Clinical Decision-Making: Observing the Smartphone User An Observational Study in Predicting Acute Surgical Patients’ Suitability for Discharge
INTRODUCTION: An accurate and rapid assessment of an acutely unwell patient’s clinical status is paramount for the physician. There is an increasing trend to rely on investigations and results to inform a clinician of a patient’s clinical status, with the subtleties of clinical observation often ignored. The aim of this study was to determine if a patient’s use of a smartphone during the initial clinical assessment by a surgical consultant could be used as a surrogate marker for patient well-being, represented as their suitability for same-day discharge.
METHODS: This was a prospective observational study performed over 2 periods at a tertiary hospital in South Australia. All patients admitted by junior surgical doctors from the emergency department to the acute surgical unit were eligible for inclusion. Upon consultant review, their status as a smartphone user was recorded in addition to their duration of hospital stay and basic demographic data. All patients and all but 1 of the consultants were blinded to the trial.
RESULTS: Two hundred and twenty-one patients were eligible for inclusion. Of these patients, 11.3% were observed to be using a smartphone and 23.5% of patients were discharged home on day 1. Those who were observed to be using a smartphone were 5.29 times more likely to be discharged home on day 1 and were less likely to be subsequently readmitted.
CONCLUSIONS: The addition of the smartphone sign to a surgeon’s clinical acumen can provide yet another tool in aiding the decision for suitability for discharge.
© 2018 Society of Hospital Medicine
Demographic data are presented as means with standard deviations (SDs) or frequencies with percentages. A 2-sample Student t test was used to compare the age of spP and spN patients. A χ2 test and logistic regressions were used to assess the association between smartphone status and patient demographics, LOS, and requirement for surgery. Results are presented as odds ratios (ORs) with 95% confidence intervals (CIs). A P value of <0.05 was considered significant. All data were analyzed by using R 3.2.3 (R Foundation for Statistical Computing, Vienna, Austria).
RESULTS
During the 2 observation periods, a total of 227 eligible surgical admissions were observed with complete data for 221 patients. Six patients were excluded as their smartphone status was not recorded. The study sample represents our population of interest within an ASU, and we had complete data for 97.4% of participants with a 100% follow-up. There was no significant effect of study between the 2 observation periods (χ2 = 140.19; P = 0.10). The mean age of patients was 50.24 years. Further demographic data are presented in Table 1. Twenty-five (11.3%) patients were spP and 196 (88.7%) were spN. Fifty-two (23.5%) patients were discharged home on day 1, and 169 (76.5%) had admissions longer than 1 day (see Figure). Sixty (27%) patients underwent surgery during their admission. Twenty-two patients had unplanned readmissions; only 1 of these patients had been observed to be spP.
There was a statistically significant difference in ages between the spP and spN groups (t = 8.40; P < 0.0005), with the average age of spP patients being 31.84 years compared with 52.58 years for spN patients. There was no statistical difference between gender and smartphone status (χ2 = 1.78; P = 0.18; Table 2).
For those patients discharged home on day 1, there was a statistically significant association with being spP (χ2 = 14.55, P = 0.0001). Patients who were spP were 5.29 times more likely to be discharged on day 1 (95% CI, 2.24-12.84). Of the variables analyzed, only gender failed to demonstrate an effect on discharge home on day 1 (Table 3). Overall, the presence of a smartphone was found to have a sensitivity of 56.0% (95% CI, 34.93-75.60) and a specificity of 80.6% (95% CI, 74.37-85.90) in regard to same-day discharge. However, it was found to have a negative predictive value of 93.49% (95% CI, 88.65-96.71).
When examining readmission rates, only 4% of spP patients were readmitted versus 10.7% of spN patients. Accounting for variables, spP patients were 4 times less likely to be readmitted, though this was not statistically significant (OR 4.02; 95% CI, 0.43-37.2; P = 0.22). Furthermore, when examining only those patients discharged on day 1, smartphone status was not a predictor of readmission (OR 0.94; 95% CI, 0.06-15.2; P = 0 .97).
To mitigate the effect of age, analysis was conducted excluding those aged over 55 years (the previous retirement age in Australia), leaving 131 patients for analysis. The average age of spP patients was 31.8 years (SD 10.0) compared with 36.7 years (SD 10.9) for spN patients, representing a significant difference (t = 2.14; P = 0.04); 51.1% of patients were male, 19.1% of patients were spP, 26.0% of patients proceeded to an operation, the oldest spP was 51 years, and 29.0% of patients were discharged home on day 1. There was no difference in gender and smartphone status (χ2 = 0.33; P = 0.6). When analyzing those discharged on day 1, again spP patients were more likely to be discharged home (χ2 = 9.4; P = 0.002), and spP patients were 3.6 times more likely to be discharged home on day 1.
There were 4 spP patients who underwent an operation. Two patients had an incision and drainage of a perianal abscess, 1 patient underwent a laparotomy for an internal hernia after recently undergoing a Roux-en-Y gastric bypass at another hospital, and the final patient underwent a laparoscopic appendicectomy. One of these patients was still discharged home on day 1.
DISCUSSION
As J. A. Lindsay4 said, “For one mistake made for not knowing, ten mistakes are made for not looking.” At medical school, we are taught the finer techniques of the physical examination in order to support our diagnosis made from the history. It is not until we are experienced clinicians do we develop the clinical acumen and ability to tell an unwell patient from a well patient at a glance—colloquially known as the “end of the bed” assessment. In the pretechnology era, a well patient could frequently be seen reading their book, eg, the “novel-sign.” With the advent of the smartphone and electronic devices upon which novels can be read, statuses updated, and locations “checked into” (ie, the modern “vital signs”), the book sign may be a thing of the past. However, the ability for the clinician to assess a patient’s wellness is still crucial, and the value of any additional “physical signs” need to be estimated.
