Abstract
Background:
The introduction of electronic health records (EHRs) has helped physicians access relevant medical information on their patients. However, the design of EHRs can make it hard for clinicians to easily find, review and document all of the relevant data.; leading to documentation that is not fully reflective of the complete history. We hypothesized that the incidence of undocumented key co-morbid diseases (atrial fibrillation, congestive heart failure, chronic obstructive pulmonary disease, diabetes, and chronic kidney disease) in the anesthesia preoperative evaluation was associated with increased postoperative length of stay (LOS) and mortality.
Methods:
Charts of patients older than 18 who received anesthesia in an inpatient facility were reviewed in this retrospective study. For each disease a precise algorithm was developed to look for key structured data (medications, lab results, structured medical history, etc.) in the EHR. Additionally, the check-boxes from the anesthesia preoperative evaluation were queried to determine the presence or absence of the documentation of the disease. Differences in mortality were modeled with logistic regression and LOS was analyzed using linear regression.
Results:
91,011 cases met inclusion criteria (Age 18–89; 52% female, 48% male; 70% admitted from home). Agreement between the algorithms and the preoperative note was greater than 84% for all comorbidities other than chronic pain (63.5%). The algorithm detected disease not documented by the anesthesia team in 34.5% of cases for chronic pain (vs. 1.9% of cases where chronic pain was documented by not detected by the algorithm), 4.0% of cases for diabetes (vs. 2.1%), 4.3% of cases for CHF (vs.0.7%), 4.3% of cases for COPD (vs.1.1%), 7.7% of cases for afib (vs. 0.3%), and 10.8% of cases for CKD (vs. 1.7%). To asses the association of missed documentation with outcomes we compared patients where the disease was detected by the algorithm but not documented (A+/P−) with patients where the disease was documented (A+/P+). For all diseases except chronic pain, the missed documentation was associated with a longer LOS. For mortality the discrepancy was associated with increased mortality for afib while the differences in significant for the other diseases. For each missed disease the odds of mortality increased 1.52 (95% CI 1.42–1.63) and the LOS increased by approximately 11%, geometric mean ratio of 1.11 (95% CI 1.10–1.12).
Conclusions:
Anesthesia preoperative evaluations not infrequently fail to document disease for which there is evidence of disease in the EHR data. This missed documentation is associated with an increased LOS and mortality in perioperative patients.
The introduction, and subsequent proliferation, of electronic health records (EHRs) precipitated by the affordable care act and later the Health Information Technology for Economic and Clinical Health (HITECH) act has created revolutionary changes to the daily work of physicians and other care providers1. The initial promise of the EHR was that it would lead to a consolidation of the patient’s overwhelming medical information and thereby help decrease redundant testing and incomplete information. While this consolidation has occurred, EHRs have created other issues such as distractions from interactions with patients, information overload and physician burnout2, 3. Additionally, studies have shown the EHRs have resulted in the proliferation of redundant, and at times inaccurate information – such as copy and paste errors4- placing further challenges on providers.
Previous work has demonstrated that despite clinicians spending a significant amount of their time going through patient’s medical records, they often miss highly important comorbid conditions5, 6. For example, one study found that up to 54% of patients with sepsis have never had the condition appropriately documented7. These studies highlight the fact that while information on patient comorbidities may be present in the EHR, it is not necessarily fully apparent or reachable to the clinicians caring for these patients - the overwhelming amount of non-categorized information in the EHR makes it difficult for clinicians to quickly access relevant information. Even medications, which are often structured lists, may be incomplete or have medications prescribed for multiple overlapping conditions. For anesthesiologists, who often have a limited period of time to learn about increasingly sick patients, this is a very challenging problem. Clinically, it means that anesthesiologists may not be fully aware of conditions such as chronic kidney disease (CKD), diabetes, and other key comorbid diseases which have shown to be correlated with post-operative outcomes8,9–12,13–18,19–23. Ultimately, clinicians cannot treat diseases they don’t know about, and it is possible that the failure to pick up on these comorbidities may be leading to anesthetics plans that differ in areas such as glycemic control, fluid balance and mean arterial pressure (MAP) goals.
Previously our group developed techniques to create rules-based algorithms that automatically phenotype patients with their comorbid diseases based upon the structured data information in the patient EHR6. We hypothesized that 1) these rules-based algorithms may detect key comorbid diseases - specifically atrial fibrillation (afib), diabetes, congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), CKD and chronic pain - that anesthesia team did not document in the preoperative note and 2) this failure to document the diseases may be associated with worse perioperative outcomes – specifically postoperative mortality and length of stay (LOS). In this manuscript we first examine the incidence of missed documentation in the anesthesia preoperative evaluation by comparing the structured data from preoperative anesthesia evaluation to the results of our rules-based algorithms and then examine the association between these missed documentation and mortality (primary outcome) and length of stay (LOS) (secondary outcome) by comparing patients where the algorithm and preoperative evaluation both note the disease (A+/P+) to those where the algorithm noted the disease but the preoperative evaluation did not (A+/P−).
Methods
Data Extraction
All data for this study were extracted from the Perioperative Data Warehouse (PDW), a custom-built robust data warehouse containing all patients who have undergone surgery at the University of California Los Angeles (UCLA) Health since the implementation of our EHR (EPIC Systems, Madison WI) in March 2013. We have previously described the creation of the PDW, which has a two stage design24, 25. Briefly, in the first stage data are extracted from EPIC’s Clarity database into 29 tables organized around three distinct concepts: patients, surgical procedures and health system encounters. These data are then used to populate a series of 4000 distinct measures and metrics such as procedure duration, readmissions, admission ICD codes, and postoperative outcomes. All data used for this study were obtained from this data warehouse and IRB approval (IRB#15–000518) was obtained from the UCLA Office of the Human Research Protection Program, including exemption from written informed consent, has been obtained for this retrospective review.
Determination of Comorbidities using algorithms
For the purposes of this study, six comorbidities were selected – afib, diabetes, CHF, COPD, CKD, and chronic pain. For each disease a rules-based algorithm designed to be highly precise (i.e. few false positives) was created in line with what we have previously published6. Briefly, in each case, an initial set of criteria for the algorithm was created. The algorithm was then run on cases in our data-warehouse and a randomly selected group of cases was chosen for review. If the algorithm was found to lack precision then the criteria were revised until the review no longer generated false positives in our random selection. The final criteria for each disease are listed in Supplementary Table 1.
Determination of documentation of patient comorbidities in the anesthesia preoperative evaluation
In our EHR (EPIC, EPIC Medical Systems, Verona, WI) the preoperative anesthesia evaluation (preop note) is created via a series of check-boxes. Patients typically undergo one of two workflows. For approximately 60% of patients, the patients are screened by our preoperative nurses who gather the relevant documentation and prepopulate the anesthesia preoperative evaluation by clicking the various boxes and adding in the relevant text. This note is then finalized – reviewed, physical exam added, plan added, etc. – by the anesthesia team on the day of surgery. For the other 40% of patients, the note is compiled entirely by the anesthesia team on the night prior to and day of surgery. No distinction between these workflows was made for the purposes of this study.
For each of the comorbidities of interest the PDW was queried to determine the checking of the appropriate box. In the event that multiple check-boxes were associated with one of the comorbidities of interest the checking of any of the boxes was considered to be sufficient to denote anesthesiologist documentation.
Inclusion and exclusion criteria
Cases with anesthesia performed between April 1, 2013 (date of EHR go-live) and July 1, 2019 (date of extract creation) were included. Cases were excluded if patient age was less than 18 years or if the case was performed at outpatient surgery facilities (as mortality and postoperative length of stay (LOS) information is not relevant for these encounters).
In the event that a patient had more than one anesthetic in the database then only the first anesthetic was selected.
Included covariates
In order to independently account for associations with the outcomes of interest, a series of relevant co-variates were extracted for each encounter. These included: age (ages greater than 89 were set to 89 per institutional deidentification guidelines), gender, American Society of Anesthesiologists (ASA) physical status score (ASA score), emergent status of the case (booking case type), type of anesthesia (general, regional and sedation), surgical procedure classification (healthcare cost and utilization (HCUP) code), and Preoperative Score to Predict Postoperative Mortality (POSPM) score26.
Definition of outcome measures
Mortality was defined as the existence of a date of death documented between a hospital admission’s admission date and discharge date. The date of death was extrapolated from documentation of a discharge disposition of “expired” and the existence of a note containing specific phrases indicative of a mortality event (“death summary”, “time of death”, “date of death”, “death date”, “death time”, “death note”). Postoperative length of stay (LOS) was defined as the day of hospitalization after surgery when the patient was discharged, with a discharge on the day of surgery considered as day 1 (i.e. if the patient was discharged the day after surgery it would be day 2, and so on).
Statistical Analysis
Patient characteristics and study outcomes were summarized overall and between the comorbidity classification groups using frequency (%) or mean (SD). For mortality, all logistic regression models were constructed with terms for POSPOM Score, ASA, Booking case type, anesthesia type, HCUP code, and the 4 level diagnosis variable we created (i.e. Algorithm Positive/Preoperative Evaluation Positive (A+/P+), Algorithm Positive/Preoperative Evaluation Negative (A+/P−) etc.). Similarly, linear models were constructed with the same terms for our other outcome of interest, post-op LOS. Since the distributional assumptions of these models were questionable, we are reporting the models after log transformation of the outcome (LOS) and report the results as geometric mean ratios (the ratios of the antilogs of the means of the log transformed data). Upon further residual analysis, the normality assumption seemed adequate but we did observe possible homoscedasticity violations, so we decided to use robust standard errors (SAS option empirical = mbn in ‘proc glimmix). For the models including diabetes, we also included A1c and home insulin usage and for chronic pain models we included pre-op opioid usage and pain clinic visit (yes/no). Pairwise comparisons (or odds ratios/geometric mean ratios) between the comorbidity classification groups (A+/P+, A+/P−, etc.) were extracted from the models and presented with 95% confidence intervals. Forest plots were constructed to aid visual interpretation of the results.
We also ran models for each outcome using the total number of missed diagnoses to get an aggregate overall result (see tables/figs). Analyses were conducted using SAS V9.4 (SAS Institute, Cary, NC). Since we ran 12 models (6 comorbidities * 2 outcomes), our overall alpha level used for determining significance was a Bonferroni-adjusted 0.004 (0.05/12 = 0.004).
An a priori sample size calculation was not performed as we extracted all records that met inclusion criteria.
Results
Overall the dataset included 91,011 cases. Age ranged from 18 to 89. There were 52% females and 48% males. 70% of the cases involved patients admitted from home and 2.8% of cases were transplants. The cases involved a variety of surgical services with 10.2% gastrointestinal procedures, 14.8% general surgical procedures, and 12.1% orthopedic procedures. 7.5% of cases were ASA physical status 1, 39.9% of cases ASA 2, 44.4% ASA 3, 7.7% ASA 4, 0.5% ASA 5. Complete details of patient demographics can be found in Table 1.
| CHF | Diabetes | Afib | COPD | CKD | Chronic Pain | ||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | A+/P+ | A−/P+ | A+/P− | A−/P− | A+/P+ | A−/P+ | A+/P− | A−/P− | A+/P+ | A−/P+ | A+/P− | A−/P− | A+/P+ | A−/P+ | A+/P− | A−/P− | A+/P+ | A−/P+ | A+/P− | A−/P− | A+/P+ | A−/P+ | A+/P− | A−/P− | TOTALS |
| Patients ASA Score |
3296 (3.6%) | 599 (0.7%) | 3929 (4.3%) | 83187 (91.4%) | 11894 (13.1%) | 1890 (2.1%) | 3625 (4.0%) | 73602 (80.9%) | 2340 (2.6%) | 231 (0.3%) | 7028 (7.7%) | 81412 (89.5%) | 2800 (3.1%) | 1005 (1.1%) | 3934 (4.3%) | 83272 (91.5%) | 2228 (2.4%) | 1565 (1.7%) | 9847 (10.8%) | 77371 (85.0%) | 2170 (2.4%) | 1715 (1.9%) | 31439 (34.5%) | 55687 (61.2%) | 91011 |
| 1 | 0 (0%) | 0 (0%) | 5 (0.1%) | 6866 (8.3%) | 14 (0.1%) | 12 (0.6%) | 20 (0.6%) | 6825 (9.3%) | 1 (0%) | 0 (0%) | 10 (0.1%) | 6860 (8.4%) | 1 (0%) | 2 (0.2%) | 27 (0.7%) | 6841 (8.2%) | 0 (0%) | 7 (0.4%) | 44 (0.4%) | 6820 (8.8%) | 20 (0.9%) | 56 (3.3%) | 1742 (5.5%) | 5053 (9.1%) | 6871 (7.5%) |
| 2 | 72 (2.2%) | 31 (5.2%) | 177 (4.5%) | 35990 (43.3%) | 2116 (17.8%) | 563 (29.8%) | 609 (16.8%) | 32982 (44.8%) | 281 (12%) | 36 (15.6%) | 790 (11.2%) | 35163 (43.2%) | 268 (9.6%) | 203 (20.2%) | 787 (20%) | 35012 (42%) | 230 (10.3%) | 308 (19.7%) | 1268 (12.9%) | 34464 (44.5%) | 688 (31.7%) | 733 (42.7%) | 10549 (33.6%) | 24300 (43.6%) | 36270 (39.9%) |
| 3 | 1845 (56%) | 452 (75.5%) | 2291 (58.3%) | 35805 (43%) | 8001 (67.3%) | 1183 (62.6%) | 2179 (60.1%) | 29030 (39.4%) | 1614 (69%) | 176 (76.2%) | 4196 (59.7%) | 34407 (42.3%) | 1998 (71.4%) | 713 (70.9%) | 2146 (54.6%) | 35536 (42.7%) | 1476 (66.2%) | 1043 (66.6%) | 6467 (65.7%) | 31407 (40.6%) | 1354 (62.4%) | 877 (51.1%) | 15450 (49.1%) | 22712 (40.8%) | 40393 (44.4%) |
| 4 | 1340 (40.7%) | 112 (18.7%) | 1377 (35%) | 4148 (5%) | 1726 (14.5%) | 122 (6.5%) | 775 (21.4%) | 4354 (5.9%) | 436 (18.6%) | 18 (7.8%) | 1952 (27.8%) | 4571 (5.6%) | 528 (18.9%) | 85 (8.5%) | 947 (24.1%) | 5417 (6.5%) | 508 (22.8%) | 207 (13.2%) | 1891 (19.2%) | 4371 (5.6%) | 108 (5%) | 49 (2.9%) | 3563 (11.3%) | 3257 (5.8%) | 6977 (7.7%) |
| 5 | 39 (1.2%) | 3 (0.5%) | 72 (1.8%) | 349 (0.4%) | 37 (0.3%) | 10 (0.5%) | 40 (1.1%) | 376 (0.5%) | 8 (0.3%) | 1 (0.4%) | 77 (1.1%) | 377 (0.5%) | 5 (0.2%) | 2 (0.2%) | 27 (0.7%) | 429 (0.5%) | 13 (0.6%) | 0 (0%) | 156 (1.6%) | 294 (0.4%) | 0 (0%) | 0 (0%) | 131 (0.4%) | 332 (0.6%) | 463 (0.5%) |
| 6 Booking Class |
0 (0%) | 1 (0.2%) | 7 (0.2%) | 29 (0%) | 0 (0%) | 0 (0%) | 2 (0.1%) | 35 (0%) | 0 (0%) | 0 (0%) | 3 (0%) | 34 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 37 (0%) | 1 (0%) | 0 (0%) | 21 (0.2%) | 15 (0%) | 0 (0%) | 0 (0%) | 4 (0%) | 33 (0.1%) | 37 (0%) |
| Blank/missing | 281 (8.5%) | 68 (11.4%) | 365 (9.3%) | 6808 (8.2%) | 811 (6.8%) | 208 (11%) | 318 (8.8%) | 6185 (8.4%) | 228 (9.7%) | 31 (13.4%) | 635 (9%) | 6628 (8.1%) | 200 (7.1%) | 66 (6.6%) | 327 (8.3%) | 6929 (8.3%) | 167 (7.5%) | 90 (5.8%) | 792 (8%) | 6473 (8.4%) | 150 (6.9%) | 105 (6.1%) | 2498 (7.9%) | 4769 (8.6%) | 7522 (8.3%) |
| Critically emergent | 19 (0.6%) | 2 (0.3%) | 38 (1%) | 959 (1.2%) | 54 (0.5%) | 19 (1%) | 47 (1.3%) | 898 (1.2%) | 10 (0.4%) | 1 (0.4%) | 66 (0.9%) | 941 (1.2%) | 8 (0.3%) | 6 (0.6%) | 22 (0.6%) | 982 (1.2%) | 8 (0.4%) | 6 (0.4%) | 155 (1.6%) | 849 (1.1%) | 2 (0.1%) | 0 (0%) | 217 (0.7%) | 799 (1.4%) | 1018 (1.1%) |
| Elective | 1707 (51.8%) | 433 (72.3%) | 1870 (47.6%) | 59492 (71.5%) | 7804 (65.6%) | 1381 (73.1%) | 1864 (51.4%) | 52453 (71.3%) | 1554 (66.4%) | 176 (76.2%) | 4080 (58.1%) | 57692 (70.9%) | 1885 (67.3%) | 820 (81.6%) | 2275 (57.8%) | 58522 (70.3%) | 1252 (56.2%) | 1209 (77.3%) | 4429 (45%) | 56612 (73.2%) | 1737 (80%) | 1542 (89.9%) | 19033 (60.5%) | 41190 (74%) | 63502 (69.8%) |
| Emergent | 146 (4.4%) | 25 (4.2%) | 267 (6.8%) | 4275 (5.1%) | 412 (3.5%) | 119 (6.3%) | 194 (5.4%) | 3988 (5.4%) | 62 (2.6%) | 15 (6.5%) | 368 (5.2%) | 4268 (5.2%) | 109 (3.9%) | 35 (3.5%) | 173 (4.4%) | 4396 (5.3%) | 127 (5.7%) | 33 (2.1%) | 616 (6.3%) | 3937 (5.1%) | 48 (2.2%) | 15 (0.9%) | 1589 (5.1%) | 3061 (5.5%) | 4713 (5.2%) |
| Inpatient | 618 (18.8%) | 18 (3%) | 691 (17.6%) | 3543 (4.3%) | 1053 (8.9%) | 21 (1.1%) | 470 (13%) | 3326 (4.5%) | 243 (10.4%) | 3 (1.3%) | 846 (12%) | 3778 (4.6%) | 266 (9.5%) | 26 (2.6%) | 480 (12.2%) | 4098 (4.9%) | 303 (13.6%) | 107 (6.8%) | 898 (9.1%) | 3562 (4.6%) | 119 (5.5%) | 19 (1.1%) | 3345 (10.6%) | 1387 (2.5%) | 4870 (5.4%) |
| Transplant case | 100 (3%) | 32 (5.3%) | 139 (3.5%) | 2286 (2.7%) | 672 (5.6%) | 86 (4.6%) | 291 (8%) | 1508 (2%) | 28 (1.2%) | 1 (0.4%) | 255 (3.6%) | 2273 (2.8%) | 66 (2.4%) | 16 (1.6%) | 228 (5.8%) | 2247 (2.7%) | 107 (4.8%) | 24 (1.5%) | 1863 (18.9%) | 563 (0.7%) | 18 (0.8%) | 19 (1.1%) | 1025 (3.3%) | 1495 (2.7%) | 2557 (2.8%) |
| Urgent Gender |
425 (12.9%) | 21 (3.5%) | 559 (14.2%) | 5824 (7%) | 1088 (9.1%) | 56 (3%) | 441 (12.2%) | 5244 (7.1%) | 215 (9.2%) | 4 (1.7%) | 778 (11.1%) | 5832 (7.2%) | 266 (9.5%) | 36 (3.6%) | 429 (10.9%) | 6098 (7.3%) | 264 (11.8%) | 96 (6.1%) | 1094 (11.1%) | 5375 (6.9%) | 96 (4.4%) | 15 (0.9%) | 3732 (11.9%) | 2986 (5.4%) | 6829 (7.5%) |
| Female | 1323 (40.1%) | 222 (37.1%) | 1685 (42.9%) | 44124 (53%) | 5286 (44.4%) | 900 (47.6%) | 1634 (45.1%) | 39534 (53.7%) | 831 (35.5%) | 94 (40.7%) | 2647 (37.7%) | 43782 (53.8%) | 1208 (43.1%) | 434 (43.2%) | 2028 (51.6%) | 43684 (52.5%) | 865 (38.8%) | 561 (35.8%) | 4652 (47.2%) | 41276 (53.3%) | 1259 (58%) | 976 (56.9%) | 16804 (53.4%) | 28315 (50.8%) | 47354 (52%) |
| Male Anesthesia Type |
1973 (59.9%) | 377 (62.9%) | 2244 (57.1%) | 39063 (47%) | 6608 (55.6%) | 990 (52.4%) | 1991 (54.9%) | 34068 (46.3%) | 1509 (64.5%) | 137 (59.3%) | 4381 (62.3%) | 37630 (46.2%) | 1592 (56.9%) | 571 (56.8%) | 1906 (48.4%) | 39588 (47.5%) | 1363 (61.2%) | 1004 (64.2%) | 5195 (52.8%) | 36095 (46.7%) | 911 (42%) | 739 (43.1%) | 14635 (46.6%) | 27372 (49.2%) | 43657 (48%) |
| Mac/Regional/Other | 1303 (39.5%) | 199 (33.2%) | 1290 (32.8%) | 17996 (21.6%) | 3028 (25.5%) | 506 (26.8%) | 1000 (27.6%) | 16254 (22.1%) | 841 (35.9%) | 69 (29.9%) | 2119 (30.2%) | 17759 (21.8%) | 777 (27.8%) | 262 (26.1%) | 1110 (28.2%) | 18639 (22.4%) | 644 (28.9%) | 371 (23.7%) | 2514 (25.5%) | 17259 (22.3%) | 413 (19%) | 239 (13.9%) | 8184 (26%) | 11952 (21.5%) | 20788 (22.8%) |
| General Surgical Service |
1993 (60.5%) | 400 (66.8%) | 2639 (67.2%) | 65191 (78.4%) | 8866 (74.5%) | 1384 (73.2%) | 2625 (72.4%) | 57348 (77.9%) | 1499 (64.1%) | 162 (70.1%) | 4909 (69.8%) | 63653 (78.2%) | 2023 (72.3%) | 743 (73.9%) | 2824 (71.8%) | 64633 (77.6%) | 1584 (71.1%) | 1194 (76.3%) | 7333 (74.5%) | 60112 (77.7%) | 1757 (81%) | 1476 (86.1%) | 23255 (74%) | 43735 (78.5%) | 70223 (77.2%) |
| Cardiac Surgery | 739 (22.4%) | 50 (8.3%) | 992 (25.2%) | 2248 (2.7%) | 963 (8.1%) | 27 (1.4%) | 442 (12.2%) | 2597 (3.5%) | 228 (9.7%) | 3 (1.3%) | 1476 (21%) | 2322 (2.9%) | 236 (8.4%) | 30 (3%) | 515 (13.1%) | 3248 (3.9%) | 219 (9.8%) | 98 (6.3%) | 773 (7.9%) | 2939 (3.8%) | 31 (1.4%) | 23 (1.3%) | 2067 (6.6%) | 1908 (3.4%) | 4029 (4.4%) |
| Cardiology | 905 (27.5%) | 118 (19.7%) | 847 (21.6%) | 3498 (4.2%) | 797 (6.7%) | 68 (3.6%) | 282 (7.8%) | 4221 (5.7%) | 747 (31.9%) | 3 (1.3%) | 1908 (27.1%) | 2710 (3.3%) | 232 (8.3%) | 38 (3.8%) | 329 (8.4%) | 4769 (5.7%) | 226 (10.1%) | 99 (6.3%) | 897 (9.1%) | 4146 (5.4%) | 45 (2.1%) | 26 (1.5%) | 1518 (4.8%) | 3779 (6.8%) | 5368 (5.9%) |
| Gastroenterology | 324 (9.8%) | 59 (9.8%) | 382 (9.7%) | 8542 (10.3%) | 1323 (11.1%) | 357 (18.9%) | 481 (13.3%) | 7146 (9.7%) | 203 (8.7%) | 28 (12.1%) | 506 (7.2%) | 8570 (10.5%) | 306 (10.9%) | 151 (15%) | 575 (14.6%) | 8275 (9.9%) | 227 (10.2%) | 159 (10.2%) | 909 (9.2%) | 8012 (10.4%) | 161 (7.4%) | 114 (6.6%) | 3361 (10.7%) | 5671 (10.2%) | 9307 (10.2%) |
| General Surgery | 185 (5.6%) | 37 (6.2%) | 283 (7.2%) | 12943 (15.6%) | 1623 (13.6%) | 239 (12.6%) | 418 (11.5%) | 11168 (15.2%) | 184 (7.9%) | 28 (12.1%) | 512 (7.3%) | 12724 (15.6%) | 291 (10.4%) | 116 (11.5%) | 422 (10.7%) | 12619 (15.2%) | 269 (12.1%) | 221 (14.1%) | 890 (9%) | 12068 (15.6%) | 249 (11.5%) | 230 (13.4%) | 4154 (13.2%) | 8815 (15.8%) | 13448 (14.8%) |
| Neurosurgery | 3 (0.1%) | 0 (0%) | 5 (0.1%) | 41 (0%) | 10 (0.1%) | 0 (0%) | 1 (0%) | 38 (0.1%) | 2 (0.1%) | 0 (0%) | 2 (0%) | 45 (0.1%) | 1 (0%) | 0 (0%) | 2 (0.1%) | 46 (0.1%) | 1 (0%) | 1 (0.1%) | 6 (0.1%) | 41 (0.1%) | 1 (0%) | 0 (0%) | 24 (0.1%) | 24 (0%) | 49 (0.1%) |
| Obstetrics and Gynecology | 44 (1.3%) | 15 (2.5%) | 34 (0.9%) | 6590 (7.9%) | 375 (3.2%) | 77 (4.1%) | 88 (2.4%) | 6143 (8.3%) | 38 (1.6%) | 1 (0.4%) | 61 (0.9%) | 6583 (8.1%) | 47 (1.7%) | 17 (1.7%) | 102 (2.6%) | 6517 (7.8%) | 37 (1.7%) | 23 (1.5%) | 219 (2.2%) | 6404 (8.3%) | 102 (4.7%) | 62 (3.6%) | 2808 (8.9%) | 3711 (6.7%) | 6683 (7.3%) |
| Orthopedics | 231 (7%) | 30 (5%) | 306 (7.8%) | 10484 (12.6%) | 1245 (10.5%) | 116 (6.1%) | 390 (10.8%) | 9300 (12.6%) | 236 (10.1%) | 17 (7.4%) | 492 (7%) | 10306 (12.7%) | 296 (10.6%) | 71 (7.1%) | 377 (9.6%) | 10307 (12.4%) | 238 (10.7%) | 179 (11.4%) | 813 (8.3%) | 9821 (12.7%) | 446 (20.6%) | 213 (12.4%) | 4703 (15%) | 5689 (10.2%) | 11051 (12.1%) |
| Others | 263 (8%) | 89 (14.9%) | 502 (12.8%) | 15533 (18.7%) | 2047 (17.2%) | 368 (19.5%) | 705 (19.4%) | 13267 (18%) | 220 (9.4%) | 53 (22.9%) | 816 (11.6%) | 15298 (18.8%) | 446 (15.9%) | 153 (15.2%) | 674 (17.1%) | 15114 (18.2%) | 301 (13.5%) | 258 (16.5%) | 1457 (14.8%) | 14371 (18.6%) | 519 (23.9%) | 350 (20.4%) | 5734 (18.2%) | 9784 (17.6%) | 16387 (18%) |
| Otolaryngology | 141 (4.3%) | 56 (9.3%) | 121 (3.1%) | 7495 (9%) | 1019 (8.6%) | 251 (13.3%) | 172 (4.7%) | 6371 (8.7%) | 135 (5.8%) | 34 (14.7%) | 305 (4.3%) | 7339 (9%) | 337 (12%) | 186 (18.5%) | 321 (8.2%) | 6969 (8.4%) | 131 (5.9%) | 232 (14.8%) | 386 (3.9%) | 7064 (9.1%) | 246 (11.3%) | 332 (19.4%) | 1539 (4.9%) | 5696 (10.2%) | 7813 (8.6%) |
| Plastic Surgery | 14 (0.4%) | 8 (1.3%) | 16 (0.4%) | 1922 (2.3%) | 101 (0.8%) | 22 (1.2%) | 25 (0.7%) | 1812 (2.5%) | 13 (0.6%) | 0 (0%) | 28 (0.4%) | 1919 (2.4%) | 13 (0.5%) | 9 (0.9%) | 33 (0.8%) | 1905 (2.3%) | 15 (0.7%) | 14 (0.9%) | 62 (0.6%) | 1869 (2.4%) | 33 (1.5%) | 30 (1.7%) | 605 (1.9%) | 1292 (2.3%) | 1960 (2.2%) |
| Surgical Oncology | 24 (0.7%) | 12 (2%) | 33 (0.8%) | 1567 (1.9%) | 195 (1.6%) | 60 (3.2%) | 41 (1.1%) | 1340 (1.8%) | 34 (1.5%) | 8 (3.5%) | 55 (0.8%) | 1539 (1.9%) | 43 (1.5%) | 18 (1.8%) | 32 (0.8%) | 1543 (1.9%) | 41 (1.8%) | 27 (1.7%) | 125 (1.3%) | 1443 (1.9%) | 44 (2%) | 71 (4.1%) | 485 (1.5%) | 1036 (1.9%) | 1636 (1.8%) |
| Thoracic Surgery | 21 (0.6%) | 5 (0.8%) | 49 (1.2%) | 1578 (1.9%) | 195 (1.6%) | 14 (0.7%) | 50 (1.4%) | 1394 (1.9%) | 30 (1.3%) | 1 (0.4%) | 245 (3.5%) | 1377 (1.7%) | 134 (4.8%) | 42 (4.2%) | 201 (5.1%) | 1276 (1.5%) | 32 (1.4%) | 21 (1.3%) | 134 (1.4%) | 1466 (1.9%) | 42 (1.9%) | 17 (1%) | 780 (2.5%) | 814 (1.5%) | 1653 (1.8%) |
| Urology | 177 (5.4%) | 65 (10.9%) | 193 (4.9%) | 8450 (10.2%) | 1367 (11.5%) | 235 (12.4%) | 372 (10.3%) | 6911 (9.4%) | 164 (7%) | 39 (16.9%) | 371 (5.3%) | 8311 (10.2%) | 236 (8.4%) | 122 (12.1%) | 206 (5.2%) | 8321 (10%) | 353 (15.8%) | 177 (11.3%) | 2383 (24.2%) | 5972 (7.7%) | 173 (8%) | 133 (7.8%) | 2689 (8.6%) | 5890 (10.6%) | 8885 (9.8%) |
| Vascular Surgery | 225 (6.8%) | 55 (9.2%) | 166 (4.2%) | 2296 (2.8%) | 634 (5.3%) | 56 (3%) | 158 (4.4%) | 1894 (2.6%) | 106 (4.5%) | 16 (6.9%) | 251 (3.6%) | 2369 (2.9%) | 182 (6.5%) | 52 (5.2%) | 145 (3.7%) | 2363 (2.8%) | 138 (6.2%) | 56 (3.6%) | 793 (8.1%) | 1755 (2.3%) | 78 (3.6%) | 114 (6.6%) | 972 (3.1%) | 1578 (2.8%) | 2742 (3%) |
| Post-op LOS, median (Q1–Q3) | 4 (2–9) | 2 (1–5) | 5 (2–10) | 2 (1–4) | 3 (2–7) | 2 (1–4) | 4 (2–8) | 2 (1–4) | 2 (1–5) | 2 (1–3) | 4 (2–10) | 2 (1–5) | 3 (1–7) | 2 (1–4) | 3 (2–8) | 2 (1–5) | 4 (2–8) | 2 (1–5) | 5 (2–9) | 2 (1–4) | 3 (1–5) | 2 (1–4) | 3 (1–6) | 2 (1–4) | 2 (1–5) |
| Mortality | 143 (4.3%) | 11 (1.8%) | 220 (5.6%) | 718 (0.9%) | 207 (1.7%) | 25 (1.3%) | 125 (3.4%) | 735 (1%) | 41 (1.8%) | 6 (2.6%) | 311 (4.4%) | 734 (0.9%) | 70 (2.5%) | 15 (1.5%) | 124 (3.2%) | 883 (1.1%) | 87 (3.9%) | 33 (2.1%) | 415 (4.2%) | 557 (0.7%) | 18 (0.8%) | 5 (0.3%) | 555 (1.8%) | 514 (0.9%) | 1092 (1.2%) |
Missed Documentation Incidence
Overall the agreement between the algorithms and the preoperative note was greater than 84% for all comorbidities other than chronic pain (63.5%). However, when there were divergent results, most often it was the algorithm detecting disease that was missed in the preoperative note. The algorithm detected disease not documented by the anesthesia team in 34.5% of cases for chronic pain (vs. 1.9% of cases where chronic pain was documented by not detected by the algorithm), 4.0% of cases for diabetes (vs. 2.1%), 4.3% of cases for CHF (vs.0.7%), 4.3% of cases for COPD (vs.1.1%), 7.7% of cases for afib (vs. 0.3%), and 10.8% of cases for CKD (vs. 1.7%).
Association of Undocumented Disease with Length of Stay (LOS) and Mortality
In order to asses if the undocumented disease itself was associated with worse postoperative outcomes we created risk adjusted models for both LOS and mortality. The results of the risk adjusted models for mortality and LOS were compared for cases where the preprocedure evaluation did not note the disease but the algorithm did flag the disease (P−/A+) with cases where the algorithm and the preoperative note both flagged the disease (P+/A+). Thus in both groups the disease was present in the patient, the difference was the documentation in the preoperative evaluation. This analysis demonstrated that for all diseases except chronic pain, the lack of documentation of the disease on the preoperative evaluation was associated with a longer LOS. With regard to mortality the discrepancy was associated with increased mortality for afib a with the remainder of the p-values lacking significance. These results are shown are shown in Table 2 and Figure 1. The comparisons for all groups are shown in Supplementary Table 2.
| Mortality | LOS (log scale) | |||
|---|---|---|---|---|
| Missed Diagnosis | Odds Ratio (95% CI) | P Value | Geo mean ratio (95% CI) | P Value |
| Atrial Fibrillation | 1.75 (1.23 , 2.48) | 0.002 | 1.25 (1.20 , 1.28) | <0.001 |
| Chronic Pain | 0.95 (0.58 , 1.56) | 0.846 | 0.98 (0.95 , 1.02) | 0.339 |
| Diabetes | 1.27 (0.98 , 1.63) | 0.066 | 1.06 (1.03 , 1.10) | <0.001 |
| Congestive Heart Failure | 1.21 (0.96 , 1.54) | 0.114 | 1.11 (1.06 , 1.15) | <0.001 |
| Chronic Obstructive Pulmonary Disease | 1.13 (0.82 , 1.56) | 0.456 | 1.11 (1.06 , 1.15) | <0.001 |
| Chronic Kidney Disease | 0.98 (0.75 , 1.27) | 0.860 | 1.06 (1.02 , 1.10) | 0.002 |
| Each additional missed diagnosis | 1.52 (1.42–1.63) | <0.001 | 1.11 (1.10–1.12) | <0.001 |
We expect a 25% increase in the geometric mean of LOS for afib missed dx compared to A+/P+
For each additional missed dx, the geometric mean of LOS increases about 11%
Figure1.
Plots showing the effects and 95% confidence interval of not documented co-morbid diseases on postoperative mortality(A) (odds-ratio) and length of stay (B) (ratio of days increased). The incremental effect of each undocumented disease is shown with the red box at the bottom. For mortality a-fib was associated with an increased risk; each additional undocumented disease increased the odds of mortality. For length of stay (LOS), all diseases except chronic pain were associated with an increased LOS (geometric mean ratio >1), and each additional undocumented disease was also associated with an increased LOS.
Models were also created to examine any additive effect of missing multiple diagnoses. For each missed disease the odds of mortality increased 1.52 (95% CI 1.42–1.63) and the LOS increased by approximately 11%, with a geometric mean ratio of 1.11 (95% CI 1.10–1.12). These results are shown in Figure 1 and Table 2.
Discussion
In this manuscript we used precise criteria in rules-based algorithms for common comorbid diseases and examined cases where the algorithm identified disease that was not documented in the anesthesia preprocedure evaluation. Overall, the concordance between the algorithms and the preop note was good for all diseases other than chronic pain (Table 1). However, in those cases where the algorithm detected disease that was not documented in the note, there was an association with an increased LOS for all diseases and an increased mortality for afib. Additionally, the effect was additive for both mortality and LOS – that is each additional disease which was not documented was associated with a longer LOS and mortality (Figure 1 and Table 2).
The findings of this study bring some important take away points. Firstly, while the EHR has helped solve the problem of physician’s lack of access to key information about their patients, the information is often spread across multiple locations, poorly presented, and at times hard to determine. Thus, while the information may be technically available it is often infeasible for physicians to access given their time constraints and the realities of modern patient care. Consistent with previous work6, 7, we have that key diseases are not documented with some frequency.
Of particular note is the discrepancy with regard to chronic pain. 34.5% of patients were flagged as having chronic pain by the algorithms, yet this condition was not documented in the preoperative note. The reasons for this are unclear. One possibility is that the criteria used in the algorithm were too sensitive – for example occasional home opioid use may not always indicate chronic pain. However, all of the criteria (home opioid use, previous documention of chronic pain syndromes, previous visits to pain clinics and previous consultation by pain management) are consistent with risk factors for increased postoperative pain. Thus, another possibility is that chronic pain is particularly underrecognized by anesthesia providers. This could potentially be due to the lack of a clear definition as opposed to many other more “objective” diseases. Given the current opioid epidemic and recurring issues around postoperative pain, this may warrant future study.
The second point is that this failure to document the existence of key comorbid diseases is associated with worse perioperative outcomes – specifically mortality and LOS. It has been well reported that diseases such as diabetes, CHF, COPD, afib and others are associated with worse perioperative outcomes. What has been less proven is the extent to which these risk factors might be modifiable. While some studies looking at better glucose control27, or prehabilitation28 have shown positive effects on postoperative outcomes, most studies have been limited to single diseases or small in size. Further, the objective management of some of these diseases may not necessarily differ from a regular patient – i.e. a patient with well controlled CHF or paroxysmal atrial fibrillation may not have anything specific optimized perioperatively. Nonetheless, we have noted that in patients where the condition was not documented in the preoperative evaluation there was an association with increased LOS and the number of diseases missed was associated with longer LOS and mortality. A growing body of evidence suggests that what we as anesthesiologists do in the operating room has profound effects on longer term outcomes. For example, recent evidence has demonstrated intraoperative hypotension to be associated with postoperative acute kidney injury (AKI) and mortality13, 22, blood transfusion with postoperative pulmonary and thromboembolic complications and infection29, glucose management with postoperative infections27, and lung protective ventilation with postoperative pulmonary complications30. Further, enhanced recovery after surgery (ERAS) protocols, which are often a distillation of best practices, have been demonstrated to improve post-operative outcomes31. An exhaustive list of best practice management for each of the co-morbidities that were studied and the extent to which they were applied to each patient would be beyond the scope of this manuscript. We also did not attempt to differentiate if patients where the anesthesiologist did not document the disease had any different postoperative treatment by the surgical team. Nonetheless, this study does demonstrate that the difficulties associated with simply obtaining accurate information from the EHR is associated with worse perioperative outcomes.
There are some important limitations of this study. This is a single center retrospective review. Thus, it is possible that these findings may not extend to other locations. However, our EHR (EPIC) is the most widely used EHR in the United States. While some elements of EHR design are customized by the hospital, the overall issues of usability are widespread and well documented. Thus, we believe that while the exact incidence of missing disease documentation might differ, most hospitals will have similar issues.
Another limitation of this study is the way in which we determined the documentation of the key comorbid diseases in the preoperative evaluation. As noted in the methods, in our EHR, the pre-operative note is populated by a series of checkboxes for common and significant diseases. However, the note also contains areas for free text. Thus, it is possible that in some patients the disease was documented in free text and not with a checkbox, thus over-estimating the incidence of missed documentation. Similarly, it is possible the anesthesia team was aware of the disease, but their documentation was incomplete – something that cannot be quantified in a retrospective chart review. For this reason, throughout the manuscript, we have chosen to use the term missed documentation as opposed to missed diagnosis. There is likely a reasonable degree of correlation between the comorbidities the anesthesia team is aware of and those documented - the degree of correlation is beyond the scope of this study. However, as clinicians, we do believe that there is at least some fraction of patients for whom a comorbid disease is unknown by the anesthesia team at the time of the procedure. While this limitation would result in an over-estimation of the incidence of missed disease, it would actually serve to reduce the power of detecting an association between the missed disease and the outcomes of interest – LOS and mortality. The fact that these associations were present, despite this limitation, supports the hypothesis that the association between a missed comorbidity and perioperative outcomes are real and that the choices anesthesiologists make in managing these critical diseases have implications beyond the OR.
Lastly, this retrospective study cannot demonstrate causation, only association. While we attempted to adjust for all clinical confounders we cannot rule out the existence of other confounding variables, bias, etc.
Overall, these results add to the increasing body of evidence that current workflows create challenges for physicians to accurately and quickly obtain and document critical information about comorbid patient diseases. Additionally, the association between this missed documentation and postoperative outcomes augment the many studies that indicate that the anesthetic plan has effects well beyond the operating room in ways that we have not yet fully understand. This study supports the trend towards increased utilization of care pathways and user interface design in order to improve the EHR and enhance patient safety.
Supplementary Material
Key Points:
The anesthesia preoperative evaluation may fail to document key comorbid diseases of which there is evidence in the EHR and this missed documentation is associated with an increased postoperative length of stay and mortality.
Key Points:
Question:
Are key co-morbid diseases accurately documented in the preanesthetic evaluation, and is a missed documentation associated with negative postoperative outcomes?
Findings:
Key co-morbid diseases are not infrequently omitted from the preanesthetic evaluation and this this missed documentation is associated with longer length of stay and postoperative mortality
Meaning:
There is data in the EHR that is sometimes not picked up on by the anesthesiologist and this is associated with negative postoperative outcomes.
Financial Interests:
Dr. Hofer is the President of Clarity Healthcare Analytics Inc. a company that assists hospitals with extracting and using data from their electronic medical records. The company currently owns the rights to the PDW software that was used to extract data from the electronic health record.
Dr. Hofer receives research funding from Merck Pharmaceuticals
This work was supported by funds from NIH Grant# R01EB029751 and R01HL144692 and K01HL150318.
Glossary:
- EHR
Electronic Healthcare Record
- CHF
Congestive Heart Failure
- Afib
Atrial Fibrillation
- CKD
Chronic Kidney Disease
- COPD
Chronic Obstructive Pulmonary Disease
- PDW
Perioperative data warehouse
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