Differences in characteristics of Medicare patients treated by ophthalmologists and optometrists
To quantify differences in the age, gender, race, and clinical complexity of Medicare beneficiaries treated by ophthalmologists and optometrists in each of the United States. Cross-sectional study based on publicly accessible Medicare payment and utilization data from 2012 through 2017. For each oph...
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description | To quantify differences in the age, gender, race, and clinical complexity of Medicare beneficiaries treated by ophthalmologists and optometrists in each of the United States.
Cross-sectional study based on publicly accessible Medicare payment and utilization data from 2012 through 2017.
For each ophthalmic and optometric provider, demographic information of treated Medicare beneficiaries was obtained from the Medicare Provider Utilization and Payment Data from the Centers for Medicare and Medicaid Services (CMS) for the years 2012 through 2017. Clinical complexity was defined using CMS Hierarchical Condition Category (HCC) coding.
From 2012 through 2017, ophthalmologists in every state treated statistically significantly older beneficiaries, with the greatest difference (4.99 years in 2014) between provider groups seen in Rhode Island. In most states there was no gender difference among patients treated by the providers but in 46 states ophthalmologists saw a more racially diverse group of beneficiaries. HCC risk score analysis demonstrated that ophthalmologists in all 50 states saw more medically complex beneficiaries and the differences were statistically significant in 47 states throughout all six years.
Although there are regional variations in the characteristics of patients treated by ophthalmologists and optometrists, ophthalmologists throughout the United States manage older, more racially diverse, and more medically complex Medicare beneficiaries. |
doi_str_mv | 10.1371/journal.pone.0227783 |
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Cross-sectional study based on publicly accessible Medicare payment and utilization data from 2012 through 2017.
For each ophthalmic and optometric provider, demographic information of treated Medicare beneficiaries was obtained from the Medicare Provider Utilization and Payment Data from the Centers for Medicare and Medicaid Services (CMS) for the years 2012 through 2017. Clinical complexity was defined using CMS Hierarchical Condition Category (HCC) coding.
From 2012 through 2017, ophthalmologists in every state treated statistically significantly older beneficiaries, with the greatest difference (4.99 years in 2014) between provider groups seen in Rhode Island. In most states there was no gender difference among patients treated by the providers but in 46 states ophthalmologists saw a more racially diverse group of beneficiaries. HCC risk score analysis demonstrated that ophthalmologists in all 50 states saw more medically complex beneficiaries and the differences were statistically significant in 47 states throughout all six years.
Although there are regional variations in the characteristics of patients treated by ophthalmologists and optometrists, ophthalmologists throughout the United States manage older, more racially diverse, and more medically complex Medicare beneficiaries.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0227783</identifier><identifier>PMID: 32925977</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject><![CDATA[Administrative Claims, Healthcare - statistics & numerical data ; Age ; Age Factors ; Aged ; Baby boomers ; Beneficiaries ; Biology and Life Sciences ; Comparative analysis ; Complexity ; Continental Population Groups - statistics & numerical data ; Cross-Sectional Studies ; Demographic aspects ; Eye diseases ; Eye Diseases - diagnosis ; Eye Diseases - economics ; Eye Diseases - therapy ; Female ; Females ; Gender ; Government programs ; Health care access ; Health risks ; Humans ; Male ; Management ; Medical personnel ; Medicare ; Medicare - economics ; Medicare - statistics & numerical data ; Medicine and Health Sciences ; Ophthalmologists ; Ophthalmologists - economics ; Ophthalmologists - statistics & numerical data ; Ophthalmology - economics ; Ophthalmology - statistics & numerical data ; Optometrists ; Optometrists - economics ; Optometrists - statistics & numerical data ; Optometry - economics ; Optometry - statistics & numerical data ; Patients ; People and places ; Practice ; Practice Patterns, Physicians' - economics ; Race ; Risk analysis ; Scope of practice ; Sex Factors ; Social Sciences ; Statistical analysis ; United States]]></subject><ispartof>PloS one, 2020-09, Vol.15 (9), p.e0227783-e0227783</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Miller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Miller et al 2020 Miller et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-6d0d47c245454592d50e48e85b0c5502f77ff59c87376780102c4add2fe165a73</citedby><cites>FETCH-LOGICAL-c692t-6d0d47c245454592d50e48e85b0c5502f77ff59c87376780102c4add2fe165a73</cites><orcidid>0000-0003-4920-0970</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7489526/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7489526/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32925977$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Surbhi, Satya</contributor><creatorcontrib>Miller, Darby D</creatorcontrib><creatorcontrib>Stewart, Michael W</creatorcontrib><creatorcontrib>Gagne, Joshua J</creatorcontrib><creatorcontrib>Wagner, Alan L</creatorcontrib><creatorcontrib>Lee, Aaron Y</creatorcontrib><title>Differences in characteristics of Medicare patients treated by ophthalmologists and optometrists</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>To quantify differences in the age, gender, race, and clinical complexity of Medicare beneficiaries treated by ophthalmologists and optometrists in each of the United States.
Cross-sectional study based on publicly accessible Medicare payment and utilization data from 2012 through 2017.
For each ophthalmic and optometric provider, demographic information of treated Medicare beneficiaries was obtained from the Medicare Provider Utilization and Payment Data from the Centers for Medicare and Medicaid Services (CMS) for the years 2012 through 2017. Clinical complexity was defined using CMS Hierarchical Condition Category (HCC) coding.
From 2012 through 2017, ophthalmologists in every state treated statistically significantly older beneficiaries, with the greatest difference (4.99 years in 2014) between provider groups seen in Rhode Island. In most states there was no gender difference among patients treated by the providers but in 46 states ophthalmologists saw a more racially diverse group of beneficiaries. HCC risk score analysis demonstrated that ophthalmologists in all 50 states saw more medically complex beneficiaries and the differences were statistically significant in 47 states throughout all six years.
Although there are regional variations in the characteristics of patients treated by ophthalmologists and optometrists, ophthalmologists throughout the United States manage older, more racially diverse, and more medically complex Medicare beneficiaries.</description><subject>Administrative Claims, Healthcare - statistics & numerical data</subject><subject>Age</subject><subject>Age Factors</subject><subject>Aged</subject><subject>Baby boomers</subject><subject>Beneficiaries</subject><subject>Biology and Life Sciences</subject><subject>Comparative analysis</subject><subject>Complexity</subject><subject>Continental Population Groups - statistics & numerical data</subject><subject>Cross-Sectional Studies</subject><subject>Demographic aspects</subject><subject>Eye diseases</subject><subject>Eye Diseases - diagnosis</subject><subject>Eye Diseases - economics</subject><subject>Eye Diseases - therapy</subject><subject>Female</subject><subject>Females</subject><subject>Gender</subject><subject>Government programs</subject><subject>Health care access</subject><subject>Health risks</subject><subject>Humans</subject><subject>Male</subject><subject>Management</subject><subject>Medical personnel</subject><subject>Medicare</subject><subject>Medicare - economics</subject><subject>Medicare - statistics & numerical data</subject><subject>Medicine and Health Sciences</subject><subject>Ophthalmologists</subject><subject>Ophthalmologists - economics</subject><subject>Ophthalmologists - statistics & numerical data</subject><subject>Ophthalmology - economics</subject><subject>Ophthalmology - statistics & numerical data</subject><subject>Optometrists</subject><subject>Optometrists - economics</subject><subject>Optometrists - statistics & numerical data</subject><subject>Optometry - economics</subject><subject>Optometry - statistics & numerical data</subject><subject>Patients</subject><subject>People and places</subject><subject>Practice</subject><subject>Practice Patterns, Physicians' - economics</subject><subject>Race</subject><subject>Risk analysis</subject><subject>Scope of practice</subject><subject>Sex Factors</subject><subject>Social Sciences</subject><subject>Statistical analysis</subject><subject>United States</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk9tu1DAQhiMEoqXwBggiISG42MXxIXZukKpyWqmoEqdb43XGG6-SeGs7Vfv2OGxabVAvkC9iTb7_H8_Yk2XPC7QsCC_ebd3ge9Uud66HJcKYc0EeZMdFRfCixIg8PNgfZU9C2CLEiCjLx9kRwRVmFefH2e8P1hjw0GsIue1z3SivdARvQ7Q65M7kX6G2WnnIdypa6GPIowcVoc7XN7nbNbFRbedat0mSkKu-TsHoOoijR3iaPTKqDfBs-p5kPz99_HH2ZXF-8Xl1dnq-0GWF46KsUU25xpSNq8I1Q0AFCLZGmjGEDefGsEoLTnjJBSoQ1lTVNTZQlExxcpK93PvuWhfk1JwgMaWYiSo1LBGrPVE7tZU7bzvlb6RTVv4NOL-RyqeiW5CoUhghIxCu0qkgJTKICqFLTs2aQJm83k_ZhnUHtU5t8aqdmc7_9LaRG3clORUVw6PBm8nAu8sBQpSdDRraVvXghv25BaWUVAl99Q96f3UTtVGpANsbl_Lq0VSeloQlt5KhRC3vodKqobM6vSRjU3wmeDsTJCbCddyoIQS5-v7t_9mLX3P29QHbgGpjE1w7ROv6MAfpHtTeheDB3DW5QHIchNtuyHEQ5DQISfbi8ILuRLcvn_wB-b4DPg</recordid><startdate>20200914</startdate><enddate>20200914</enddate><creator>Miller, Darby D</creator><creator>Stewart, Michael W</creator><creator>Gagne, Joshua J</creator><creator>Wagner, Alan L</creator><creator>Lee, Aaron Y</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-4920-0970</orcidid></search><sort><creationdate>20200914</creationdate><title>Differences in characteristics of Medicare patients treated by ophthalmologists and optometrists</title><author>Miller, Darby D ; Stewart, Michael W ; Gagne, Joshua J ; Wagner, Alan L ; Lee, Aaron Y</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-6d0d47c245454592d50e48e85b0c5502f77ff59c87376780102c4add2fe165a73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Administrative Claims, Healthcare - 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economics</topic><topic>Ophthalmologists - statistics & numerical data</topic><topic>Ophthalmology - economics</topic><topic>Ophthalmology - statistics & numerical data</topic><topic>Optometrists</topic><topic>Optometrists - economics</topic><topic>Optometrists - statistics & numerical data</topic><topic>Optometry - economics</topic><topic>Optometry - statistics & numerical data</topic><topic>Patients</topic><topic>People and places</topic><topic>Practice</topic><topic>Practice Patterns, Physicians' - economics</topic><topic>Race</topic><topic>Risk analysis</topic><topic>Scope of practice</topic><topic>Sex Factors</topic><topic>Social Sciences</topic><topic>Statistical analysis</topic><topic>United States</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Miller, Darby D</creatorcontrib><creatorcontrib>Stewart, Michael W</creatorcontrib><creatorcontrib>Gagne, Joshua J</creatorcontrib><creatorcontrib>Wagner, Alan L</creatorcontrib><creatorcontrib>Lee, Aaron Y</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Miller, Darby D</au><au>Stewart, Michael W</au><au>Gagne, Joshua J</au><au>Wagner, Alan L</au><au>Lee, Aaron Y</au><au>Surbhi, Satya</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Differences in characteristics of Medicare patients treated by ophthalmologists and optometrists</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-09-14</date><risdate>2020</risdate><volume>15</volume><issue>9</issue><spage>e0227783</spage><epage>e0227783</epage><pages>e0227783-e0227783</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>To quantify differences in the age, gender, race, and clinical complexity of Medicare beneficiaries treated by ophthalmologists and optometrists in each of the United States.
Cross-sectional study based on publicly accessible Medicare payment and utilization data from 2012 through 2017.
For each ophthalmic and optometric provider, demographic information of treated Medicare beneficiaries was obtained from the Medicare Provider Utilization and Payment Data from the Centers for Medicare and Medicaid Services (CMS) for the years 2012 through 2017. Clinical complexity was defined using CMS Hierarchical Condition Category (HCC) coding.
From 2012 through 2017, ophthalmologists in every state treated statistically significantly older beneficiaries, with the greatest difference (4.99 years in 2014) between provider groups seen in Rhode Island. In most states there was no gender difference among patients treated by the providers but in 46 states ophthalmologists saw a more racially diverse group of beneficiaries. HCC risk score analysis demonstrated that ophthalmologists in all 50 states saw more medically complex beneficiaries and the differences were statistically significant in 47 states throughout all six years.
Although there are regional variations in the characteristics of patients treated by ophthalmologists and optometrists, ophthalmologists throughout the United States manage older, more racially diverse, and more medically complex Medicare beneficiaries.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32925977</pmid><doi>10.1371/journal.pone.0227783</doi><tpages>e0227783</tpages><orcidid>https://orcid.org/0000-0003-4920-0970</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Administrative Claims, Healthcare - statistics & numerical data Age Age Factors Aged Baby boomers Beneficiaries Biology and Life Sciences Comparative analysis Complexity Continental Population Groups - statistics & numerical data Cross-Sectional Studies Demographic aspects Eye diseases Eye Diseases - diagnosis Eye Diseases - economics Eye Diseases - therapy Female Females Gender Government programs Health care access Health risks Humans Male Management Medical personnel Medicare Medicare - economics Medicare - statistics & numerical data Medicine and Health Sciences Ophthalmologists Ophthalmologists - economics Ophthalmologists - statistics & numerical data Ophthalmology - economics Ophthalmology - statistics & numerical data Optometrists Optometrists - economics Optometrists - statistics & numerical data Optometry - economics Optometry - statistics & numerical data Patients People and places Practice Practice Patterns, Physicians' - economics Race Risk analysis Scope of practice Sex Factors Social Sciences Statistical analysis United States |
title | Differences in characteristics of Medicare patients treated by ophthalmologists and optometrists |
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