed | 170, 194 |
| abstract_inverted_index.sources. | 240 |
| abstract_inverted_index.steroids | 179 |
| abstract_inverted_index.Fiftyfour | 374 |
| abstract_inverted_index.Microsoft | 78 |
| abstract_inverted_index.September | 45 |
| abstract_inverted_index.automated | 10 |
| abstract_inverted_index.available | 377 |
| abstract_inverted_index.bronchial | 214 |
| abstract_inverted_index.children, | 169 |
| abstract_inverted_index.commonly. | 224 |
| abstract_inverted_index.consuming | 237 |
| abstract_inverted_index.databases | 396 |
| abstract_inverted_index.detection | 15 |
| abstract_inverted_index.determine | 2 |
| abstract_inverted_index.encounter | 319 |
| abstract_inverted_index.expressed | 84 |
| abstract_inverted_index.frequency | 90 |
| abstract_inverted_index.hospitals | 41 |
| abstract_inverted_index.intensity | 327 |
| abstract_inverted_index.language. | 316 |
| abstract_inverted_index.occurring | 150 |
| abstract_inverted_index.potential | 411 |
| abstract_inverted_index.presented | 310 |
| abstract_inverted_index.questions | 61, 94 |
| abstract_inverted_index.reference | 357 |
| abstract_inverted_index.searching | 228 |
| abstract_inverted_index.Objective: | 0 |
| abstract_inverted_index.benefitted | 301 |
| abstract_inverted_index.clinicians | 6 |
| abstract_inverted_index.comprising | 54 |
| abstract_inverted_index.conditions | 219 |
| abstract_inverted_index.definition | 123 |
| abstract_inverted_index.inadequate | 379 |
| abstract_inverted_index.knowledge. | 364 |
| abstract_inverted_index.percentage | 102 |
| abstract_inverted_index.physicians | 35 |
| abstract_inverted_index.pre-tested | 52 |
| abstract_inverted_index.previously | 386 |
| abstract_inverted_index.statistics | 87 |
| abstract_inverted_index.therefore, | 98 |
| abstract_inverted_index.Conclusion: | 393 |
| abstract_inverted_index.Seventyfour | 225 |
| abstract_inverted_index.antibiotics | 195 |
| abstract_inverted_index.descriptive | 23, 86 |
| abstract_inverted_index.information | 229, 349, 371, 378, 402 |
| abstract_inverted_index.interaction | 232, 247, 276, 348, 370 |
| abstract_inverted_index.prescribing | 234, 410 |
| abstract_inverted_index.prospective | 26 |
| abstract_inverted_index.specialists | 37 |
| abstract_inverted_index.voluntarily | 69 |
| abstract_inverted_index.interaction, | 126 |
| abstract_inverted_index.interaction. | 383 |
| abstract_inverted_index.interactions | 136, 149, 183, 222, 321, 337 |
| abstract_inverted_index.participated | 70 |
| abstract_inverted_index.percentages. | 92 |
| abstract_inverted_index.polypharmacy | 159 |
| abstract_inverted_index.pre-designed | 50 |
| abstract_inverted_index.prescribing, | 351, 408 |
| abstract_inverted_index.prescribing. | 339, 373 |
| abstract_inverted_index.procurement, | 403 |
| abstract_inverted_index.responsible. | 201 |
| abstract_inverted_index.hypertension, | 210 |
| abstract_inverted_index.interactions, | 164 |
| abstract_inverted_index.interactions. | 18, 263 |
| abstract_inverted_index.participants, | 113 |
| abstract_inverted_index.questionnaire | 53 |
| abstract_inverted_index.respectively. | 175, 291 |
| abstract_inverted_index.specialities. | 153 |
| abstract_inverted_index.Simultaneously, | 241 |
| abstract_inverted_index.cross-sectional | 27 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.11425604 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |