HIMSS Davies Enterprise Award Submission

Transcription

1 HIMSS Davies Enterprise Award Submission Applicant Organization: St. Clair Hospital Organization s Address: 1000 Bower Hill Road, Pittsburgh, PA, Submitter s Name: Richard J. Schaeffer Submitter s Title: VP and CIO Submitter s rich.schaeffer@stclair.org Core Item/Clinical Value: EHR support for organizational initiatives, including diabetes, C.diff, and pneumonia vaccine administration Executive Summary: St. Clair continually seeks opportunities to improve quality, reduce cost, and provide a positive patient experience. Consistent with this commitment, St. Clair Hospital is enrolled in Highmark s QualityBlue Pay-for-Performance program. Participation in this program assists the organization to approach challenging clinical improvements with a more focused effort, centered on improving quality, improving patient satisfaction and implementing evidence - based practices. Powered by tools within the EHR, improved clinical value was realized in the following areas: improved diabetes care, a reduction in C.diff infections, and improvements in the administration of pneumonia vaccine. Background Knowledge: St. Clair Hospital is a 328-bed independent, acute care facility that provides advanced, high quality health care to more than 480,000 residents of southwestern Pennsylvania. Our mission is to provide highly-valued, service oriented healthcare to our community across the hospital s main campus and five outpatient centers. The selected quality initiatives represented welldocumented risks to our patients and worthy of directed efforts at improvement. Leveraging tools within the EHR could help us achieve success in improving the care provided to our patients. Local Problem and Intended Improvement: 1. Diabetes The American Diabetes Association recently reported that diabetes is not only the 7 th leading cause of death, but over 9% of the US population has the disease. Accordingly, a self-assessment of the St. Clair experience related to diabetes care was conducted. In 2011, twenty-eight (28%) of patients had a primary diagnosis of diabetes. Several key improvements were desired, including data analysis to identify the rate of patients with hyperglycemia and severe hypoglycemia events, the identification of the percent of diabetic patients who, either prior to discharge or within 90 days of the hospital admission, received a hemoglobin A1C (HbA1c), and the implementation of evidence-based, standardized order sets and protocols. Diabetes is typically a secondary diagnosis, and patients with diabetes are managed across multiple nursing units. 1

2 2. Healthcare Infection Prevention In recent years, the CDC has reported that C.diff infections are at an all time high nationally, and have been linked to 14,000 annual deaths nationally. In 2011, the Hospital observed an undesirable upward trend in the frequency of C. diff infections. In an effort to address this potentially devastating condition, efforts were focused on reduction strategies. These included an improvement of the rate of C.diff per 10,000 patient days, as well as the use of protocols that restrict and reduce the overall use of antibiotics. To further drive the rate down, for FY2014, the Hospital established an organizational goal around C.diff reduction and tied it to the Hospital s organizational achievement award. Three levels of performance were established with the maximum target was set at 55 infections. 3. Pneumonia The CDC reports that more than 5,000 patient deaths occur as a result of pneumococcal infections and these infections are the sixth leading cause of death in the country. A significant number of these deaths were preventable had the patient received a pneumococcal vaccination. Beyond the CMS Core Measure requiring hospitals to report pneumococcal vaccination rates, St. Clair Hospital recognized this issue to be a major health care concern and sought an improvement in the number of patients discharged who were screened for pneumococcal vaccine status and received pneumococcal vaccine prior to discharge, if indicated. Design and Implementation: For each initiative, clinical improvement teams were established. Membership included physician champions, clinical leadership, quality, IT and bedside clinical staff. Members were selected based on knowledge, expertise and skill related to the specific goals that needed to be accomplished. All goals were assigned to the most appropriate team member and included accountability for achieving the desired outcome. Once this structure was established, assessments were conducted related to the current situation and the development of an action plan for improvement. For the St. Clair Diabetes protocol, our HIS team had to hardwire the clinical decision support into the EHR. First, the Diabetes Risk Assessment was added to the Patient Profile tab. Next, the electronic search and auto-population of diabetes history functionality was added. Finally, an automatic order was added to the system to schedule a HbA1c test if no record of a HbA1c test in the last three months is part of the patient s medical history. Our approach was to minimize unnecessary alerts that lead to alert fatigue. The selected diabetic process uses a question and answer dialog rather than leaning on an alert and provider reaction. Within the Patient Profile process, the nurse completes the Diabetes Risk Assessment, which automatically pulls in history of diabetes and history of A1C results. How Health IT Was Utilized: 2

3 Diabetes: To successfully pursue improved care for the population of diabetes patients, the EHR must first assist clinicians in identifying these patients using analytics to generate the Monthly Diabetes Report shown below. For example, a key focus for improvement was identifying patients who experienced blood glucose levels of greater than 180 mg/dl or less than 40 mg/dl. At the time of the admission, the EHR searches for a previously documented history of diabetes. For a positive history, the system then searches the patient s record for a documented Hemoglobin A1C result filed within the last 3 months. If the system finds none, the EHR interacts with the clinician by inquiring if an order is needed and, if yes, proceeds to place the order as depicted in the flow diagram below. Below, the top portion of the screen shot show the system finding the diabetes history and auto-selecting the Yes radio button during the Diabetes Risk Assessment. Likewise, the A1C test 3

4 within 3 months is also found, and again, the appropriate radio button is auto-selected for the nurse. In this screen shot, the search for the A1C within the last 3 months is unsuccessful and the auto-order of the test is entered. 4

5 To better communicate with care providers, the Hemoglobin A1C result is retrieved and added to the patient s discharge instructions via the Transition of Care Record. This document is available via the patient portal and patients are encouraged to take these instructions with them when they visit the next care provider. In addition, those providers able to access the EHR, can view this document online. Diabetes - Capturing Undiagnosed Patients For the process of identifying new diabetic patients, screening criteria was put in place early on in the patient s stay, as shown in the diagram below. Once identified, it is essential to ensure that these patients receive critical survival skills and appropriate follow up education. To support the key focus for improvement, identifying patients with blood glucose levels greater than 180 mg/dl or less than 40 mg/dl is conducted through the use of logic based on specific criteria, as shown in the flow diagram below. The EHR first searches for a filed blood 5

6 glucose result. Exceptions are built in to this logic so that patients with a known history of diabetes or those patients receiving medications known to increase blood glucose levels, are eliminated. Following this inclusion/exclusion criteria, only those patients with a possible new diabetes diagnosis remain. At this point, an order for a Hemoglobin A1C is generated. Once the Hemoglobin A1C result is filed, those patients with a result greater than 6.5 are pulled onto the Potential Diabetes Risk list, and are then referred to appropriate clinicians for review as diagramed in the workflow below. 6

7 The Subcutaneous Insulin Order Set shown below preselects orders for Outpatient Diabetes Education and Nutritional Counseling. 7

8 This is done so that the order will cross over to the Discharge Orders Reconciliation for the physician to address if the patient will need further education upon discharge. Once selected on the Discharge Orders Reconciliation, it will send an automatic referral to the Diabetes Center to contact the patient. Diabetes - Patient Glycemic Control New for 2014, a goal to improve the time between obtaining blood glucose levels and the delivery of meal trays was defined. A system report was designed based on each unit s meal delivery time. Care providers are then able to prioritize tray delivery based on the last BGM documented. Tray delivery is not to occur for those patients who have not yet had a blood glucose level obtained. The EHR also provides clinicians with ongoing 7-day trending reports for blood glucose levels and any insulin coverage required. Finally, all efforts were supported by revised protocols and 8

9 electronic order sets that were developed with evidence-based sliding scale orders and referrals for outpatient diabetes self-management education. Healthcare Infection Prevention In 2012, a targeted goal of 5% reduction in the C. diff infection (CDI) rate was established. Process goals included a coordinated and enhanced antibiotic stewardship and earlier identification and isolation of C. diff patients. In support of this goal, the EHR and the electronic infection prevention program, Theradoc, were utilized to define key data elements. These included the identification of healthcare-facility onset cases (HO), community onset cases (CO) and community onset, healthcare-facility associated cases (CO-HCFA). In addition, the system provided a report of Defined Daily Dose (DDD) for quinolones and cephalosporins. Using Theradoc s Infection Control assistant module daily, the St. Clair infection preventionists perform daily surveillance (Monday through Friday). Theradoc generates reports of patients with positive C.diff result in the designated timeframe. Here is an example of a real time microbiology report. 9

10 Patients with positive C.diff results are moved by the Infection Preventionist to the Infection Worklist. The Worklist serves as Infection Prevention's electronic worksheet of patients with potential Health-care associated infection. The Infection Preventionist can expand C.diff patient entry on the Infection Work List to access the Theradoc Infection Worksheet. Using the Worksheet, the Infection Preventionist can classify C.diff patient as healthcare associated or community acquired C.diff infection based on current CDC healthcare associated infection definitions. Theradoc interfaces with the EHR system (Allscripts SCM) to recover clinical symptoms to meet criteria such as temperature and 10

11 complete blood count (CBC.) The Infection Prevention also uses SCM to research nursing and physician progress notes for further information to meet criteria. The Infection Preventionist can pull reports of healthcare-associated infections identified in the Infection Report Context of Theradoc, for mandatory reporting to the CDC s National Healthcare Safety Network (NHSN) system. The reports are used for Infection Prevention Committees, Leadership, and frontline staff to identify trends and gaps so that interventions can be implemented to prevent C.diff infections. Introduction of Probiotic Treatment Based on current literature, including the November 2012 Annals of Internal Medicine, which concluded there is some evidence that probiotic prophylaxis results in large reduction in CDAD, the Pharmacy team members recommended the use of probiotics for patients receiving the known antibiotic-culprits for acquiring an HO episode of C. diff. As a result, further changes were added to key antibiotic order sets. Once the decision was made to implement the probiotic protocol, it was essential that the EHR deliver this capability seamlessly. This required several rounds of discussion to find the middle ground between the Pharmacy team members who advocated alert messages being triggered for the automatic order, and physician leadership who was not in favor of an extra click every time an antibiotic was ordered. Needless to say a compromise was reached and the probiotic auto-order is now a standard part of antibiotic ordering. Once the antibiotic is ordered, the EHR automatically enters the order for the probiotic. In the workflow shown below, the logic running behind the order entry screen determines if the medication is an antibiotic, and if so, examines whether it is an antiviral, antifungal, antimalarial, antituberculosis, or anthelmintic. It then compares the med to an exclusion list and further examines the patient type, patient age, the duration of the medication therapy, the ANC lab results, and potential probiotic duplication. If all criteria are met, the probiotic is autoordered. 11

12 Pneumonia: In January, 2011, 95% of patients were screened for vaccine status and administration, if appropriate. It was determined that the organization should strive for near 100% compliance. Evidence-based order sets were implemented. One year later, the number rose to 98%, closing the gap. However, more improvements were required. During the admission process, nurses reported that it was often difficult to ascertain whether the patient had received the vaccine in the past. Functionality within the EHR was designed to search the patient s past medical history. If the patient has no vaccine history documented, the EHR alerts the nurse to that fact and begins to assist the nurse in assessing whether the patient is appropriate to receive the vaccine. The EHR now searches for any chronic condition (as defined by the CDC) and asks the nurse to validate this information. Depending on that validation, an order for the pneumococcal vaccine is automatically placed, if appropriate. Once administered using bedside, bar-coded technology, the EHR automatically updates the vaccine history, facilitating ease of retrieval should the patient be admitted in the future. As the workflow in the diagram depicts, the logic embedded behind the scenes in the EHR performs the heavy lifting of searching the various sections of the patient s electronic chart to find evidence of a prior vaccine. 12

13 In addition, health issues are accessed to determine whether chronic or immunocompromising conditions are present and the appropriate radio button is autoselected. The Hospital s protocol rules are applied to appropriately select whether the vaccine should be ordered. By following the Protocol Rules flowchart below, the factors involved in determining the autoorder, and the sequence of decisions, can be seen. If it is determined that the patient should receive the vaccine, and the patient refuses, the nurse has the opportunity to record that. If the routine determines that the vaccine should not be ordered, the nurse cannot override and process the order. Finally, upon charting of the vaccine administration, an entry is recorded in the patient s health issues list. All of these automated portions of the workflow save the nurse time, and ultimately enable an efficient process. 13

14 Value/Derived Outcomes: Diabetes: The EHR was designed to provide results to the Diabetes Team, identifying those patients who experienced hyperglycemic episodes of greater than 180 mg/dl and severe hypoglycemia less than 70 mg/dl. In March, 2011, the incidence of hyperglycemia was 659 per 1,000 patient days. Hypoglycemia incidence was 55 per 1,000 patient days. It was clear that there was room for improvement. With physician engagement, a standardi zed, evidence-based subcutaneous order set was implemented, accompanied by a revised hypoglycemia protocol. Three years later, in March, 2014, the rate of hyperglycemic episodes dropped to 416 per 1,000 patient days. A refocus of hypoglycemia resulted in revised data collection for severe hypoglycemia, now defined as a blood sugar less than 40 mg/dl. In 2014, the rate of severe hypoglycemic episodes fell from 4.6 to a low of 1.86 per 1,000 patient days. The search for an HbA1c result with the last 90 days was conducted and reviewed. If the outcome was that there was no result, auto-ordering an HbA1C was triggered. Since the 2011 timeframe, this has consistently exceeded the established goal of 90%. Finally, time frames from the last blood glucose reading to meal delivery time have been reduced to less than the targeted goal of 30 minutes or less, with the one exception being a 33 minute average in March Healthcare Infection Prevention: In 2012, positive outcomes were realized. The rate of HO-CDI reduction exceeded the targeted goal of a 5%. In 2013, at 61 cases of HO-CDI, the rate remained within the targeted range. However, beginning in fiscal 2014, the Hospital established the stretch goal of reducing the number of infection to 55 or less per year. During the first three (3) months, the Hospital was well off the pace to achieve the goal and additional actions had to be identified and implemented. After getting off to such a bad start, the Hospital was not successful in achieving the stretch goal of 55 or less infections for the 12-month period. However, over the final 6 months of the period, the rate of infection was well below the pace necessary to stay below 55, and leads to optimism for the next 12 months. The use of probiotics is still considered an evolving subject of 14

15 study as a mechanism for the prevention of C.diff, and it is therefore difficult to draw a direct correlation between its use and the decreased rate of infection. However, the implementation of the auto-ordering feature of the EHR was identified as a key intervention, and the rate of C.diff infection did decrease thereafter. Pneumonia: In January, 2010, 95% of patients were screened for their current vaccine status and administered the vaccine if appropriate. In January, 2013, 100% of patients met the measure. This has been sustained with little fluctuation. 15

16 Lessons Learned: Although it may seem that there is organizational consensus, and a level of urgency surrounding the implementation of clinical decision support, in the case auto-ordering the probiotic, different stakeholders were firm in their views on exactly how it should be implemented. For example, pharmacy advocated a detailed alert to be presented to the physician. Physician leadership was firm in their belief an alert box was unnecessary. Expecting clinicians to readily adopt and apply a new best practice, will have only limited success. To achieve the desired adoption, it must be hardwired into the EHR so that it happens automatically. The use of probiotics has been advocated for some time, but the compliance was inconsistent until it became an automatic function of the EHR. Improvement can be achieved on particular quality measures, and despite having the same solutions in place and functioning as expected, there are variables that arise that erode the gains, and new solutions have to be put in place to recover the gains. Identification of chronic disease patients is fundamental to many quality initiatives and yet these cohorts are not always pre-defined in the EHR. Without a disease registry module, the chronic disease patient lists must be created in the EHR. Specific for diabetic identification and treatment o Not every newly diagnosed diabetic patient is ordered insulin. Listen to the front line staff. They identified the gap in the order sets for protocol IV fluids. o Establishing systems to support patients continued use of their insulin pumps during hospitalization is challenging. Champions must increase staff awareness and confidence with implementing intervention of initiating IV fluids with dextrose or adding them to existing fluids. o Collecting data related to monitoring time between blood glucose measurement and tray delivery is a challenge. It is expected the EHR s elapsed time feature will help to further automate this task and provide analytic reporting. Financial Considerations: The Diabetes program required the following capital/staff investments totaling $68,513: o Lab procedures (Glycohemoglobin A1C- One per Admission)- $12,557 o Total labor costs (meeting time, lab time, HIS team) was calculated to total $54,286 to implement IT and execute the diabetic protocols. o Over 1400 education packets were developed and distributed to staff ($1,671) The reduction of hospital acquired infections represents significant potential return on investment through cost avoidance and avoiding CMS Value Based Purchasing penalties. 16

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