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Research ArticleOriginal Research

The Relationship of Self-Report of Quality to Practice Size and Health Information Technology

Paul N. Gorman, Jean P. O'Malley and Lyle J. Fagnan
The Journal of the American Board of Family Medicine September 2012, 25 (5) 614-624; DOI: https://doi.org/10.3122/jabfm.2012.05.120063
Paul N. Gorman
From the Departments of Medical Informatics and Clinical Epidemiology (PNG) and Public Health and Preventive Medicine (JPO), Oregon Health and Science University, Portland; and the Oregon Practice-Based Research Network, Portland, Oregon (LJF).
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Jean P. O'Malley
From the Departments of Medical Informatics and Clinical Epidemiology (PNG) and Public Health and Preventive Medicine (JPO), Oregon Health and Science University, Portland; and the Oregon Practice-Based Research Network, Portland, Oregon (LJF).
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Lyle J. Fagnan
From the Departments of Medical Informatics and Clinical Epidemiology (PNG) and Public Health and Preventive Medicine (JPO), Oregon Health and Science University, Portland; and the Oregon Practice-Based Research Network, Portland, Oregon (LJF).
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    Figure 1.

    The Medical Office Survey on Patient Safety (MOSOPS) overall rating on quality and overall rating on patient safety.

Tables

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    Table 1. Distribution of Staff Roles in Medical Offices in This Sample and among Respondents
    Staff in Office, nProportion in Role, %Office-Wide Response Rate, %
    All roles20 (11–38), 3–28010084 (67–100), 16–100
    Office roles
        Clinicians5 (3–10), 1–9027 (20–33), 5–10023 (15–30), 0–72
        Management1 (1–2), 0–406 (4–11), 0–577 (3–11), 0–50
        Clinical staff7 (4–12), 0–9035 (26–47), 0–9036 (29–46), 0–100
        Office staff5 (3–11), 0–12529 (20–36), 0–9027 (20–36), 0–83
    • Data provided as median (interquartile range), range.

    • View popup
    Table 2. Size, Ownership, and Stage of Health Information Technology (HIT) in Medical Offices in Sample
    Total SampleSmall (3–15)Medium (16–40)Large (41–70)Very Large (> 70)P (vs Provider Owned)
    Owner*
        Provider/physician78 (25)38 (32)30 (26)7 (15)3 (14)
        Hospital or health system133 (38)70 (58)42 (36)16 (34)5 (23).9012
        University or academic61 (20)7 (6)35 (30)17 (36)2 (9).9480
        Government25 (8)2 (2)7 (6)5 (11)11 (50)<.0001
        Other9 (3)3 (3)3 (3)2 (4)1 (5).8959
    HIT implementation stage†P (vs Full Implementation)
        Low16 (5)9 (8)6 (5)1 (2)0 (0).9988
        Partial86 (28)38 (32)32 (27)11 (23)5 (23).9220
        High109 (36)41 (34)36 (31)14 (45)11 (50).1970
        Full95 (31)32 (27)43 (37)14 (30)6 (27)
    Totals306120 (39)117 (38)47 (15)22 (7)
    • Data provided as n (%).

    • ↵* Differences in size associated with category were assessed through ordinal regression of office size category (from Table 1) on ownership type or HIT stage. Reported P values are adjusted for multiple comparisons (Tukey).

    • ↵† Implementation stages were averaged for 5 capabilities as described in the text.

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    Table 3. Overall Rating on Quality by Office Size, Ownership, and Health Information Technology (HIT) Implementation
    Fixed Effect and CategoriesNo. in CategoryEffects Modeled SeparatelyCombined Model
    Role-Adjusted % Positive Ratings*Global PAdjusted % Positive Ratings†Global P
    Office size
        Small (3–15 staff)12075 (71–79)<.000175 (72–79)<.0001
        Medium (16–40 staff)11765 (61–69)‡§65 (61–69)‡§
        Large (41–70 staff)4760 (56–65)‡60 (56–65)‡
        Very large (>70 staff)2255 (48–61)‡§55 (48–61)‡§
    Ownership
        Provider owned7872 (67–78)§.0077Not included¶
        Government owned2560 (52–67)§
        University/academic6162 (56–69)
        Hospital/health system13369 (64–74)
        Other968 (64–74)
    HIT implementation level
        Low1676 (67–85)§.026874 (67–82)§.0102
        Partial8664 (59–70)§64 (60–68)§
        High10969 (64–74)70 (66–73)§
        Full9567 (62–72)68 (64–72)
    • Data provided as mean (95% confidence interval).

    • ↵* Estimated through mixed models that included respondent role and the specified office characteristic as fixed effects. Models treated the 4 scores due-role as repeated measures for the office and included health system group membership as a random effect. The main effect of role was significant at P < .0001 in all models.

    • ↵† Estimated from the combined model with role, office size, and HIT implementation level as fixed effects.

    • ↵‡ Post hoc pair-wise comparison with small (3–15 staff) significant at P < .0001 after Tukey adjustment.

    • ↵§ Post hoc pair-wise comparison significant at P < .05 after Tukey adjustment.

    • ↵¶ If ownership is added-the combined model P = .4610.

    • View popup
    Table 4. Office Size and Ratings of Components of Overall Quality and Safety
    Average % Positive Rating Overall Quality Item
    Patient CenteredEffective, Based on Scientific KnowledgeTimelyEfficient (Cost-Effective)EquitableOverall Safety Rating of Office Systems
    Office Size*
        Small (3–15 staff)78 (73–82)78 (74–83)64 (60–68)69 (64–74)89 (86–92)71 (68–75)
        Medium (16–40 staff)69 (64–73)72 (67–76)46 (42–50)54 (50–59)85 (82–89)63 (60–67)
        Large (41–70 staff)64 (58–70)68 (63–74)42 (36–48)46 (40–52)81 (77–85)59 (53–64)
        Very Large (≥71 staff)55 (46–64)63 (55–70)38 (29–47)42 (34–50)76 (71–82)54 (47–62)
    Global P (overall effect of size)<.0001<.0001<.0001<.0001<.0001<.0001
    Pair-wise comparisons†
        Small vs medium.0013.0087<.0001<.0001.1128.0065
        Small vs large.0002.0028<.0001<.0001.0005.0007
        Small vs very large<.0001.0006<.0001<.0001.0001.0006
        Medium vs large.4724.6551.6833.0625.0837.4838
        Medium vs very large.0166.1161.2864.0182.0097.1627
        Large vs very large.2652.5544.8267.7584.4874.7941
    • ↵* Data provided as means adjusted for role and stage of HIT implementation (low, partial, high, or full) (95% confidence intervals).

    • ↵† Data provided as Tukey-adjusted P values.

    • View popup
    Table 5. Overall Rating of Quality and Individual Health Information Technology (HIT) Capabilities
    HIT Implementation StageAverage % Positive Rating
    N (%)Patient CenteredEffective (Scientific)TimelyEfficient (Cost-Effective)EquitableOverall Safety of SystemsAveragef Quality Score
    Electronic appointment scheduling
        Not implemented6 (2)76 (60–92)78 (64–91)55 (36–73)62 (46–78)89 (79–99)57 (42–73)71 (59–83)
        Planned within 12 months3 (1)56 (34–78)67 (47–86)35 (9–60)42 (20–64)84 (71–97)45 (22–67)54 (37–71)
        Implementation in process5 (2)64 (47–80)72 (57–86)47 (28–66)49 (32–65)78 (68–88)62 (46–78)62 (50–75)
        Fully implemented289 (95)66 (62–71)70 (67–74)48 (44–51)54 (49–59)83 (80–86)62 (59–66)64 (60–68)
        P.4518.7115.6602.4404.4415.4218.3743
    Electronic medication ordering
        Not implemented32 (10)71 (64–79)73 (67–80)47 (39–55)56 (48–64)84 (79–89)63 (56–69)66 (60–71)
        Planned within 12 months51 (17)66 (60–73)69 (63–74)46 (40–53)50 (43–57)82 (78–86)58 (53–64)62 (57–67)
        Implementation in process62 (20)63 (57–69)69 (64–74)47 (41–52)53 (46–59)81 (77–85)60 (55–65)62 (58–66)
        Fully implemented160 (52)67 (62–71)70 (67–74)48 (44–52)55 (48–61)83 (80–86)64 (60–67)65 (61–68)
        P.2144.5742.9255.3900.4929.3017.3738
    Electronic procedure and image ordering
        Not implemented52 (17)70 (63–76)70 (65–76)47 (41–53)53 (47–58)84 (80–88)61 (56–67)64 (60–69)
        Planned within 12 months62 (21)61 (55–67)65 (60–70)41 (35–47)47 (42–52)*80 (76–84)57 (52–63)59 (54–63)*
        Implementation in process49 (16)67 (61–73)73 (67–78)50 (43–56)58 (53–64)*85 (81–89)66 (60–71)66 (62–71)*
        Fully implemented139 (46)67 (62–71)71 (67–74)49 (45–53)54 (50–57)83 (80–86)63 (59–66)64 (61–68)
        P.0688.1044.0953.0189.0630.1230.0274
    Electronic results access
        Not implemented12 (4)77 (65–88)71 (61–81)53 (40–66)55 (44–67)90 (82–97)*71 (59–82)71 (62–79)
        Planned within 12 months16 (5)62 (52–72)63 (55–72)37 (26–48)49 (39–59)76 (70–82)*55 (46–65)58 (50–65)
        Implementation in process69 (23)65 (59–71)69 (64–74)45 (39–51)52 (45–58)83 (79–87)60 (55–66)62 (58–67)
        Fully implemented207 (68)67 (62–71)71 (67–74)49 (46–53)54 (49–60)83 (80–86)63 (60–67)65 (61–68)
        P.1678.3139.0954.5264.0189.1429.0777
    Electronic health/medical records
        Not implemented35 (12)66 (59–73)70 (64–76)47 (39–54)53 (46–59)83 (78–87)59 (53–66)64 (58–69)
        Planned within 12 months48 (16)60 (53–66)65 (60–71)41 (34–48)46 (41–52)80 (76–84)57 (52–63)58 (53–63)
        Implementation in process35 (12)68 (61–75)70 (64–76)49 (42–57)55 (49–62)84 (80–89)61 (54–67)64 (59–70)
        Fully implemented186 (61)67 (63–71)71 (67–75)49 (45–52)54 (51–57)83 (80–86)64 (60–67)65 (61–68)
        P.1291.2695.1816.0798.4566.1723.0643
    • Data presented as means adjusted for role and office size (95% confidence intervals) unless otherwise indicated. Note that HIT elements were added individually for models that each included office size and role.

    • ↵* Post hoc pair-wise difference statistically significant at P < .05 after Tukey's adjustment for multiple comparisons.

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The Journal of the American Board of Family     Medicine: 25 (5)
The Journal of the American Board of Family Medicine
Vol. 25, Issue 5
September-October 2012
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The Relationship of Self-Report of Quality to Practice Size and Health Information Technology
Paul N. Gorman, Jean P. O'Malley, Lyle J. Fagnan
The Journal of the American Board of Family Medicine Sep 2012, 25 (5) 614-624; DOI: 10.3122/jabfm.2012.05.120063

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The Relationship of Self-Report of Quality to Practice Size and Health Information Technology
Paul N. Gorman, Jean P. O'Malley, Lyle J. Fagnan
The Journal of the American Board of Family Medicine Sep 2012, 25 (5) 614-624; DOI: 10.3122/jabfm.2012.05.120063
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