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Seven Pillars Response to Patient Safety:

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Seven Pillars Response to Patient Safety:

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1-incident reporting
2-investigation while holding hospital fees and professional fees
3-early communication with family/patient
4-full disclosure, apology, and rapid remedy
5-system improvement
6-data tracking and evaluation
7-education and training
(Lambert et al., 2016)

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Improving patient safety and minimizing medical liability costs are important priorities for U.S. health care system reform (Kohn, Corrigan, and Donaldson 2000; Clinton and Obama 2006; Leape et al. 2009; Mello and Brennan 2009). It may be possible to achieve these goals by developing a culture of transparency, effective communication, and early resolution following adverse events caused by inappropriate care or other factors.

According to the U.S. Agency for Healthcare Research and Quality, communication and optimal resolution (i.e., CANDOR) refers to "a process that health care institutions and practitioners can use to respond in a timely, thorough, and just way when unexpected events cause patient harm" (Agency for Healthcare Research and Quality 2016). A CANDOR program generally includes an integrated set of processes for event identification, system activation, response and disclosure, investigation and analysis, resolution and remediation, and care for care providers.

These programs have demonstrated that effective communication with patients and families in the immediate aftermath of patient harm, regardless of the cause of that harm, can lead to organizational learning, improved surrogate measures of patient safety, and reduction in medical liability (Boothman et al. 2009; Kachalia et al. 2010; Helmchen, Richards, and McDonald 2011; Mello et al. 2014a, b; Agency for Healthcare Research and Quality 2016). In CANDOR programs, health systems and medical liability insurers encourage communication of unanticipated outcomes to patients while seeking early resolution that may include offering an apology, an explanation, a commitment to learn and improve, an admission of liability, and where appropriate, reimbursement or compensation (Mello et al. 2014a, b).

The following sections describe how the University of Illinois Hospital and Health Sciences System (UIH) responded to its own local medical liability crisis by adopting a version of a CANDOR program known as the Seven Pillars (McDonald et al. 2010). The first section describes the historical context, and the following section describes the intervention.

Catalyst for Change in Approach to Medical Liability

In 2001, a medical liability crisis erupted nationwide, leading to calls for transformation within the medical and legal communities (Thorpe 2004). Many regions across the United States faced rising medical malpractice premiums and large judgments. Insurance carriers began to exit the marketplace, further increasing upward pressure on malpractice premiums (Freudenheim 2001).

The UIH, located in a jurisdiction without limits personal injury liability payouts, Cook County, Illinois, was adversely affected by these events. In 2001, the excess insurance carrier that provided coverage to UIH for any loss greater than $3 million exited the marketplace. In 2002, excess carriers in Cook County would not provide financial coverage at any level less than $15 million per case. This caused a shortfall in the UIH self‐insurance fund, with a potential $12 million gap on every case involving major harm. The result was a massive increase in the required annual contribution to the University's self‐insurance fund that was needed to cover potential ongoing losses and to reduce the fund deficit of more than $30 million.

Responding to the Institute of Medicine's report of the large numbers of preventable patient deaths (Kohn, Corrigan, and Donaldson 2000) and the medical liability crisis in Illinois, UIH charged the office of the Chief Medical Officer with developing an alternative approach for managing the health system's patient safety, medical liability, and risk profile. The team explored programs from around the country, including those at the Lexington, KY Veterans Administration (Kraman and Hamm 1999) and the University of Michigan (Boothman et al. 2009). Further exploration into the impact of effective communication after unexpected outcomes on patient claims behavior was also conducted during this time (Hickson et al. 2002).

In September 2005, then Senators Clinton and Obama proposed the Medical Error Disclosure and Compensation (MEDiC) Act (Clinton and Obama 2006). Although it did not pass, the proposal brought the work of the University of Michigan to the forefront of the medical liability reform debate. As a result, UIH leadership granted permission to develop an alternative approach to patient safety and medical liability, and UIH initiated an intervention to increase transparency, early reporting and rapid investigation of unexpected adverse outcomes, learning and improvement, and full disclosure of inappropriate care with early resolution.

Development of the UIH Approach to CRP: The Seven Pillars

The UIH team conducted site visits with the early adopters of alternative dispute resolution and disclosure and early offer programs. The University of Michigan program (Boothman et al. 2009) inspired the model for the Seven Pillars approach (McDonald et al. 2010). A preliminary process model was developed and then revised based on feedback from patients and families who had been harmed by medical care. The Seven Pillars approach to responding to patient safety incidents was finalized after integrating this feedback (McDonald et al. 2010).

The approach consists of seven processes that are deployed in the wake of a patient harm event. The seven components are as follows: (1) incident reporting; (2) investigation while holding hospital bills and professional fees; (3) early communication with patient/family; (4) full disclosure, apology, and rapid remedy if appropriate; (5) system improvement; (6) data tracking and evaluation; and (7) education and training (McDonald et al. 2010). The driving hypothesis is that early and consistent communication with patients and/or family members after an adverse event will serve to maintain trust between the patient and the health system and yield improved learning, patient outcomes, and reduced medical liability (Hickson et al. 2007; Hickson and Entman 2008).

The Seven Pillars intervention includes incident reporting that triggers a rapid response system. The rapid response system seeks, concurrently, to provide support to the patient and family, to investigate the event, and to provide ongoing support to the caregivers (Scott et al. 2010) involved in the event. Communication with patients and families occurs within 15-30 minutes after notification of the event and continues throughout the investigation period. Event investigations are conducted rapidly.

Once consensus has been reached as to the facts, including potential factors leading to the event and possible breaches of the standard of care, a patient or family meeting is convened to discuss the findings of the event analysis, including, in appropriate cases, the admission of mistakes along with an apology and a discussion of appropriate resolution, including the promise to waive or reimburse patients for all hospital and professional fees and the costs of future care associated with the preventable harm.

Information collected from the time of the report through the event analysis, full explanation, apology, and resolution is used to improve hospital processes and embedded into ongoing health professional educational efforts, thereby linking the outcomes into a cycle of continuous improvement. Financial resolution recommendations are developed outside the control of the Department of Safety and Risk Management and within the context of a medical malpractice action group with broad stakeholder engagement, including clinical departments, patient safety, claims management, legal, business, and finance. The University of Illinois Board of Trustees makes the final decision regarding financial resolution.

Licensed professionals involved in cases associated with financial resolution were reported to the National Practitioner Data Bank and the Illinois Department of Financial and Professional Regulation, consistent with federal and state regulations. As part of the peer support or "care‐for‐the‐caregiver" program (Scott et al. 2009, 2010), attempts were made to support professionals throughout this regulatory response process.

The Seven Pillars approach was presented to and approved by the UIH leadership, including the Office of Legal Counsel and the Office of Business and Financial Services in March of 2006. The program was presented to and approved by the College of Medicine's Medical Service Plan [MSP], representing the several hundred physicians practicing at UIH. The MSP agreed to provide financial incentives to departments participating in the program.

Following full approval by the administrative and clinical arms of the health system, communication skills training for stakeholders was conducted by Gerald Hickson and James Pichert from the Vanderbilt Center for Patient and Professional Advocacy. Following the communication training, the program went live on April 1, 2006. The first major case, involving a financial resolution of more than $1 million, took place in June 2006. Program updates, including process improvements following event analyses, were presented quarterly to University leadership and the Executive Committee of the MSP.

Two processes encouraged physician incident reporting. For attending physicians, the departmental share of yearly malpractice premium was based upon a formula that included a substantial financial penalty (>$10,000) for patient adverse events that resulted in a claim without a prior report having been received by the Department of Safety and Risk Management. In addition, graduate medical education program directors established the expectation that resident physicians would submit incident reports. This resulted in a substantial increase in reporting, communication with patients and families, and learning from patient safety events (Jericho et al. 2010).

The Department of Safety and Risk Management began to ensure proper feedback to reporters, and it frequently took visible actions in response to reports. The program also launched a care‐for‐the‐caregiver program, whereby clinicians discovered they would reliably receive peer support after unexpected outcomes, and this seemed to increase the trust between the clinicians and the Department of Safety and Risk Management.

Study Objective

The purpose of this investigation was to determine whether a CANDOR intervention based on the Seven Pillars approach to adverse events was associated with measurable improvements on a targeted set of safety, communication process, and liability outcomes.

Hypotheses

We expected the intervention to be associated with an increase in incident reports, patient communication consults, and event analyses, and a decrease in claims, lawsuits, legal fees and expenses, settlement amounts, total liability costs, time to closure, and self‐insurance funding needs.

Study Data and Methods

Design

We used an interrupted time series design within one organization (UIH), using itself as a historical control (Cook and Campbell 1979a, b; Gillings, Makuc, and Siegel 1981; Weinberg et al. 2001; Wagner et al. 2002). In this type of design, outcomes are repeatedly measured over time, and the analysis assesses changes in the outcome before and after an "interruption." In some cases, the interruption in the time series is the result of natural experiments or policy changes (Wagner et al. 2002) and in others, like ours, it is a planned intervention (Weinberg et al. 2001).

Analyses of interrupted time series (ITS) designs focus on both the absolute level of an outcome at a given point in time (typically immediately before and after the intervention) and on the trend in the outcome during a specific period before and after the intervention. We analyzed outcomes for 5 years prior and 7 years after our intervention. The design allowed us to assess the time course of the effect of our interventions on each outcome (immediate or delayed), the persistence of these effects, their magnitude, and their direction.

Setting

UIH consists of the University of Illinois Hospital, its medical center, and more than 20 affiliated outpatient locations. Nearly all of the physicians who work with UIH are University employees and are provided liability coverage from the same University self‐insurance plan.

Data

We obtained study data by querying databases maintained by the Department of Safety and Risk Management and the Department of Administrative Services at UIH. The main dataset was extracted from the risk module of the Midas incident reporting system, for calendar years 2000-2013, although not every outcome spans that entire time period. The study was approved by the Institutional Review Board at UIC.

Outcome Definitions

We analyzed quarterly (and sometimes annual) trends in incident reports, patient communication consults, event analyses, claims, lawsuits, legal fees and expenses, settlement amounts, total liability costs, time to closure, and self‐insurance funding needs and balances. Some outcomes were analyzed on the basis of both cumulative quarterly total and per claim quarterly means (defined as the cumulative total divided by the total number of claims).

Incident reports are reports received by the Safety and Risk Management Department describing actual harm, near misses, and unsafe conditions. A patient communication consult is a support service available to facilitate health care providers in effective and honest communication about adverse events and outcomes to patients and families. When a patient communication consult is requested, a trained member of the risk management or medical staff comes to the aid of a front‐line clinician to assist with difficult conversations with a patient or family. An event analysis is the evaluation of all factors leading to an event. In the Seven Pillars program, individual performance is evaluated by peers in order to enhance the quality of their work or to determine the reasonableness of care. Reason's unsafe acts algorithm was employed as part of a structured approach to determination of individual accountability (Reason 1997).

We defined a claim as a request, either written or verbal, for financial compensation related to a patient harm event. We did not count as claims: potentially compensable events, tendered lawsuits, or when medical staff were named as respondents in discovery.

Legal fees were defined as payments to attorneys or para‐professionals for hourly work. Legal expenses were defined as all legal costs excluding legal fees, such as filing fees, expert witness expenses, travel, phone, postage, copying, etc. Settlement amount was defined as the total amount paid to settle a claim. Total liability costs were defined as the sum of settlements, legal fees, and legal expenses. All dollar values were adjusted for inflation to be equivalent to 2013 dollars using the annual Consumer Price Index (Urban) from the U.S. Department of Labor (Bureau of Labor Statistics: U.S. Department of Labor 2014).

Self‐insurance is a method of managing risk by setting aside a pool of money to be used if an unexpected loss occurs. The self‐insurance fund is a fund controlled and managed by the University in which money is held in reserve for paying estimated and ongoing indemnity and legal costs.

Self‐insurance balance is the difference between the current dollars in the fund and all current estimated expenses for all current and actual claims and lawsuits and those that may arise in the future related to any care that has been rendered up until to the date of calculating the balance.

Self‐insurance funding needed is the money needed by the self‐insurance fund to cover the estimated payouts for the coming year. At UIH, not only did it need to cover the estimated payouts for each coming year but also to begin to close the deficit in the fund that occurred when St. Paul Insurance left the market.

Outcomes pertaining to lawsuits, legal fees and expenses, and settlement amounts were based on closed claims only. There is a 2‐year statute of limitations in Illinois. Observational data ended December 31, 2013, so counts of lawsuits pertained only to incidents occurring before December 31, 2011.

To account for variation in the volume or intensity of clinical services being provided at UIH, we normalized outcomes by dividing by number of patient encounters in the inpatient and outpatient settings. Results are reported in terms of outcomes per 100,000 encounters. We considered alternative normalization methods, such as normalizing by relative value units, but these measures changed significantly over our observation period. In a similar analysis, Kachalia et al. ( 2010) normalized by 100,000 encounters.

Analysis Plan

We computed descriptive statistics for the main dataset of risk management incidents. We then compared the pre‐ and postintervention means for each outcome using nonparametric tests (the normal approximation to the Mann-Whitney U test). Next, we analyzed ITS data using segmented linear regression with terms for time (representing the preintervention trend), intervention (representing the change in level of the outcome from immediately prior to immediately after the intervention), and time after intervention (representing the difference in time trend between pre‐ and postintervention periods (Cook and Campbell 1979a; Wagner et al. 2002). We used SAS 9.4 for all statistical analyses. We used PROC GENMOD for dollar outcomes and PROC AUTOREG for other outcomes.

Results

Descriptive Statistics

The main dataset consisted of 764 claims‐related incidents, 127 of which involved UIH physicians working off‐site at other hospitals. For the analyses below, we only included the 637 on‐site claims‐related incidents, of which 257 were nonlawsuit claims, 207 were lawsuits, 156 were potentially compensable events, and 17 were cases where UIC was named as a respondent in discovery (see Table ). In the analysis and discussion below, lawsuits and nonlawsuit claims are referred to collectively as "claims." As of December 31, 2013, 367 of the 637 total claims‐related incidents were closed claims (167 lawsuits and 200 nonlawsuit claims).

Table 1

Comparison of Preintervention and Postintervention Means

Comparing the entire preintervention period to the entire postintervention period, we observed significant postintervention changes in mean quarterly number of incident reports (pre = 394.8, post = 837.6, z = −5.1, p < .0001), patient communication consults (pre = 0.2, post = 9.6, z = −6.5, p < .0001), event analyses (pre = 0.7, post = 19.6, z = −6.4, p < .0001), claims (pre = 8.8, post = 5.1, z = 3.4, p = .0004), and lawsuits (pre = 4.5, post = 2.4, z = −3.7, p = .0002). We also observed significant decreases in mean quarterly legal fees and expenses (pre = $554,221, post = $98,017, z = −4.2, p < .0001), settlement amounts (pre = $3,379,561, post = $1,670,979, z = −2.8, p = .005), and total liability costs (pre = $3,933,781, post = $1,768,996, z = −3.3, p = .001) (see Table ).

Table 2

In addition to examining these outcomes on a cumulative quarterly basis, we examined many on a per claim basis for each quarter (e.g., mean settlement amount per claim per quarter). Quarterly lawsuits per claim decreased significantly (0.58 vs. 0.39, z = −2.0, p < .05), as did legal fees and expenses per claim, settlement amounts per claim, and total liability costs per claim (see Table for details). Mean time to closure decreased significantly from 4.0 to 2.4 years ( z = −4.0, p < .0001).

In the pre‐post comparison of means, total funding needed for self‐insurance and the self‐insurance trust fund balance did not change significantly.

Analysis of Segmented Regression

Table summarizes the results of segmented regression analyses of the study outcomes.

Table 3

Figures , , , , , , , illustrate the magnitude, time course, and direction of the changes in process and outcome measures during the pre‐ and postintervention period for selected outcomes. Additional figures are available in an online Appendix. Segmented regression analysis of these time series data tested the overall time trend (across the entire observation period), the instantaneous effect of the intervention (i.e., the difference between the quarter immediately prior to and immediately after the intervention), and the effect of the intervention on the time trend for each outcome. The last of these measures, testing the change in trend pre versus post, will typically be of greatest interest.

Incident Reports

Figure shows a significant decrease in the number of incident reports ( b = −132.20, p < .0001) immediately after the rollout of the new reporting system, followed by a significant and long‐lasting positive change in the reporting trend, compared to the preintervention period ( b = 35.84, p = .004). Incident reports increased by roughly 36 reports per quarter for the 7‐year postintervention observation period.

Event Analyses

Figure shows a significant increase in the postintervention time trend for quarterly number of event analyses postintervention ( b = 0.78, p = .03), with no change in the immediate pre‐post comparison and no significant overall effect of time.

Claims

Figure shows a significant declining trend in quarterly claims during the time after the intervention, compared to the preintervention trend ( b = −0.55, p < .0001).

Lawsuits

The number of lawsuits per quarter decreased significantly during the entire time period ( b = −0.14, p = .003), with no significant effect of the intervention (see Figure ).

Settlement Costs and Total Liability Costs

Quarterly settlement costs decreased significantly during the postintervention period ( b = −0.24, p = .004), as did total liability costs, that is, the sum of settlement costs and legal fees and expenses ( b = −0.23, p = .0004, see Figure ).

Quarterly Lawsuits per Claim

The number of lawsuits per claim declined significantly over the entire observation period ( b = −0.03, p < .0001). Lawsuits per claim increased significantly immediately after implementation of the program ( p = .001), and there was a small but significant decrease in the slope of the time trend after the intervention ( b = 0.02, p = .04), meaning that the rate of decline in lawsuits per claim slowed during the postimplementation period.

Quarterly Legal Fees and Expenses per Claim

On a per claim basis, legal fees and expenses declined over the entire observation period ( b = −0.10, p = .02) and rose slightly immediately after the intervention ( b = 3.30, p = .006). Compared to the rate of decline during the preintervention period, the rate of decline increased in the postintervention period ( b = −0.28, p = .002). That is, per claim legal fees and expenses declined more rapidly in the postintervention period than they did in the preintervention period.

Self‐Insurance Funding Needed

The total amount of funding needed for the self‐insurance fund showed a significantly increasing trend across the entire observation period ( b = 3,939,070, p = .0005, see Figure ). There was no change immediately after the intervention. The slope of the postintervention trend was significantly different (and in the opposite direction) from the preintervention trend ( b = −5,912,737, p < .0001). Although self‐insurance funding needs increased during the preintervention period, they decreased during the postintervention period (see Figure ).

Self‐Insurance Fund Balance

Figure shows that that the balance in the self‐insurance fund was negative and decreasing during the preintervention period. In the postintervention period, the trend reversed, a difference that was statistically significant ( b = 13,185,796, p = .002).

Time to Closure

Figure shows that time to closure for closed claims decreased steadily over the entire observation period ( b = −0.14, p < .0001). There was a significant increase in time to closure immediately after the intervention ( b = 1.65, p = .002). Time to closure continued to decline during the postintervention period, but the rate of decline did not differ from the preintervention period ( b = 0.02, p = .62).

Discussion

The purpose of this investigation was to determine whether a communication and optimal resolution (i.e., CANDOR) approach to adverse events would be associated with measurable changes in medical liability processes and outcomes. The data show an association between the intervention and the majority of outcomes. In the segmented regression analyses of ITS data, the observed pre‐ versus post‐changes in time trends were both practically and statistically significant and in the predicted and desirable direction. Incident reports, event analyses, and patient communication consults became more common. Claims were less common, settlement costs, and legal fees and expenses went down, both overall and on a per claims basis. Annual contributions to the self‐insurance fund declined dramatically, and the self‐insurance fund moved from a $30 million deficit to a $40 million surplus.

Unexpected Findings

Two results were not consistent with our hypotheses about the beneficial effects of the intervention. These concerned lawsuits and time to closure, both of which went down dramatically over the period of observation, but the postintervention rates of decline were not greater during than those observed in the preintervention period, so the declines could not be attributed to the intervention.

Lawsuits per Claim

We expected that rate of lawsuits per claim would decrease after the intervention. As it happened, there was a brief postimplementation increase in lawsuits per claim, and a slight reduction in the rate of decrease in lawsuits per claim over the entire postintervention period. These were unexpected findings, and we can only speculate about their cause. From inspection of Figures and , it is clear that, in the year or two after the intervention began, the quarterly number of claims declined at a more rapid rate than the quarterly number of lawsuits. As a result, the ratio of lawsuits to claims increased during that period.

We surmise that this occurred because the program was initially more effective at preventing claims than preventing lawsuits. This was likely due to the use of the Patient Communication Consult Service (PCCS), which engaged with patients and families soon after adverse events, provided information, explanation, and sometimes remedies, and thereby eliminated the motivation for filing a claim (Golann 2011). The PCCS was designed to prevent patients and families from needing to file a claim in order to get answers to their questions about the adverse event. One factor impacting lawsuit filing in Illinois may have been the lack of a prelitigation "cooling off" period, as provided in Michigan and Massachusetts, during which patient and family concerns can be addressed and resolved prior to litigation.

In spite of the brief, postintervention increase in lawsuits per claim, the number was still significantly lower in the postintervention period than in the preperiod (0.58 vs. 0.39, p < .05), as were claims, legal fees and expenses, settlement costs, and total liability costs.

Time to Closure

We hypothesized that time to closure would go down after the intervention. Instead, it increased slightly immediately after the intervention, before returning to its preintervention declining trend. But as with the unexpected increase in lawsuits per claim, it is important to note that the time to closure was still significantly lower in the postintervention period than it was in the preintervention period (4.0 years pre vs. 2.4 years post, p < .0001). So although there was a brief postintervention increase in time to closure, the intervention did not substantially alter the longer term trend toward shorter times to closure. One possible explanation is that Illinois Tort Reform, passed in late 2005, prompted a delay in closure while plaintiffs awaited an expected determination of the unconstitutionality of the reform that ultimately occurred at the circuit court level in November, 2007 (Circuit Court of Cook County 2007) and was affirmed by the Illinois Supreme Court. This might explain the initial increase in time to closure in the early postintervention period.

In spite of dire predictions to the contrary (Studdert et al. 2007), both the current findings and those published previously (Kachalia et al. 2010) indicate that telling the truth to patients and families and offering early resolution after adverse events does not produce any significant negative liability consequences.

Overall, when viewed in terms of both the absolute level and trends in important outcomes, the Seven Pillars CANDOR intervention achieved its main goals.

Limitations

This investigation was carried out in only one health system, a self‐insured, academic medical center with an employed medical staff. Results observed may or may not be generalizable to systems with open medical staffs of physicians insured by multiple providers. The current study lacked a direct measure of patient harm, making it impossible to determine whether the observed decline in malpractice claims was due to fewer patients being harmed or to adverse events being resolved without recourse to claims. The study was not a randomized trial. However, it did use an ITS design, which is generally regarded as a strong, nonrandomized design. The ITS makes it possible to distinguish between changes attributable to secular trends that precede the intervention (e.g., in this study, the decline in lawsuits and in time to settlement), and changes that are attributable to the intervention because they coincide with the intervention and have no other apparent explanation. The design would have been even stronger had we had one or more control health systems, enabling us to do a difference‐in‐differences analysis, or if we had been able to identify instrumental variables to control for unmeasured confounders.

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The "Seven Pillars" Response to Patient Safety Incidents: Effects on Medical Liability Processes and Outcomes

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Improving patient safety and minimizing medical liability costs are important priorities for U.S. health care system reform (Kohn, Corrigan, and Donaldson 2000; Clinton and Obama 2006; Leape et al. 2009; Mello and Brennan 2009). It may be possible to achieve these goals by developing a culture of transparency, effective communication, and early resolution following adverse events caused by inappropriate care or other factors.

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How to Encourage Adverse Event Reporting

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EHR Usability and Uninteded Health Issues

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Electronic Health Records: Usability and Unintended Safety Issues

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Hi, Nice to have you here with us today.

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This padlet presentation focuses on informatics to promote patient safety and quality outcomes.
The best way to improve safety and outcomes is to recognize processes that need improvement and are dangerous to an individual or a group of individuals.
The primary way to identify these dangerous processes is through data collection from incident reporting.
With informatics these reports can be transcribed into useful data that can assist in improving patient safety.
As informatics progresses this system of data analysis will and has become faster and more efficient, providing the most relevant information.
The most common reportable event in a health setting is an error in medication administration.
With todays advancements in health care using informatics and technology patient safety is increasing and the future is bright.

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Have you ever had to report or witnesses an adverse event? How do you think technology could have prevented this? Did it cause it?

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Have you ever had to report or witnesses an adverse event? How do you think technology could have prevented this? Did it cause it?

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(If not use a near miss, we've all had them!)

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Where do you hope to see improvements in patient and quality outcomes in the future? How could technology/informatics help? 

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Where do you hope to see improvements in patient and quality outcomes in the future? How could technology/informatics help? 

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Pardis Sabeti and Christian Happi: A virus detection network to stop the next pandemic

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How can we stop the next pandemic before it starts? Disease researchers Pardis Sabeti and Christian Happi introduce Sentinel, an early warning system that detects and tracks viral threats in real time -- and could help stop them before they spread.

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Daniel Kraft: Medicine's future? There's an app for that

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Daniel Kraft offers a fast-paced look at the next few years of innovations in medicine, powered by new tools, tests and apps that bring diagnostic information right to the patient's bedside.

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Telehealth allows for rural hospitals to have access to specialists they may not normally have. This provides physicians to consult with specialties when needed to improve patient outcomes. Such as nephrology, cardiology, neurologist, etc. This can be especially useful in emergency situations such as suspected stroke.

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Telehealth Use in Rural Healthcare Introduction - Rural Health Information Hub

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Provides resources and answers frequently asked questions related to telehealth in rural areas. Covers some challenges related to providing telehealth in rural communities, and the necessary equipment and staffing to implement telehealth services.

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I recently read a post highlighting an innovative and evidence-based solution to a consistently overlooked (but important) issue - hand hygiene in health care settings. Maureen Banks shared how the use of sensing technology improved hand hygiene compliance and reduced C. Difficile infection rates by 75% at her post-acute care hospital. In facilities like Banks' hospital, where patients experience a longer average length of stay, proper hygiene infrastructure and practices are especially important in reducing infections.

The World Health Organization (WHO)'s guidelines on hand hygiene push for new innovations and technologies to be developed to better measure hand hygiene practices. As highlighted by Banks, sensing technology is an emerging evidence-based strategy to measure and promote hand hygiene. In this post, I focus on evidence showing sensing technology as a reliable (perhaps superior) method for measuring and guaranteeing hand hygiene compliance.

What is hygiene-specific sensing technology?

Hygiene-specific sensing technology is designed to ensure health care workers perform hand hygiene during WHO's Five Moments for Hand Hygiene (critical moments of care). It increases accountability, provides a monitoring system, and creates an overall sense of ownership among health care workers.

The technology records and promotes hand hygiene compliance ( Marra and Edmond, 2014) using a special badge (a variation pictured at right) with colored lights to indicate hand hygiene status - green means hand hygiene has been performed, yellow is a reminder to clean hands, and red means hand hygiene should occur immediately.

The image below provides an illustration of how that works. A sensor in the badge automatically communicates with transmitters at key points in a patient's room (e.g., the room entrance/exit and patient bedside), detecting when there is a hand hygiene opportunity.

Health care workers have up to 60 seconds to confirm that their hands have been cleaned with alcohol-based handrub by cupping their hand over the badge. Sensors in the badge detect alcohol vapors and determine whether there is enough alcohol on their hands to indicate good hand hygiene. If yes, the light turns green. If no (or if the worker doesn't cup their hands over the badge at all - i.e., didn't clean their hands), the badge displays a yellow or red light to indicate a missed hand hygiene opportunity. This yellow or red light notifies patients and other health care workers of the worker's hand hygiene status. Often, depending on the variation in the sensing technology format, this visual nudge may also be accompanied by a vibration or an audible ping nudge as well.

Monitoring and guaranteeing hand hygiene compliance with sensing technology

Sensing technology has been proven to increase hand hygiene compliance rates. A study by Edmond et al. (2010) [gated] used four weeks of direct observation (the gold standard of hand hygiene measurement), followed by a two-week evaluation of a sensor technology system to assess the impact of sensing technology for monitoring hand hygiene compliance.

Sensing technology has been proven to increase hand hygiene compliance rates.

In the first study phase, Edmond et al. observed 1,646 hand hygiene opportunities through direct observation with a compliance rate of 66%. In the second phase, the researchers electronically monitored 6,831 opportunities using sensing technology with an overall compliance rate of 93%. Segmented regression analysis showed a 23% increase in measured hand hygiene compliance immediately following the implementation of sensing technology. The captured data on hand hygiene moments improved the ability of health care staff and their supervisors to recognize current gaps in hand hygiene, holding health care workers more accountable to clean their hands during critical moments of care. This study, like many others, found that the use of sensing technology for hand hygiene monitoring can result in improved compliance rates.

Direct observation has long been considered the gold standard for measuring hand hygiene compliance because it provides the most information. However, it tends to be labor intensive and costly. Direct observation also has limitations with validity, including reliability, sampling bias, and concerns regarding patient privacy. Sensing technology, on the other hand (pun intended), may be a superior alternative to direct observation that addresses these limitations.

For example, Grenado-Villar et al. (2011) explored the technology's reliability in tracking the number of hand hygiene opportunities during critical moments of care. In their presentation at the 2011 Society of Healthcare Epidemiology of America Meeting, Grenado-Vilar et al. reported 100% accuracy of the sensing technology system during their study period. In the study, the researchers examined a monitoring system, which included a bed monitor, electronic badge, and handwashing station that recorded hand hygiene opportunities. They found correlations between the use of sensing technology and hand hygiene behavior.

...sensing technology can provide real-time analysis and feedback on hand hygiene activities.

According to Grenado-Villar et al.'s findings, the use of sensing technology (compared to direct observation) is a more comprehensive way to measure the proportion of hand hygiene opportunities taken. While there are some limitations to sensing technology, such as lack of context-specific data ( Boyce, 2015), sensing technology can provide real-time analysis and feedback on hand hygiene activities.

Further research on sensing technology is still needed. Most studies are limited to short study periods and some have failed to establish the badge's sensitivity (e.g., measures truly clean hands) and specificity (e.g., measures truly unclean hands) of compliance rates, which is an important measure of accuracy. Nonetheless, the most interesting aspect of sensing technology for me is its ability to motivate health care workers' decisions. The use of visual nudges (colored lights) on badges provides an accountability mechanism among peers and patients and provides a personal reminder to health care workers to clean their hands. Rather than a generalized handwashing sign, sensing technology is individualized to the health care worker, providing a color-coded image of their own actions.

What does this mean?

With an end goal of improving patient outcomes, the evidence around sensing technology points to an innovative way to measure and improve hand hygiene practice among health care staff.

The adoption of sensing technology yields impacts for both staff members and their patients. Multiple studies, like Venkatesh et al. (2008) and Song et al. (2013), have shown a reduction in health care-associated infections after the implementation of a hand hygiene-specific initiative - like the case of Maureen Banks.

With an end goal of improving patient outcomes, the evidence around sensing technology points to an innovative way to measure and improve hand hygiene practice among health care staff. Ultimately, the power of accurate and centralized data paired with the immediate reminders from their badges keeps health care workers more accountable for their hand hygiene practices, allowing them to better connect their own hand hygiene behaviors with their role in preventing infections and providing safe care.

Photo credit: Barry Westhead/TRI

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Sensing technology: A superior alternative for improving hand hygiene compliance in health care settings? | R&E Search for Evidence

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I recently read a post highlighting an innovative and evidence-based solution to a consistently overlooked (but important) issue - hand hygiene in health care settings. Maureen Banks shared how the use of sensing technology improved hand hygiene compliance and reduced C. Difficile infection rates by 75% at her post-acute care hospital.

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McGonigle, D., & Mastrian, K. G. (2018). Chapter 15 Informatics Tools to Promote Patient Safety and Quality Outcomes. In Nursing informatics and the foundation of knowledge (pp. 293-321). Burlington, MA: Jones & Bartlett Learning.

Agrawal, A. (2009, June). Medication errors: Prevention using information technology systems. Retrieved September 20, 2020, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2723209/

CDS - Clinical Decision Support. (n.d.). Retrieved September 20, 2020, from https://ecqi.healthit.gov/cds

Clinical Decision Support: More Than Just "Alerts" Tipsheet. (2014). Retrieved 2020, from https://www.cms.gov/eHealth/downloads/ClinicalDecisionSupport_AlertsTipsheet.pdf

Lambert, B., Centomani, N., Smith, K., Helmchen, L., Bhaumik, D., Jalundhwala, Y., & McDonald, T. (2016, December). The "Seven Pillars" Response to Patient Safety Incidents: Effects on Medical Liability Processes and Outcomes. Retrieved September 20, 2020, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5134341/

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In most hospitals their EHR has some level of a CDSS. When most people think of clinical support systems they think of alerts and suggestions. The Centers for Medicare & Medicaid Services (CMS) defines CDS as "health IT functionality that provides persons involved in care processes with general and person-specific information, intelligently filtered and organized, at appropriate times, to enhance health and health care.” (2014)

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The most common error associated with adverse events in a health care setting. The following technological advances assist in reducing medication errors.
-computerized physician order entry
-automated dispensing of medication
-smart pumps
-bar-coded medication administration
-alerts and reminders via CDSS (clinical decision support)
-health information exchange
(McGonigle, Dee, Mastrian, Kathleen, 2018 pp. 301-306)

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-alerts and reminders
-relevant references
-diagnostic support
-templates for documentation
-individual patient reports and summaries
-order-sets triggered by conditions, labs, or diagnosis
(eCQI Resource Center, 2014.)

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Technologies assist in ensuring the five rights of medication and reduce errors.
-right patient
-right time and frequency
-right dose
-right route
-right drug
(Agrawal, 2009)

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Abstract

1. Given the high frequency of medication errors with resultant patient harm and cost, their prevention is a worldwide priority for health systems.

2. Systems that use information technology (IT), such as computerized physician order entry, automated dispensing, barcode medication administration, electronic medication reconciliation, and personal health records, are vital components of strategies to prevent medication errors, and a growing body of evidence calls for their widespread implementation.

3. However, important barriers, such as the high costs of such systems, must be addressed through economic incentives and government policies.

4. This paper provides a review of the current state of IT systems in preventing medication errors.

Keywords: CPOE, decision support, electronic health record, health information technology, medication errors, patient safety

A substantial body of evidence from international literature points to the risks posed by medication errors and the resulting preventable adverse drug effects. In the USA, medication errors are estimated to harm at least 1.5 million patients per year, with about 400 000 preventable adverse events [ 1]. In Australian hospitals about 1% of all patients suffer an adverse event as a result of a medication error [ 2]. In the UK, of 1000 consecutive claims reported to the Medical Protection Society from 1 July 1996, 193 were associated with prescribing and medications [ 3]. Medication errors are also costly - to healthcare systems, to patients and their families, and to clinicians [ 4, 5].

Prevention of medication errors has therefore become a high priority worldwide. There is mounting evidence that systems that use information technology (IT), such as computerized physician order entry, automated dispensing cabinets, bedside bar-coded medication administration, and electronic medication reconciliation, are key components of strategies to prevent medication errors. IT systems have also been reported to have the potential to save up to $88 billion over 10 years in costs in the USA, with increasing adoption [ 6, 7]. Hospitals with automated notes and records, order entry, and clinical decision support have fewer complications, lower mortality rates, and lower costs [ 8, 9].

Here I review the current state of various IT systems in preventing medication errors.

The medication management process

Medication management is a complex, multifaceted operation involving multiple people and numerous steps. describes the various steps in a typical medical management process, the estimated error rate for each step, and the 'true' error rate based on the likelihood that the error will be intercepted [ 10- 12]. Since medication administration is the last step in the process, the intercept rate is understandably very low. The last column in lists the IT systems that target each step in the process. Many institutions are now implementing a 'closed-loop' system, i.e. end-to-end electronic medication management with seamless flow of information along the process.

Table 1

Information technology systems in medication management

Clinical decision making is a complex process that depends on human ability to provide undivided attention and to memorize, recall, and synthesize huge amounts of data - all vulnerable areas. IT systems can improve access to pieces of information, organize them, and identify links between them. Clinicians often 'know' the information (such as a patient's allergies, a drug recall warning, or a drug-drug interaction) but forget to consider it at the time of prescribing. IT systems are effective in bridging this 'knowing-doing' gap [ 13], by presenting the relevant information to the clinician at the time of decision making.

Computerized physician order entry with decision support

Since most errors occur at the prescribing step, computerized physician order entry (CPOE) with patient-specific decision support is a potentially powerful intervention for improving patient safety. Common prescribing errors include using the wrong drug or dosage form, incorrect dose calculation, not checking for allergies, and failure to adjust dosages in patients with renal or hepatic dysfunction [ 14]. CPOE systems work by (i) making sure that the order is legible and complete, including all necessary information, such as dose, route, and dosage form; (ii) checking for problems such as drug allergies and drug-drug interactions; (iii) providing dosage adjustment calculations based on clinical features such as weight or renal function; (iv) checking for appropriate baseline laboratory results, such as platelet count and international normalized ratio for patients receiving anticoagulants; (v) computing drug-laboratory interactions, such as alerting the prescriber to a low potassium concentration when digoxin is being prescribed; and (vi) updating the prescriber with the latest drug information, such as the need to avoid rofecoxib after it had been withdrawn by the manufacturer.

shows a variety of decision support alerts embedded in an intravenous heparin prescribing screen.

Of the various systems used in the medication process, CPOE systems have the largest impact on reducing medication errors, with reported error reductions of 55-83% [ 15, 16]. Other studies have shown impressive reductions in antibacterial drug-related adverse events [ 17], reduced lengths of stay [ 18], and improved dosaging of psychoactive drugs in elderly people [ 19].

Pharmacy dispensing systems

With the high volume of medications dispensed in hospitals, dispensing errors are common, often go undetected, and can have serious consequences [ 20]. For example, it has been estimated that >44 000 errors occur annually in a 735-bed hospital dispensing 6 million doses per year [ 21]. A variety of systems, such as drug-dispensing robots and automated dispensing cabinets, reduce dispensing errors by packaging, dispensing, and recognizing medications using bar codes [ 22, 23]. In a recent evaluation of the impact of bar-coding drugs in pharmacy and checking them before they are sent to patient care units, the dispensing error rate fell by 31% after bar-code implementation in pharmacy, and the potential rate of adverse events fell by 63% [ 24].

Bar-coded medication administration

Bar-coded medication administration (BCMA) systems require that the nurse who administers the medication at the bedside should scan the patient's identification bracelet and the unit dose of the medication being administered. The system alerts the nurse to any mismatch of patient identity or of the name, dose, or route of administration of the medication. BCMA reduces medication errors by ensuring the five 'rights' of medication administration: the right patient, drug, dose, route, and time. BCMA systems reportedly produce 54-87% reductions in errors during administration of medications [ 25]. In a London teaching hospital, implementation of a 'closed-loop' system including CPOE and BCMA reduced prescribing and medication administration errors [ 26]. BCMA is reviewed in more detail elsewhere in this special issue [ 27].

Electronic medication reconciliation

With growing recognition that many inpatient medication errors occur at care transition points, reconciliation of medication lists during admission, transfer and discharge is an important step in improving safety. CPOE systems are effective in reducing errors during prescribing; however, a CPOE system cannot detect an error if the physician does not remember to prescribe a medication that the patient was taking at home. There is preliminary evidence that electronic medication reconciliation systems are quite effective in reducing such unintended discrepancies [ 28].

Personal health records

Personal health records (PHR) engage and empower patients in their own medical care. An individual can maintain a paper-based record, but in the current context a PHR is defined as 'a set of computer-based tools that allow people to access and coordinate their lifelong health information and make appropriate parts of it available to those who need it'[ 29]. Implementation of such a record may consist of a 'stand-alone' website for patients to enter their medical data, or a physician/hospital-hosted patient portal, giving patients access to their electronic health record (EHR), or an employer/payer portal giving patients access to claims data.

Online medication lists are an essential component of most PHRs. Patients can access medication information from multiple providers, reconcile them, update them, and share them with their physician. A patient-initiated medication reconciliation system is likely to be more accurate, as patients know not only what has been prescribed but what they are actually taking. Furthermore, advanced PHRs provide decision support tools, such as checking for drug allergies and drug-drug interactions and allowing patients to anticipate potential medication errors and alert physicians to them. Since this is an emerging area in health IT, there is no hard evidence as yet; however, there is much optimism about its potential effectiveness in reducing medication errors [ 30].

Decision support to reduce medication errors of omission

We often focus on the role of IT in preventing errors of commission (e.g. the wrong dose, route, medication). However, medication errors of omission can have an equally significant impact when evidence of the benefit of the medication is clear [ 31]. For example, for patients with heart failure due to left ventricular dysfunction, prescription of an angiotensin-converting enzyme inhibitor or angiotensin receptor antagonist is the most useful measure in reducing mortality and rehospitalization in the first 60-90 days after discharge [ 32]. In a pilot study the implementation of a 'smart' electronic discharge summary that reminds physicians to prescribe these medications improved the prescription rate from 88 to 100% [ 33]. Similar systems can be used to remind physicians to prescribe other important medications, such as aspirin or β-blockers after myocardial infarction.

Concerns and problems

Although IT systems provide clear and compelling mechanisms for reducing medication errors and improving safety, with a significant body of evidence to support their role, there are several concerns about their widespread clinical use.

First, there is scepticism about the evidence regarding the impact of such systems on clinical outcomes. Most of the current evidence is based either on single-site evaluations in large academic hospitals that have developed the systems internally and incrementally [ 34], or on large-scale economic models relying on projections [ 35]. The former is limited by questionable generalizability of the findings, as most hospitals will be implementing commercially developed systems with few resources for customization. The latter seem to overstate the potential benefits of IT by making calculations based on best-case scenarios. These problems are being addressed in more recent reports based on rigorous methods demonstrating the positive impact of a number of different IT systems and their clinical implementations, across multiple institutions, on a broad range of clinical outcomes such as inpatient mortality, length of stay, complications, and costs [ 8].

A second concern is raised by evidence of the potential negative consequences of IT systems on patient safety [ 36]. IT systems can adversely affect clinical care by generating more work or new work for clinicians, causing workflow problems, or even generating new kinds of errors [ 37]. These cautionary reports underscore the fact that IT systems must be designed to optimize clinical workflow and must be continually improved. One approach to addressing this problem is to require that clinical IT systems in the market place be tested and approved by a certification agency, such as the Certification Commission for Healthcare Information Technology (CCHIT - http://www.cchit.org) in the USA. This certification process assures a buyer that a system meets minimum standards in the domains of functionality, interoperability, and privacy and security. Although voluntary, this is similar to approval of medications by the US Food and Drug Administration (FDA), screening out grossly harmful products. Even so, all drugs by the approved FDA still carry risks of potentially harmful effects, especially if they are used inappropriately.

Third, it is being increasingly recognized that to prevent medication errors it is not merely the IT system itself that is important, but its implementation, in other words how it has been incorporated into clinical processes and workflow and how users actually use it in routine clinical care, sometimes described as the sociotechnical environment of the clinical workplace. For example, in one study there was a threefold increase in mortality in children after implementation of CPOE [ 38]. However, in another study there was a 36% reduction in standardized mortality using exactly the same software but with a different implementation strategy [ 39]. The current approach to IT standardization and certification is focused on the functionality of the system, but does not address its implementation or usability by clinicians.

IT systems must be widely implemented for most patients to derive benefits. However, currently only 10% of US hospitals use CPOE and <25% of US physicians in office practices use EHRs [ 40]. The main barriers to widespread adoption are the high costs of the systems and an environment of misaligned incentives, in which hospitals and physicians pay for the systems, but the insurance companies reap greater financial benefits. One important mechanism to remove this hurdle is through financial incentives to healthcare organizations. The Obama Administration has already proposed incentive payments to Medicare and Medicaid providers and hospitals for using CCHIT-certified EHRs in the widely anticipated 2009 economic stimulus bill. For years, US federal law, commonly called the Stark law, made it illegal for hospitals to assist outside physicians financially in acquiring EHRs. Recently, these restrictions have been significantly softened, removing an important legal barrier from financial incentives for the adoption of such records [ 41].

Conclusion

IT systems are key components of a multifaceted strategy to prevent medication errors and improve patient safety. However, we need to be mindful of their potential to affect clinical workflow adversely, with attendant complications. Improving standardization and certification of the design and implementation of such systems should help. In addition, creating an economic and policy environment conducive to the financial goals of hospitals and physicians will facilitate wider adoption.

Competing interests

None to declare.

REFERENCES

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22. Weaver P. American Society of Health Systems Pharmacists Mid-Year Clinical Meeting. Las Vegas, Nevada: 1998. Random sampling for quality assurance of the RxOBOT dispensing system. [ Google Scholar]

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33. Agrawal A, Khaneja M, Onyebuke I. Research and Practice. New York: 2007. 'Smart' electronic discharge summary to improve care of heart failure patients at discharge. Annual Symposium on Health Care Services in New York. [ Google Scholar]

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Medication errors: prevention using information technology systems

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Given the high frequency of medication errors with resultant patient harm and cost, their prevention is a worldwide priority for health systems. Systems that use information technology (IT), such as computerized physician order entry, automated dispensing, barcode medication administration, electronic medication reconciliation, and personal health records, are vital components of strategies to prevent medication errors, and a growing body of evidence calls for their widespread implementation.