Using a Machine Learning System to Identify and Prevent Medication Prescribing Errors: A Clinical and Cost Analysis Evaluation.

BACKGROUND: Clinical decision support (CDS) alerting tools can identify and reduce medication errors. However, they are typically rule-based and can identify only the errors previously programmed into their alerting logic. Machine learning holds promise for improving medication error detection and reducing costs associated with adverse events. This study evaluates the ability of a machine learning system (MedAware) to generate clinically valid alerts and estimates the cost savings associated with potentially prevented adverse events. METHODS: Alerts were generated retrospectively by the MedAware system on outpatient data from two academic medical centers between 2009 and 2013. MedAware alerts were compared to alerts in an existing CDS system. A random sample of 300 alerts was selected for medical record review. Frequency and severity of potential outcomes of alerted medication errors of medium and high clinical value were estimated, along with associated health care costs of these potentially prevented adverse events. RESULTS: A total of 10,668 alerts were generated. Overall, 68.2% of MedAware alerts would not have been generated by the existing CDS system. Ninety-two percent of a random sample of the chart-reviewed alerts were accurate based on structured data available in the record, and 79.7% were clinically valid. Estimated cost of adverse events potentially prevented in an outpatient setting was more than $60 per drug alert and $1.3 million when extrapolating study findings to the full patient population. CONCLUSION: A machine learning system identified clinically valid medication error alerts that might otherwise be missed with existing CDS systems. Estimates show potential for cost savings associated with potentially prevented adverse events.

Who adopts a patient portal?: An application of the diffusion of innovation model.

BACKGROUND: Patient portals have emerged as an important tool through which patients can access online health information and engage in their health care. However, we know little about how patients perceive portals and whether patient perceptions might influence portal adoption. OBJECTIVE: Apply the diffusion of innovation (DOI) theory to assess perceptions of adopters and non-adopters of a patient portal. METHODS: We conducted a cross-sectional survey of adopters and non-adopters of the portal. Our survey consisted of perceived attributes from the DOI theory, socio-demographic characteristics and patient perceptions of technology adoption. RESULTS: Three factors representing perceived attributes from DOI theory accounted for 73% of the variance in the data: Factor 1 - Relative Advantage (27%); Factor 2 - Ease of Use (24%) and Factor 3 - Trialability (22%). Adopters perceived greater Relative Advantage [mean (SD)] = 3.8 (0.71) versus 3.2 (0.89), p < 0.001, Ease of Use = 4.1 (0.71) versus 3.3 (0.95), p < 0.001 and Trialability = 4.0 (0.57) versus 3.4 (0.99), p < 0.001 than non-adopters. In multivariate modelling, age [OR = 3.75, 95% CI: (2.17, 6.46), p < 0.001] and income [OR = 1.87, 95% CI: (1.17, 3.00), p < 0.01] predicted adoption of the portal. Among DOI factors, Relative advantage predicted adoption of the portal [OR = 1.48, 95% CI: (1.03, 2.11), p < 0.05]. CONCLUSION: Patients will adopt a patient portal if they perceive it to offer a relative advantage over existing practices such as telephoning or visiting the doctor's office. Organisations seeking to increase the adoption of patient portals should implement strategies to promote the relative advantage of portals as, for example, through posters in waiting and exam rooms. A digital divide in the adoption of patient portals may exist with respect to age and income.

Cultural taboos as a factor in the participation rate of Native Americans in STEM.

BACKGROUND: Native Americans are underrepresented in science, technology, engineering, and mathematics (STEM). We investigated whether having to violate cultural taboos might be a factor in the decisions of some Native Americans not to pursue STEM degrees. Many STEM faculty likely know very little about Native Americans' historical experiences with an education system that has been used to forcibly acculturate them and so may not be aware of the threat many Native Americans perceive from curricula that claim cultural neutrality yet require Native Americans to violate strongly held cultural beliefs. RESULTS: We reviewed the relevant literature, surveyed 96 students from 42 different tribes, and interviewed two STEM and two non-STEM faculty at Haskell Indian Nations University. We found that 50% of survey respondents generally observe tribal taboos, 38% would choose not to pursue a science major if they knew or suspected that doing so would require them to violate an important tribal taboo, and 67% would be more likely to take science classes if the science curriculum was more respectful of tribal taboos. The most problematic activities and animals encountered in laboratory classes include, in order of discomfort level, human dissection, human bodies, animal dissection, snakes, spiders, and lizards. CONCLUSIONS: Increasing Native American participation in STEM requires that their cultural concerns regarding STEM curricula be acknowledged and addressed. This is important for several reasons. First, Native Americans have the highest poverty rate of all racial/ethnic groups, while STEM graduates have higher employment rates and salaries than non-STEM graduates. Second, increasing diversity in STEM supports cognitive growth and critical thinking, benefits problem solving, and contributes to increasing productivity, creativity, and global competitiveness. Third, there is a long history of exploitation of Native Americans and their lands by scientists and engineers, so it is particularly important to increase Native American participation so that their interests are represented in these professions. Many Native Americans' concerns can be proactively and reasonably accommodated to provide a more respectful and welcoming learning and working environment and increase their participation in STEM, to everyone's benefit.

Screening for medication errors using an outlier detection system.

Objective: The study objective was to evaluate the accuracy, validity, and clinical usefulness of medication error alerts generated by an alerting system using outlier detection screening. Materials and Methods: Five years of clinical data were extracted from an electronic health record system for 747 985 patients who had at least one visit during 2012-2013 at practices affiliated with 2 academic medical centers. Data were screened using the system to detect outliers suggestive of potential medication errors. A sample of 300 charts was selected for review from the 15 693 alerts generated. A coding system was developed and codes assigned based on chart review to reflect the accuracy, validity, and clinical value of the alerts. Results: Three-quarters of the chart-reviewed alerts generated by the screening system were found to be valid in which potential medication errors were identified. Of these valid alerts, the majority (75.0%) were found to be clinically useful in flagging potential medication errors or issues. Discussion: A clinical decision support (CDS) system that used a probabilistic, machine-learning approach based on statistically derived outliers to detect medication errors generated potentially useful alerts with a modest rate of false positives. The performance of such a surveillance and alerting system is critically dependent on the quality and completeness of the underlying data. Conclusion: The screening system was able to generate alerts that might otherwise be missed with existing CDS systems and did so with a reasonably high degree of alert usefulness when subjected to review of patients' clinical contexts and details.

Design of a cluster-randomized trial of electronic health record-based tools to address overweight and obesity in primary care.

BACKGROUND: Primary care providers often fail to identify patients who are overweight or obese or discuss weight management with them. Electronic health record-based tools may help providers with the assessment and management of overweight and obesity. PURPOSE: We describe the design of a trial to examine the effectiveness of electronic health record-based tools for the assessment and management of overweight and obesity among adult primary care patients, as well as the challenges we encountered. METHODS: We developed several new features within the electronic health record used by primary care practices affiliated with Brigham and Women's Hospital in Boston, MA. These features included (1) reminders to measure height and weight, (2) an alert asking providers to add overweight or obesity to the problem list, (3) reminders with tailored management recommendations, and (4) a Weight Management screen. We then conducted a pragmatic, cluster-randomized controlled trial in 12 primary care practices. RESULTS: We randomized 23 clinical teams ("clinics") within the practices to the intervention group (n = 11) or the control group (n = 12). The new features were activated only for clinics in the intervention group. The intervention was implemented in two phases: the height and weight reminders went live on 15 December 2011 (Phase 1), and all of the other features went live on 11 June 2012 (Phase 2). Study enrollment went from December 2011 through December 2012, and follow-up ended in December 2013. The primary outcomes were 6-month and 12-month weight change among adult patients with body mass index ≥25 who had a visit at one of the primary care clinics during Phase 2. Secondary outcome measures included the proportion of patients with a recorded body mass index in the electronic health record, the proportion of patients with body mass index ≥25 who had a diagnosis of overweight or obesity on the electronic health record problem list, and the proportion of patients with body mass index ≥25 who had a follow-up appointment about their weight or were prescribed weight loss medication. LESSONS LEARNED: We encountered challenges in our development of an intervention within the existing structure of an electronic health record. For example, although we decided to randomize clinics within primary care practices, this decision may have introduced contamination and led to some imbalance of patient characteristics between the intervention and control practices. Using the electronic health record as the primary data source reduced the cost of the study, but not all desired data were recorded for every participant. CONCLUSION: Despite the challenges, this study should provide valuable information about the effectiveness of electronic health record-based tools for addressing overweight and obesity in primary care.

Impact of an automated email notification system for results of tests pending at discharge: a cluster-randomized controlled trial.

BACKGROUND AND OBJECTIVE: Physician awareness of the results of tests pending at discharge (TPADs) is poor. We developed an automated system that notifies responsible physicians of TPAD results via secure, network email. We sought to evaluate the impact of this system on self-reported awareness of TPAD results by responsible physicians, a necessary intermediary step to improve management of TPAD results. METHODS: We conducted a cluster-randomized controlled trial at a major hospital affiliated with an integrated healthcare delivery network in Boston, Massachusetts. Adult patients with TPADs who were discharged from inpatient general medicine and cardiology services were assigned to the intervention or usual care arm if their inpatient attending physician and primary care physician (PCP) were both randomized to the same study arm. Patients of physicians randomized to discordant study arms were excluded. We surveyed these physicians 72 h after all TPAD results were finalized. The primary outcome was awareness of TPAD results by attending physicians. Secondary outcomes included awareness of TPAD results by PCPs, awareness of actionable TPAD results, and provider satisfaction. RESULTS: We analyzed data on 441 patients. We sent 441 surveys to attending physicians and 353 surveys to PCPs and received 275 and 152 responses from 83 different attending physicians and 112 different PCPs, respectively (attending physician survey response rate of 63%). Intervention attending physicians and PCPs were significantly more aware of TPAD results (76% vs 38%, adjusted/clustered OR 6.30 (95% CI 3.02 to 13.16), p<0.001; 57% vs 33%, adjusted/clustered OR 3.08 (95% CI 1.43 to 6.66), p=0.004, respectively). Intervention attending physicians tended to be more aware of actionable TPAD results (59% vs 29%, adjusted/clustered OR 4.25 (0.65, 27.85), p=0.13). One hundred and eighteen (85%) and 43 (63%) intervention attending physician and PCP survey respondents, respectively, were satisfied with this intervention. CONCLUSIONS: Automated email notification represents a promising strategy for managing TPAD results, potentially mitigating an unresolved patient safety concern. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov (NCT01153451).

Use of a web-based risk appraisal tool for assessing family history and lifestyle factors in primary care.

BACKGROUND: Primary care clinicians can play an important role in identifying individuals at increased risk of cancer, but often do not obtain detailed information on family history or lifestyle factors from their patients. OBJECTIVE: We evaluated the feasibility and effectiveness of using a web-based risk appraisal tool in the primary care setting. DESIGN: Five primary care practices within an academic care network were assigned to the intervention or control group. PARTICIPANTS: We included 15,495 patients who had a new patient visit or annual exam during an 8-month period in 2010-2011. INTERVENTION: Intervention patients were asked to complete a web-based risk appraisal tool on a laptop computer immediately before their visit. Information on family history of cancer was sent to their electronic health record (EHR) for clinicians to view; if accepted, it populated coded fields and could trigger clinician reminders about colon and breast cancer screening. MAIN MEASURES: The main outcome measure was new documentation of a positive family history of cancer in coded EHR fields. Secondary outcomes included clinician reminders about screening and discussion of family history, lifestyle factors, and screening. KEY RESULTS: Among eligible intervention patients, 2.0% had new information on family history of cancer entered in the EHR within 30 days after the visit, compared to 0.6% of eligible control patients (adjusted odds ratio = 4.3, p = 0.03). There were no significant differences in the percent of patients who received moderate or high risk reminders for colon or breast cancer screening. CONCLUSIONS: Use of this tool was associated with increased documentation of family history of cancer in the EHR, although the percentage of patients with new family history information was low in both groups. Further research is needed to determine how risk appraisal tools can be integrated with workflow and how they affect screening and health behaviors.

How physicians document outpatient visit notes in an electronic health record.

BACKGROUND: Clinical documentation, an essential process within electronic health records (EHRs), takes a significant amount of clinician time. How best to optimize documentation methods to deliver effective care remains unclear. OBJECTIVE: We evaluated whether EHR visit note documentation method was influenced by physician or practice characteristics, and the association of physician satisfaction with an EHR notes module. MEASUREMENTS: We surveyed primary care physicians (PCPs) and specialists, and used EHR and provider data to perform a multinomial logistic regression of visit notes from 2008. We measured physician documentation method use and satisfaction with an EHR notes module and determined the relationship between method and physician and practice characteristics. RESULTS: Of 1088 physicians, 85% used a single method to document the majority of their visits. PCPs predominantly documented using templates (60%) compared to 34% of specialists, while 38% of specialists predominantly dictated. Physicians affiliated with academic medical centers (OR 1.96, CI (1.23, 3.12)), based at a hospital (OR 1.57, 95% CI (1.04, 2.36)) and using the EHR for longer (OR 1.13, 95% CI (1.03, 1.25)) were more likely to dictate than use templates. Most physicians of 383 survey responders were satisfied with the EHR notes module, regardless of their preferred documentation method. CONCLUSIONS: Physicians predominantly utilized a single method of visit note documentation and were satisfied with their approach, but the approaches they chose varied. Demographic characteristics were associated with preferred documentation method. Further research should focus on why variation exists, and the quality of the documentation resulting from different methods used.

Patient perceptions of a personal health record: a test of the diffusion of innovation model.

BACKGROUND: Personal health records (PHRs) have emerged as an important tool with which patients can electronically communicate with their doctors and doctor's offices. However, there is a lack of theoretical and empirical research on how patients perceive the PHR and the differences in perceptions between users and non-users of the PHR. OBJECTIVE: To apply a theoretical model, the diffusion of innovation model, to the study of PHRs and conduct an exploratory empirical study on the applicability of the model to the study of perceptions of PHRs. A secondary objective was to assess whether perceptions of PHRs predict the perceived value of the PHR for communicating with the doctor's office. METHODS: We first developed a survey capturing perceptions of PHR use and other factors such as sociodemographic characteristics, access and use of technology, perceived innovativeness in the domain of information technology, and perceptions of privacy and security. We then conducted a cross-sectional survey (N = 1500). Patients were grouped into five groups of 300: PHR users (innovators, other users, and laggards), rejecters, and non-adopters. We applied univariate statistical analysis (Pearson chi-square and one-way ANOVA) to assess differences among groups and used multivariate statistical techniques (factor analysis and multiple regression analysis) to assess the presence of factors identified by the diffusion of innovation model and the predictors of our dependent variable (value of PHR for communicating with the doctor's office). RESULTS: Of the 1500 surveys, 760 surveys were returned for an overall response rate of 51%. Computer use among non-adopters (75%) was lower than that among PHR users (99%) and rejecters (92%) (P < .001). Non-adopters also reported a lower score on personal innovativeness in information technology (mean = 2.8) compared to 3.6 and 3.1, respectively, for users and rejecters (P < .001). Four factors identified by the diffusion of innovation model emerged in the factor analysis: ease of use, relative advantage, observability, and trialability. PHR users perceived greater ease of use and relative advantage of the PHR than rejecters and non-adopters (P < .001). Multiple regression analysis showed the following factors as significant positive predictors of the value of PHR for communicating with the doctor's office: relative advantage, ease of use, trialability, perceptions of privacy and security, age, and computer use. CONCLUSION: Our study found that the diffusion of innovation model fits the study of perceptions of the PHR and provides a suitable theoretical and empirical framework to identify the factors that distinguish PHR users from non-users. The ease of use and relative advantage offered by the PHR emerged as the most important domains among perceptions of PHR use and in predicting the value of the PHR. Efforts to improve uptake and use of PHRs should focus on strategies that enhance the ease of use of PHRs and that highlight the relative advantages of PHRs.

Effects of an online personal health record on medication accuracy and safety: a cluster-randomized trial.

OBJECTIVE: To determine the effects of a personal health record (PHR)-linked medications module on medication accuracy and safety. DESIGN: From September 2005 to March 2007, we conducted an on-treatment sub-study within a cluster-randomized trial involving 11 primary care practices that used the same PHR. Intervention practices received access to a medications module prompting patients to review their documented medications and identify discrepancies, generating 'eJournals' that enabled rapid updating of medication lists during subsequent clinical visits. MEASUREMENTS: A sample of 267 patients who submitted medications eJournals was contacted by phone 3 weeks after an eligible visit and compared with a matched sample of 274 patients in control practices that received a different PHR-linked intervention. Two blinded physician adjudicators determined unexplained discrepancies between documented and patient-reported medication regimens. The primary outcome was proportion of medications per patient with unexplained discrepancies. RESULTS: Among 121,046 patients in eligible practices, 3979 participated in the main trial and 541 participated in the sub-study. The proportion of medications per patient with unexplained discrepancies was 42% in the intervention arm and 51% in the control arm (adjusted OR 0.71, 95% CI 0.54 to 0.94, p=0.01). The number of unexplained discrepancies per patient with potential for severe harm was 0.03 in the intervention arm and 0.08 in the control arm (adjusted RR 0.31, 95% CI 0.10 to 0.92, p=0.04). CONCLUSIONS: When used, concordance between documented and patient-reported medication regimens and reduction in potentially harmful medication discrepancies can be improved with a PHR medication review tool linked to the provider's medical record. TRIAL REGISTRATION NUMBER: This study was registered at ClinicalTrials.gov (NCT00251875).

Impact of automated alerts on follow-up of post-discharge microbiology results: a cluster randomized controlled trial.

BACKGROUND: Failure to follow up microbiology results pending at the time of hospital discharge can delay diagnosis and treatment of important infections, harm patients, and increase the risk of litigation. Current systems to track pending tests are often inadequate. OBJECTIVE: To design, implement, and evaluate an automated system to improve follow-up of microbiology results that return after hospitalized patients are discharged. DESIGN: Cluster randomized controlled trial. SUBJECTS: Inpatient and outpatient physicians caring for adult patients hospitalized at a large academic hospital from February 2009 to June 2010 with positive and untreated or undertreated blood, urine, sputum, or cerebral spinal fluid cultures returning post-discharge. INTERVENTION: An automated e-mail-based system alerting inpatient and outpatient physicians to positive post-discharge culture results not adequately treated with an antibiotic at the time of discharge. MAIN MEASURES: Our primary outcome was documented follow-up of results within 3 days. Secondary outcomes included physician awareness and assessment of result urgency, impact on clinical assessments and plans, and preferred alerting scenarios. KEY RESULTS: We evaluated the follow-up of 157 post-discharge microbiology results from patients of 121 physicians. We found documented follow-up in 27/97 (28%) results in the intervention group and 8/60 (13%) in the control group [aOR 3.2, (95% CI 1.3-8.4); p=0.01]. Of all inpatient physician respondents, 32/82 (39%) were previously aware of the results, 45/77 (58%) felt the results changed their assessments and plans, 43/77 (56%) felt the results required urgent action, and 67/70 (96%) preferred alerts for current or broader scenarios. CONCLUSION: Our alerting system improved the proportion of important post-discharge microbiology results with documented follow-up, though the proportion remained low. The alerts were well received and may be expanded in the future.

Design and implementation of an automated email notification system for results of tests pending at discharge.

Physicians are often unaware of the results of tests pending at discharge (TPADs). The authors designed and implemented an automated system to notify the responsible inpatient physician of the finalized results of TPADs using secure, network email. The system coordinates a series of electronic events triggered by the discharge time stamp and sends an email to the identified discharging attending physician once finalized results are available. A carbon copy is sent to the primary care physicians in order to facilitate communication and the subsequent transfer of responsibility. Logic was incorporated to suppress selected tests and to limit notification volume. The system was activated for patients with TPADs discharged by randomly selected inpatient-attending physicians during a 6-month pilot. They received approximately 1.6 email notifications per discharged patient with TPADs. Eighty-four per cent of inpatient-attending physicians receiving automated email notifications stated that they were satisfied with the system in a brief survey (59% survey response rate). Automated email notification is a useful strategy for managing results of TPADs.

Randomized controlled trial of health maintenance reminders provided directly to patients through an electronic PHR.

BACKGROUND: Provider and patient reminders can be effective in increasing rates of preventive screenings and vaccinations. However, the effect of patient-directed electronic reminders is understudied. OBJECTIVE: To determine whether providing reminders directly to patients via an electronic Personal Health Record (PHR) improved adherence to care recommendations. DESIGN: We conducted a cluster randomized trial without blinding from 2005 to 2007 at 11 primary care practices in the Partners HealthCare system. PARTICIPANTS: A total of 21,533 patients with access to a PHR were invited to the study, and 3,979 (18.5%) consented to enroll. INTERVENTIONS: Patients in the intervention arm received health maintenance (HM) reminders via a secure PHR "eJournal," which allowed them to review and update HM and family history information. Patients in the active control arm received access to an eJournal that allowed them to input and review information related to medications, allergies and diabetes management. MAIN MEASURES: The primary outcome measure was adherence to guideline-based care recommendations. KEY RESULTS: Intention-to-treat analysis showed that patients in the intervention arm were significantly more likely to receive mammography (48.6% vs 29.5%, p = 0.006) and influenza vaccinations (22.0% vs 14.0%, p = 0.018). No significant improvement was observed in rates of other screenings. Although Pap smear completion rates were higher in the intervention arm (41.0% vs 10.4%, p < 0.001), this finding was no longer significant after excluding women's health clinics. Additional on-treatment analysis showed significant increases in mammography (p = 0.019) and influenza vaccination (p = 0.015) for intervention arm patients who opened an eJournal compared to control arm patients, but no differences for any measure among patients who did not open an eJournal. CONCLUSIONS: Providing patients with HM reminders via a PHR may be effective in improving some elements of preventive care.

The digital divide in adoption and use of a personal health record.

BACKGROUND: Personal health records (PHRs) offer the potential to improve the patient experience and the quality of patient care. However, the "digital divide," the population-level gap in Internet and computer access, may prevent certain groups from accessing the PHR. METHODS: We conducted a cross-sectional analysis of a PHR within a northeastern health system. We compared adopters (ie, those activating a PHR account online) with nonadopters (ie, those who see a physician offering the PHR but do not activate an account). We further categorized adopters by intensity of PHR use, measured by number of log-ins and number of messages sent to physicians' practices. RESULTS: As of September 30, 2009, among 75,056 patients, 43% had adopted the PHR since 2002. Blacks and Hispanics were less likely to adopt the PHR compared with whites (odds ratio [OR], 0.50; 95% confidence interval [CI], 0.45-0.55; and 0.64; 0.57-0.73, respectively), and those with lower annual income were less likely to adopt the PHR than were those with higher income. Compared with nonadopters, adopters were more likely to have more than 2 comorbidities (OR, 1.27; 95% CI, 1.17-1.30). Use of an aggressive marketing strategy for PHR enrollment increased adoption nearly 3-fold (OR, 2.92; 95% CI, 1.58-5.40). Intensity of use was best predicted by increasing number of comorbidities, followed by race/ethnicity (whites more than blacks and Hispanics) and insurance status. We found no association between income and log-in frequency or secure messages sent. CONCLUSIONS: Despite increasing Internet availability, racial/ethnic minority patients adopted a PHR less frequently than white patients, and patients with the lowest annual income adopted a PHR less often than those with higher incomes. Among adopters, however, income did not have an effect on PHR use.

Single-parameter early warning criteria to predict life-threatening adverse events.

OBJECTIVES: Early warning criteria ("criteria"), used to activate rapid response system (RRS) teams, may improve patient outcomes by predicting life-threatening adverse events--urgent intensive care unit (ICU) transfers and cardiac arrests. METHODS: We conducted a case-control study on medicine patients in an academic medical center from May 2005 to June 2006. Controls were matched to RRS activation patients by admission date and unit. Rapid response system activation patients were then excluded from analysis. Chart reviews identified positive criteria on non-RRS patients. For controls, physiologic data were collected from admission until the day of matched RRS activations. Data were collected in patients with adverse events for the 8 hours preceding events. RESULTS: A total of 262 patients (2.8%) had 271 adverse events including 245 ICU transfers and 26 arrests. Positive criteria were found during 21.1% of control admissions (68/323). Positive criteria preceded 60.8% of the ICU transfers (158/260; 95% confidence interval, 54.8%-66.7%). However, 76.9% of patients with an arrest did not have previous positive criteria (20/26; 95% confidence interval, 60.7%-93.1%). Intensive care unit transfers with positive criteria were more likely to die than patients without criteria (35.4% versus 20.6%; odds ratio [OR], 2.1). Positive criteria most strongly associated with life-threatening adverse events were tachypnea (OR, 31.1) and 100% supplemental oxygen (OR, 13.7). CONCLUSIONS: The early warning conditions used to activate RRS teams were only fair predictors of acute deterioration, although early signs of respiratory failure during routine monitoring were strongly associated with future life-threatening adverse events. Improved respiratory monitoring may improve outcomes from RRS interventions.

Risks of complications by attending physicians after performing nighttime procedures.

CONTEXT: Few data exist on the relationships between experienced physicians' work hours and sleep, and patient safety. OBJECTIVE: To determine if sleep opportunities for attending surgeons and obstetricians/gynecologists are associated with the risk of complications. DESIGN, SETTING, AND PATIENTS: Matched retrospective cohort study of procedures performed from January 1999 through June 2008 by attending physicians (86 surgeons and 134 obstetricians/gynecologists) who had been in the hospital performing another procedure involving adult patients for at least part of the preceding night (12 am-6 am, postnighttime procedures). Sleep opportunity was calculated as the time between end of the overnight procedure and start of the first procedure the following day. Matched control procedures included as many as 5 procedures of the same type performed by the same physician on days without preceding overnight procedures. Complications were identified and classified by a blinded 3-step process that included administrative screening, medical record reviews, and clinician ratings. MAIN OUTCOME MEASURES: Rates of complications in postnighttime procedures as compared with controls; rates of complications in postnighttime procedures among physicians with more than 6-hour sleep opportunities vs those with sleep opportunities of 6 hours or less. RESULTS: A total of 919 surgical and 957 obstetrical postnighttime procedures were matched with 3552 and 3945 control procedures, respectively. Complications occurred in 101 postnighttime procedures (5.4%) and 365 control procedures (4.9%) (odds ratio, 1.09; 95% confidence interval [CI], 0.84-1.41). Complications occurred in 82 of 1317 postnighttime procedures with sleep opportunities of 6 hours or less (6.2%) vs 19 of 559 postnighttime procedures with sleep opportunities of more than 6 hours (3.4%) (odds ratio, 1.72; 95% CI, 1.02-2.89). Postnighttime procedures completed after working more than 12 hours (n = 958) compared with 12 hours or less (n = 918) had nonsignificantly higher complication rates (6.5% vs 4.3%; odds ratio, 1.47; 95% CI, 0.96-2.27). CONCLUSION: Overall, procedures performed the day after attending physicians worked overnight were not associated with significantly increased complication rates, although there was an increased rate of complications among postnighttime surgical procedures performed by physicians with sleep opportunities of less than 6 hours.

Survey analysis of patient experience using a practice-linked PHR for type 2 diabetes mellitus.

Patient experience was assessed by survey as part of a large, randomized controlled trial of a secure, practice-linked personal health record called Patient Gateway at Partners HealthCare in Boston, MA. The subjects were patients with Type 2 diabetes who prepared for their upcoming primary care visit using a previsit electronic journal. The journal generated a diabetes care plan using patient chart information and patient responses to questions in preparation for a scheduled office visit. Review of 37 surveys revealed that a diabetes care plan took 5-9 minutes (modal) to be created by the patient and helped many patients to feel more prepared for their visit (60%) and give more accurate information to their provider (53%). Study limitations included small numbers of survey participants and a bias toward white, better educated patients with better controlled diabetes. Nevertheless, the electronic journal is a promising tool for visit preparation and process improvement.

Effectiveness of health maintenance reminders provided directly to patients.

Provider-centric Electronic Health Records (EHRs) with clinical decision support have had a modest impact on improving quality care. However, they do not directly engage patients in promoting guideline adherence. Tethered Personal Health Records (PHRs) in contrast, can deliver decision support to both patients and providers. We conducted a randomized controlled trial to determine the effectiveness of providing seven screening reminders directly to patients via a PHR. Patients receiving the intervention were significantly more likely to receive Pap smears. No difference was seen for the other reminders.

Practice-linked online personal health records for type 2 diabetes mellitus: a randomized controlled trial.

BACKGROUND: Web-based personal health records (PHRs) have been advocated as a means to improve type 2 diabetes mellitus (DM) care. However, few Web-based systems are linked directly to the electronic medical record (EMR) used by physicians. METHODS: We randomized 11 primary care practices. Intervention practices received access to a DM-specific PHR that imported clinical and medications data, provided patient-tailored decision support, and enabled the patient to author a "Diabetes Care Plan" for electronic submission to their physician prior to upcoming appointments. Active control practices received a PHR to update and submit family history and health maintenance information. All patients attending these practices were encouraged to sign up for online access. RESULTS: We enrolled 244 patients with DM (37% of the eligible population with registered online access, 4% of the overall population of patients with DM). Study participants were younger (mean age, 56.1 years vs 60.3 years; P < .001) and lived in higher-income neighborhoods (median income, $53,784 vs $49,713; P < .001) but had similar baseline glycemic control compared with nonparticipants. More patients in the intervention arm had their DM treatment regimens adjusted (53% vs 15%; P < .001) compared with active controls. However, there were no significant differences in risk factor control between study arms after 1 year (P = .53). CONCLUSIONS: Previsit use of online PHR linked to the EMR increased rates of DM-related medication adjustment. Low rates of online patient account registration and good baseline control among participants limited the intervention's impact on overall risk factor control. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00251875.