Electronic Surveillance For Catheter-Associated Urinary Tract Infection Using Natural Language Processing.

Catheter-associated urinary tract infection (CAUTI) is a common and costly healthcare-associated infection, yet measuring it accurately is challenging and resource-intensive. Electronic surveillance promises to make this task more objective and efficient in an era of new financial and regulatory imperatives, but previous surveillance approaches have used a simplified version of the definition. We applied a complete definition, including subjective elements identified through natural language processing of clinical notes. Through examination of documentation practices, we defined a set of rules that identified positively and negatively asserted symptoms of CAUTI. Our algorithm was developed on a training set of 1421 catheterizedpatients and prospectively validated on 1567 catheterizedpatients. Compared to gold standard chart review, our tool had a sensitivity of 97.1%, specificity of 94.5% PPV of 66.7% and NPV of 99.6% for identifying CAUTI. We discuss sources of error and suggestions for more computable future definitions.

Diagnosing Surgical Site Infection Using Wound Photography: A Scenario-Based Study.

BACKGROUND: Postoperative surgical site infections (SSI) are common and costly. Most occur post discharge, and can result in potentially preventable readmission or unnecessary urgent evaluation. Mobile health approaches incorporating patient-generated wound photos are being implemented in an attempt to optimize triage and management. We assessed how adding wound photos to existing data sources modifies provider decision making. STUDY DESIGN: We used a web-based simulation survey using a convenience sample of providers with expertise in surgical infections. Participants viewed a range of scenarios, including surgical history, physical exam, and description of wound appearance. All participants reported SSI diagnosis, diagnostic confidence, and management recommendations (main outcomes) first without, and then with, accompanying wound photos. At each step, participants ranked the most important features contributing to their decision. RESULTS: Eighty-three participants completed a median of 5 scenarios (interquartile range 4 to 7). Most participants were physicians in academic surgical specialties (n = 70 [84%]). The addition of photos improved overall diagnostic accuracy from 67% to 76% (p < 0.001), and increased specificity from 77% to 92% (p < 0.001), but did not significantly increase sensitivity (55% to 65%; p = 0.16). Photos increased mean confidence in diagnosis from 5.9 of 10 to 7.4 of 10 (p < 0.001). Overtreatment recommendations decreased from 48% to 16% (p < 0.001), and undertreatment did not change (28% to 23%; p = 0.20) with the addition of photos. CONCLUSIONS: The addition of wound photos to existing data as available via chart review and telephone consultation with patients significantly improved diagnostic accuracy and confidence, and prevented proposed overtreatment in scenarios without SSI. Post-discharge mobile health technologies have the potential to facilitate patient-centered care, decrease costs, and improve clinical outcomes.

A Prognostic Model of Surgical Site Infection Using Daily Clinical Wound Assessment.

BACKGROUND: Surgical site infection (SSI) remains a common, costly, and morbid health care-associated infection. Early detection can improve outcomes, yet previous risk models consider only baseline risk factors (BF) not incorporating a proximate and timely data source-the wound itself. We hypothesize that incorporation of daily wound assessment improves the accuracy of SSI identification compared with traditional BF alone. STUDY DESIGN: A prospective cohort of 1,000 post open abdominal surgery patients at an academic teaching hospital were examined daily for serial features (SF), for example, wound characteristics and vital signs, in addition to standard BF, for example, wound class. Using supervised machine learning, we trained 3 Naïve Bayes classifiers (BF, SF, and BF+SF) using patient data from 1 to 5 days before diagnosis to classify SSI on the following day. For comparison, we also created a simplified SF model that used logistic regression. Control patients without SSI were matched on 5 similar consecutive postoperative days to avoid confounding by length of stay. Accuracy, sensitivity/specificity, and area under the receiver operating characteristic curve were calculated on a training and hold-out testing set. RESULTS: Of 851 patients, 19.4% had inpatient SSIs. Univariate analysis showed differences in C-reactive protein, surgery duration, and contamination, but no differences in American Society of Anesthesiologists scores, diabetes, or emergency surgery. The BF, SF, and BF+SF classifiers had area under the receiver operating characteristic curves of 0.67, 0.76, and 0.76, respectively. The best-performing classifier (SF) had optimal sensitivity of 0.80, specificity of 0.64, positive predictive value of 0.35, and negative predictive value of 0.93. Features most associated with subsequent SSI diagnosis were granulation degree, exudate amount, nasogastric tube presence, and heart rate. CONCLUSIONS: Serial features provided moderate positive predictive value and high negative predictive value for early identification of SSI. Addition of baseline risk factors did not improve identification. Features of evolving wound infection are discernable before the day of diagnosis, based primarily on visual inspection.

A patient-centered system in a provider-centered world: challenges of incorporating post-discharge wound data into practice.

OBJECTIVE: The proposed Meaningful Use Stage 3 recommendations require healthcare providers to accept patient-generated health data (PGHD) by 2017. Yet, we know little about the tensions that arise in supporting the needs of both patients and providers in this context. We sought to examine these tensions when designing a novel, patient-centered technology - mobile Post-Operative Wound Evaluator (mPOWEr) - that uses PGHD for post-discharge surgical wound monitoring. MATERIALS AND METHODS: As part of the iterative design process of mPOWEr, we conducted semistructured interviews and think-aloud sessions using mockups with surgical patients and providers. We asked participants how mPOWEr could enhance the current post-discharge process for surgical patients, then used grounded theory to develop themes related to conflicts and agreements between patients and providers. RESULTS: We identified four areas of agreement: providing contextual metadata, accessible and actionable data presentation, building on existing sociotechnical systems, and process transparency. We identified six areas of conflict, with patients preferring: more flexibility in data input, frequent data transfer, text-based communication, patient input in provider response prioritization, timely and reliable provider responses, and definitive diagnoses. DISCUSSION: We present design implications and potential solutions to the identified conflicts for each theme, illustrated using our work on mPOWEr. Our experience highlights the importance of bringing a variety of stakeholders, including patients, into the design process for PGHD applications. CONCLUSION: We have identified critical barriers to integrating PGHD into clinical care and describe design implications to help address these barriers. Our work informs future efforts to ensure the smooth integration of essential PGHD into clinical practice.

Patient perspectives on post-discharge surgical site infections: towards a patient-centered mobile health solution.

BACKGROUND: Post-discharge surgical site infections (SSI) are a major source of morbidity, expense and anxiety for patients. However, patient perceptions about barriers experienced while seeking care for post-discharge SSI have not been assessed in depth. We explored patient experience of SSI and openness to a mobile health (mHealth) wound monitoring "app" as a novel solution to address this problem. METHODS: Mixed method design with semi-structured interviews and surveys. Participants were patients who had post-discharge surgical wound complications after undergoing operations with high risk of SSI, including open colorectal or ventral hernia repair surgery. The study was conducted at two affiliated teaching hospitals, including an academic medical center and a level 1 trauma center. RESULTS: From interviews with 13 patients, we identified 3 major challenges that impact patients' ability to manage post-discharge surgical wound complications, including required knowledge for wound monitoring from discharge teaching, self-efficacy for wound monitoring at home, and accessible communication with their providers about wound concerns. Patients found an mHealth wound monitoring application highly acceptable and articulated its potential to provide more frequent, thorough, and convenient follow-up that could reduce post-discharge anxiety compared to the current practice. Major concerns with mHealth wound monitoring were lack of timely response from providers and inaccessibility due to either lack of an appropriate device or usability challenges. CONCLUSIONS: Our findings reveal gaps and frustrations with post-discharge care after surgery which could negatively impact clinical outcomes and quality of life. To address these issues, we are developing mPOWEr, a patient-centered mHealth wound monitoring application for patients and providers to collaboratively bridge the care transition between hospital and home.