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Case Report |
a Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
b Department of Family and Preventive Medicine, University of Utah, Salt Lake City, UT
c Intermountain Healthcare, Salt Lake City, UT
* Correspondence: Jacob S. Tripp, Department of Biomedical Informatics, University of Utah School of Medicine, 26 South 2000 East, Suite 5700 HSEB, Salt Lake City, UT 84112-5750 (Email: jacob.tripp{at}hsc.utah.edu).
Received for publication: 02/11/08; accepted for publication: 08/14/08.
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Abstract |
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Introduction |
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An important handoff occurs when patients are discharged from the hospital after inpatient or emergency department (ED) encounters. Primary Care Providers (PCPs) are often unaware of treatment that patients have received from other providers and in other settings unless informed by the patient themselves. Numerous researchers have described the importance of studying and improving the transition from hospital to outpatient care.3–7 Several studies have indicated that poorly handled transitions of care at hospital discharge are responsible for post-discharge adverse events and have suggested that improving this transition would reduce the likelihood of adverse events.8–10
To improve communication across this transition, a system of automated notifications was implemented at Intermountain Healthcare (Intermountain), to proactively notify PCPs of the existence of documentation for hospital visits involving their patients. While these providers already have electronic access to this documentation, often they do not access it in a timely manner, if at all, because they are not aware of its existence.
Because Intermountain does not maintain a database of patient-PCP relationships, which provider(s) ought to be notified following a patient's hospital visit can be unclear. The initial design of the system only sends notifications to the provider named as the PCP in the patient's hospital admission record. These data are incomplete and at times inaccurate, and rely on both the patient knowing who their PCP is, and on the admissions clerk asking for the information and then recording it accurately. In addition, follow-up care is often provided by specialists who are not a patient's PCP.6,11 These providers might also benefit from being notified about a patient's hospital care.
The purpose of this case report was to find out if existing medical data from electronic medical records (EMRs) could be used effectively to predict with which providers a patient would have appointments for follow-up treatment. To date, a few studies have been published looking at ways to use electronic data to predict who a doctor will consider as "their" patient.12,13 These studies do not, however, answer the question of who the patients consider to be "their" doctors, or who they are likely to see for follow-up care.
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Methods |
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This study analyzed six months of clinical and administrative data from two Intermountain hospitals, LDS Hospital in Salt Lake City, Utah, and McKay-Dee Hospital in Ogden, Utah, and from numerous Intermountain clinics.
Intermountain has developed its own EMR14 that is used by both inpatient and outpatient providers for storing and retrieving patient data. Data for this study were gathered from the Enterprise Data Warehouse (EDW), a large database that combines medical records from the EMR with other relevant records, including administrative data.
This analysis compared the accuracy of seven alternative criteria for predicting which providers a patient was likely to have an appointment with after an inpatient or ED encounter to the current method, using the PCP named in the admission record. All eight predictors are listed in Table 1.
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Each hospital encounter that met the inclusion criteria was compared against all patient appointments found in the EDW for the 60 days after the discharge date. The EDW patient schedule table stores information about outpatient appointments scheduled at all Intermountain and Intermountain-affiliated outpatient clinics. A follow-up appointment was defined as a scheduled appointment found in the patient schedule with a start date within 60 days of the hospital discharge date. Our assumption is that the providers who would be most benefited by a notification about a patient's hospital care are those that treat the patients in the 60 days following their discharge.
While not every scheduled appointment actually occurs, we chose to base our measure on all scheduled appointments rather than only completed appointments. The reason behind an uncompleted appointment is not recorded in the EDW, so it is not possible to tell when a cancellation might have been due to an appointment being mistakenly scheduled with the wrong provider, was simply rescheduled, or was cancelled because it was deemed unnecessary. We are looking for relationships between providers and patients and while a completed appointment may indicate a stronger relationship than an uncompleted one, we still feel that a scheduled appointment is a strong indicator of an existing relationship.
The current predictor, PCP named in the admission record, was recommended by a committee of outpatient providers as the best way to ensure that the alerts go to providers who will find them useful. The other seven predictors were recommended by informaticians who have extensive experience working with EMRs, including work on the problem of determining existing patient-provider relationships based on available electronic data.
For each predictor, data were grouped into a 2x2 contingency table. To view these tables, as well as a brief discussion of how true and false positives and negatives were defined, please see Appendix 1, available as an online resource at www.jamia.org.
For each of the predictors, Sensitivity and Positive Predictive Value (PPV) were calculated. In the context of this analysis, Sensitivity is the percentage of providers who had follow-up appointments scheduled with the patient that were correctly indicated by the predictor, and can be thought of as the percentage of actual follow-up providers who would be alerted by an alerting system triggered by that predictor.
The PPV of each predictor is the percentage of follow-up providers indicated by the predictor that actually had follow-up appointments scheduled with the patient. If that predictor were used as the triggering criterion for an alerting system, 1-PPV is the percentage of alerts that would be sent to providers who did not have a follow-up appointment scheduled with the patient.
In any notification system, it is important not only that the notifications go to as many providers who will treat the patient as possible, but that the number of alerts sent to providers who will not see the patient is limited. It has been demonstrated that too many false positive alerts can cause providers to ignore more relevant alerts,15 and this concern was also voiced by the Intermountain outpatient provider committee.
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Results |
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Sensitivity and PPV for the eight predictors are given in Table 1. "Last provider who had an appointment with the patient prior to their hospital encounter" had the highest PPV, 48% for inpatient encounters and 35% for ED encounters.
"All providers who had scheduled appointments with the patient in the 180 days preceding their hospital encounter" had the highest Sensitivity, 48% for inpatient encounters and 42% for ED encounters. "All providers who dictated clinical documents about the patient in the last 180 days" also had a Sensitivity of 48% for inpatient encounters, but only 33% for ED encounters. Raw numerical data for all eight of the predictors can be found in Appendix 1, available as an online resource at www.jamia.org.
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Discussion |
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The PCP identified in the patient's hospital admission record was not the worst predictor, but neither was it the best. This does not necessarily mean that the identified PCP should not be alerted. In instances where PCP data are collected accurately, it is likely that this provider will appreciate being notified about their patient's treatment regardless of whether or not a follow-up appointment occurs.
The second key finding from the analysis is that none of the data we evaluated were highly accurate in their prediction of follow-up providers. None of the predictors successfully predicted more than half of follow-up providers, and the best predictor still would generate more false alerts than correct ones. Certainly, this analysis was fairly simple in nature and further refinement of the prediction criteria would likely lead to improved performance. Possibilities for improved prediction might include the use of other clinical or administrative data, experimentation with varying windows of time, or the combination of multiple prediction criteria.
No matter how the predictors are created, it is also true that prior medical data will be unable to predict all follow-up appointments. For example, consider that patients being treated at the hospital for a new condition are likely to begin receiving treatment from providers with whom they have no past medical history. Thus, it is also vital to research and provide mechanisms that allow outpatient providers to "pull" information from electronic medical record systems in addition to improving on existing "push" methods.
The results of this study are limited by the fact that the Intermountain EDW only contains data for encounters and appointments taking place at Intermountain facilities. While Intermountain does provide about half of all medical treatment in the region,14 the EDW does not contain data for encounters and appointments occurring with providers and facilities outside of the Intermountain network. Some patients that did not appear to have preceding or subsequent outpatient appointments may well have had appointments with providers outside of the Intermountain network. This seems especially likely for ED encounters.
Additionally, the generalizability of this study is limited in that many healthcare networks do not have record systems that provide easy access to both inpatient and outpatient medical records. Not having access to records of previous outpatient care would limit an organization's ability to implement notifications using some of the criteria analyzed here.
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Footnotes |
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