AP RX: Clinical Decision Support System

Transcription

1 Northwestern University AP RX: Clinical Decision Support System MED INF 406 Decision Support Systems and Healthcare Summer 2010 Instructor: Dr. Pallav Sharda Katherine Chun, Michael Lim, Mari Pirie-St. Pierre 8/19/2010

2 TABLE OF CONTENTS Section 1. Overview / Introduction... 3 Section 2. The Model Model Design: Knowledge Engineering: Section 3. System Description Architecture: Architecture Overview Architecture Implemenation Customization User Interfaces How It Works Section 4. Evaluation FDA recommended Guidelines for software verification and validation Iterative Approach Verification Validation Clinical Efficacy Competitor Models Section 5.Discussion Appendix A: Model Appendix B: Model Assumptions Appendix C: Model References Appendix D: Proposed Antipsychotic Electronic Medical Record Summary Page References Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 2

3 SECTION 1. OVERVIEW / INTRODUCTION The issue of protection and promotion of physical health in persons with severe mental illness is emerging as one of the great public health and ethical relevance worldwide. i Studies show that people that have severe mental disorders have a reduced life expectancy when compared to the rest of the general population. People with severe mental illness (SMI) tend to be overweight, smoke more, have a poor diet and unhealthy lifestyle, as well as less access to healthcare. Treatment for mental illness, such as schizophrenia and bipolar disorder, consists of antipsychotic medications. These medications are associated with a variety of adverse side effects, some of which cause physical morbidity, and can lead to early death. Antipsychotic medications are known to cause Metabolic Syndrome (MetS). Metabolic syndrome is the name for a group of risk factors linked to obesity that increase your chance for heart disease and other health problems such as diabetes and stroke. The term metabolic refers to the biochemical processes involved in the body s normal functioning. Risk factors are behaviors or conditions that increase the chance of getting a disease. ii The increased risk to develop MetS under antipsychotic agents is in part related to their propensity to induce weight gain. Although all antipsychotics can induce weight changes, the relative risk to induce clinical relevant weight changes (>7% increase) is clearly different between antipsychotic agents. iii According studies completed by De Hert et al. there is now extensive research evidence that indicates: 1. Prevalence of many physical diseases is higher in persons with severe mental illness; 2. Gaps between these persons and the rest of the population concerning the prevalence of some these diseases (most notably type 2 diabetes) is increasing; 3. Co-existence of one or more physical diseases has significant impact on quality of life and psycho-pathological variables 4. Higher mortality in people with SMI; 5. Gap in mortality in people with SMI is increasing in recent decades; and 6. Access and quality of, physical healthcare in people with SMI is reduced, compared to general population. iv There are several approaches to dealing with the emergent information related to the morbidity and mortality concern surrounding the prescribing of antipsychotic medications and impact on physical health of the patient. Several lines of action as outlined by Mario Maj v are as follows: 1. Raising awareness of the problem among mental health professionals, primary care providers, patients, and families; 2. Education and training of mental health professionals and primary care providers; 3. Incorporation of dietary and exercise programs as an essential part of mental health treatment; and 4. Development of an appropriate integration between mental and physical health care. Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 3

4 People that suffer from a mental illness, especially a severe mental illness such as schizophrenia, receive life-long and at times fragmented care and treatment. Because of the length of treatment, medical records for behavioral healthcare patients have the propensity to become quite voluminous. Even with electronic medical records (EMRs), it is necessary for the clinician to go to several places within the medical record in order to seek out diagnoses, laboratory data, current medications, and vital signs such as height, weight, and blood pressure; all valuable information to have when prescribing medication. A Clinical Decision Support System (CDSS), to be utilized at the time of ordering/prescribing medications will assist in bringing decision-making treatment factors together. It will assist the practitioner in making the best treatment decision for the patient, as well as providing monitoring tools for the treatment of the patient. The patient summary screen below indicates the active problems (diagnoses), active medications, lab data, allergies, as well as active vital signs. The functionality for order entry is located on a different tab/screen than the patient summary information. At times, order entry is housed outside of the EMR. When the physician completes the antipsychotic prescribing orders, there is the potential that valuable information may be overlooked. The CDSS will provide information to the physician in the background, without the need to go searching for it or remembering all the key information required at the time of order entry. When prescribing antipsychotic medications, the psychiatrist needs to be aware of potential metabolic side effects of antipsychotic medications and include them in the risk/benefit assessment when choosing a specific antipsychotic medication. vi The AP RX CDSS (antipsychotic prescribing decision support system) tool will be used in conjunction with the inpatient and outpatient systems Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 4

5 available. It will integrate with the laboratory, pharmacy, and electronic medical record; and could be extended to a patient health record as well. Within a healthcare system, it will interface across the continuum of care. This would allow inpatient and outpatient staff to have access, electronically, to all pertinent treatment information. When the patient presents to an outpatient clinic, the clinician will have available to them the last medication orders provided to the patient from inpatient services, as well as a history of what was ordered during the inpatient hospitalization. All diagnoses, laboratory, and vital statistics would be available as well. With this, the clinician will be able to review the medications, and determine, based on the patient s response to treatment, how the medication therapy should proceed. The decision here is not one of the right diagnoses, but rather the right treatment choice. One that is not only providing a short-term solution, but one that will limit any long-term negative effects. It should be noted a solution is only another set of problems. What we are looking at is how one minimizes the potential problems of the solution of reduction of psychotic symptoms through the use of antipsychotic medications while limiting the affect of morbidity and mortality on the patient. Clinical decision support will provide the clinician with the clinical knowledge contained in the AP Rx tool. This will provide for improvement in the quality, safety, and efficiency of care when applied in the prescribing process. Due to prescribing of particular types of psychotropic medications and implications related to MetS, the physician will be provided with alerts related to cardio-metabolic factors. Nonetheless, if the physician chooses to continue to prescribe medications that would place the patient in a medium or high risk of MetS, the system will require a documented reason for continuing with the medication order. Override reasons are as follows: Adverse event or allergy to alternative treatments Contraindications to alternative treatments Patient previously on treatment (admitted on medication) Patient preference Previous treatment failure with alternative medications Patient being monitored or treated for metabolic syndrome Other*: (the clinician will type in a reason for the order) The alert will be triggered if the patient has one of the following diagnoses assigned: Hypertension Hyperlipidemia Diabetes Obesity And if one of the following medications is being prescribed that would place the patient at an increased risk of MetS. High Risk: Zyprexa/Olanzapine Clozaril/Clozapine Moderate Risk: Risperdol/Risperidone Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 5

6 Seroquel/Quetiapine Low Risk: Abilify/Aripiprazole Geodon/Ziprasidone In conjunction with monitoring of medical and psychiatric diagnoses, BMI, waist circumference, blood pressure, fasting plasma glucose, and fasting lipid profile will also be reviewed by the system. This data will be reviewed in conjunction with the prescribing of medication. If there are high values noted for instance with a BMI level an alert will pop up letting the physician know this. The alerts would be based on medication being prescribed, being one that is of moderate or high risk, in conjunction with a current diagnoses related to MetS. These will not be separate alerts, but rather, a notification as such: Patient s BMI is 42, has a diagnoses of Diabetes Type II, and has an elevated blood pressure reading of 180/ ) The physician would be required to acknowledge the above statement noting they are aware of the health indicators. Note, as previously discussed, if the physician is prescribing a medication that will place a patient in a medium/high risk category for MetS, he/she will be responsible for providing an override if he/she chooses to prescribe. Monitoring orders would also be presented to the clinician. The Clinical Decision Support System (CDSS) will need to be designed so that it can be incorporated for support at the time the prescriber is making a decision related to the prescribing of an antipsychotic medication. It is important for the DSS to be housed within the EMR and interfaced with various treatment support applications. The following Ten Commandments for clinical decision support as outlined by Bates et. al., vii were used in designing the AP RX CDSS: (Table 1) No. Commandment System Response 1 Speed is everything Contain sub-second screen flips for CPOE/CDSS screens 2 Anticipate and deliver in real time System gathers and associates information Diagnoses (medical and psychiatric) Lab Values Medications Allergies 3 Fit into the users workflow Utilized during medication and physical health order entry Combined medication and monitoring orders No need to re-enter information located in other systems (LIS, PhIS, EMR) 4 Little Things Can Make a Big Difference Makes it easy for practitioner to do the right thing in ordering antipsychotic medications Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 6

7 Provides automated monitoring orders based on diagnoses, medications, and vital statistics Reminders are set for monitoring lab data 5 Recognize that Physicians Will Strongly Resist Stopping 6 Changing Direction Is Easier than Stopping 7 Simple Interventions Work Best 8 As for Additional Information Only When Really Need it 9 Monitor Impact, Get Feedback, and Respond 10 Manage and Maintain Your Knowledgebased Systems When system presents medium or high alert, system provides suggestions for antipsychotic ordering based on diagnoses (psychiatric and medical), medications, and vital statistics Overrides are available for use Monitoring reports available related to risk categories of medications and usage System provides medium/high alerts based on complete medical and psychiatric course of treatment; allows for easier decision making process provides physician time to think through the ordering process and overall impact on patient s physical and mental well being Alerts will be combined Physician will be presented with other treatment options (medications) Physician will have overrides to justify bypass on order of medium/high risk Practitioner will be presented with monitoring orders for weight, waist circumference, lipid panel, and blood glucose Medium/High risk override only additional information required Assess system for how often medication suggestions are followed Monitor use of other in override category Monitor time of screen flips Ability to provide feedback on order entry screen Further defined in verification and validation section (Sections 4.3 and 4.4) Assign areas of decision support to individuals that are subject matter experts and periodically assess system to ensure that the knowledge base remains applicable Review data collected in other category of overrides Research and quality clinicians update knowledge system based on new studies and information related medications/dosages, etc. SECTION 2. THE MODEL 2.1. MODEL DESIGN: Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 7

8 The problem that we are focusing on is the prescribing of an appropriate second generation antipsychotic medication (SGA) given a patient s risk factors (or diagnosis of) metabolic syndrome. We are also attempting to model how to obtain the necessary baseline and follow-up monitoring assessment parameters, vital signs, and laboratory data given the medication and patient characteristics. Our model is primarily based on two guidelines: 1) The Consensus Development Conference on Antipsychotic Drugs and Obesity and Diabetes viii and 2) The Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). ix The Consensus Development Conference on Antipsychotic Drugs and Obesity published a consensus statement regarding the use of second generation antipsychotic drugs in relation to obesity and diabetes. They looked at 5 key questions: 1) What is the current use of antipsychotic drugs? 2) What is the prevalence of obesity, pre-diabetes, and type 2 diabetes in the populations in which the SGAs are used? 3) What is the relationship between the use of these drugs and the incidence of obesity or diabetes? 4) Given the above risks, how should patients be monitored for the development of significant weight gain, dyslipidemia, and diabetes, and how should they be treated if diabetes develops? 5) What research is needed to better understand the relationship between these drugs and significant weight gain, dyslipidemia, and diabetes? Of particular importance to the model are questions 3 and 4. The amount of weight gain, risk for diabetes, and worsening lipid profile vary among the SGAs. The findings of the panel are shown in the table below: Drug Weight Gain Risk for diabetes Worsening lipid profile Clozapine Olanzapine Risperidone ++ D D Quetiapine ++ D D Aripiprazole* +/- - - Ziprasidone* +/ = increase effect; - = no effect; D= discrepant results. *Newer drugs with limited long-term data Reproduced from Consensus Development Conference on antipsychotic drugs and obesity and diabetes vii For the model, we defined high risk medications as clozapine and olanzapine, medium risk medications as risperidone and quetiapine, and low risk medications as aripiprazole and ziprasidone. Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 8

9 The findings of the panel regarding question 4 are shown in the following table and forms the logic behind our monitoring protocol. Personal/family history Baseline 4 weeks 8 weeks 12 weeks x Quarterly Annually Every 5 years x Weight (BMI) x x x x x Waist circumference x x x Blood pressure x x x Fasting plasma glucose Fasting lipid profile X x x x x x More frequent assessments may be warranted based on clinical status Reproduced from Consensus Development Conference on antipsychotic drugs and obesity and diabetes vii It is important to note that the guideline does not differentiate between different risk categories of the medications and the frequency of monitoring for the patients. The FDA also mandated class warnings for the second generation antipsychotics in regards to the increased risk for severe hyperglycemia and diabetes and required a Dear Doctor letter be sent by the manufacturers for all the SGAs x. We followed the timeline as determined by the panel in our model design. Our model also uses the definition of metabolic syndrome as set forth by the ATP III panel viii : Clinical Identification of the Metabolic Syndrome Any 3 of the following: Risk Factor Abdominal obesity Men Women Triglycerides HDL cholesterol Men Women Defining Level Waist circumference >102 cm (>40 in) >88 cm (>35 in) 150 mg/dl <40 mg/dl <50mg/dL Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 9

10 Blood pressure Fasting glucose 130/ 85 mmhg 110 mg/dl Reproduced from Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) viii For repeat monitoring levels, physicians should consider treatment options when patients gain 5% of the baseline weight, BMI increases by 1 unit, and fasting glucose and lipid levels become undesirable xi. Our model analyzes the patient s medical history based on the initial nursing/physician assessment and laboratory data already in the system for the patient. Once a SGA is prescribed electronically, the CDSS will evaluate the patient s data against parameters as defined by the ADA Consensus paper and the ATP III definition of metabolic syndrome. The clinician will be alerted if there are missing baseline labs, if the clinician prescribes a medium or high risk medication and the patient has a diagnosis of metabolic syndrome. The clinician is prompted to order the missing labs for a baseline assessment and the continual monitoring of the patient after a SGA is prescribed. Clinicians must provide override reasons if they wish to continue ordering a medium or high risk medication if the patient has metabolic syndrome or other contraindications to the medication. It has been shown that despite the published guidelines for monitoring of the effects of SGAs and the warnings issued by the FDA, an associated increase in screening rates for metabolic syndrome has not been in evidence xii. By assisting the clinician in regards to screening and monitoring for a patient s risk for metabolic syndrome and second generation antipsychotic medications, we hope the AP RX CDSS will make a positive impact on patient care KNOWLEDGE ENGINEERING: 1. Knowledge acquisition: A Medline search was conducted in order to obtain the knowledge used in the model design. The search was focused on review articles, randomized controlled trials, treatment guidelines, and position statements on the use of second generation antipsychotic medications and their side effects. Psychiatrists known to the paper s authors were also interviewed for their input as to the model design, prescribing patterns, and general knowledge of the subject matter. 2. Knowledge representation: Please see appendix A, B, C, D for model and model definitions 3. Knowledge Inference: The decision tree initially depends on the baseline information and labs that are entered or are available for the patient. Once the prescriber orders a SGA, the decision tree becomes activated. The alerts that fire initially for the prescriber are based on the monitoring parameters as set forth by the ADA Consensus guideline in determining new lab data that needs to be obtained. Follow-up laboratory alert values are determined by the change from baseline and the diagnosis criteria for metabolic syndrome. 4. Knowledge maintenance: The model will need to be updated when new medications become approved, new research data is available, and new treatment guidelines are published. Our panel of medical experts will need to review the data (at least yearly) and have the system engineers update the model database and design based on the new information available. The model will also be updated based on user response to the alerts. Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 10

11 Reports will be generated which our experts will review in order to determine if the right alerts and order sets were appearing. SECTION 3. SYSTEM DESCRIPTION 3.1 ARCHITECTURE: ARCHITECTURE OVERVIEW In order for AP RX CDSS to be successful, it must interface with several systems within a hospital information system as well as external information sources such as Health Information Exchanges and Personal Health Records, in order to draw the information it requires to make proper assessment and apply the knowledge rules. CPOE Recommendation RX AP RX CDSS Knowledge / Rules Base Optional External HIE PHR EHR LIS PhIS Figure 3.1: System Architecture As Figure 3.1 shows, our Clinical Decision Support System (AP RX CDSS) will sit between the CPOE system and the information sources it requires. It will be able to connect to the Pharmacy, Lab, EHR, HIE and PHR to gather the appropriate information. Within a Hospital Information Systems, AP RX CDSS will connect with 4 key systems. information system provides key yet different information. Each Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 11

12 Clinical Prescription Order Entry System will provide patient identification, and prescription order information such as drug prescribed, dose, frequency, and duration. The Electronic Medical Record will provide patient information and details, allergies, medical history, and medical diagnosis. (Weight, Height, Waist Circumference, Blood Pressure.) The Lab Information system will provide lab results such as fasting glucose plasma and fasting lipid profile. The Pharmacy Information System will provide past prescription history and patient drug profiles. Externally, Health Information Exchanges may be able to provide supplemental or more recent information from various sources such as other hospitals and outpatient clinics. The Health Information Exchange will be providing all the information from all the other participating care providers for a given patient. The Personal Health Record can also provide additional information such as home glucose results, weight monitoring, and home blood pressure readings. ARCHITECTURE IMPLEMENTATION As seen in Figure 3.2, our clinical decision support system will sit between the CPOE system and the information sources. Additionally, you can see that the system is designed to allow all the necessary information to flow to the Clinical Decision Support System Interface Engine. Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 12

13 New / Current RX Rules MED INF Decision Support Systems and Healthcare Summer 2010 HIE PHR RX Clinician CPOE Interface RX Interface Engine Prescribing Clinician Alerts / Recommendation Alerts / Recommendation Knowledge Update Interface New MetS Knowledge Research / Quality Clinician New RX Knowledge EMR LIS PhIS Case Repository Historical RX Data Knowledge / Rules Base Medical Analysis Quality Assurance Metrics / Reporting Figure 3.2: Information Architecture The Clinical Decision Support System (AP RX CDSS) consists of three components, The Interface Engine which provide the decision making engine, and a framework that allows the engine to connect to various information sources as well as an integration point in CPOE. The Knowledge/Rules Database provides the brains of the decision support system and houses the decision and knowledge rules the interface engine will use to make decisions. The Case Repository provides a repository or memory for the system, which will record all transactions that go through the interface engine. This information can then be used for medical analysis and quality assurance metrics and reporting. Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 13

14 The Prescribing Clinician initiates the system with an Order in to the CPOE Interface, which then allows the CDSS Interface Engine to pick up the order information. It then goes out and pulls information from the various information sources, as well as the knowledge rules the interface engine will need to apply. It will also record the information in to the case repository, where historical analysis can be completed through medical analysis and/or quality assurance reporting. CUSTOMIZATION One of the key features of the system architecture will be the knowledge update interface which will allow Research or Quality Clinicians to update the knowledge/rules base with MetS or new Rx Knowledge, as well as default override definitions. Some examples could be new medications or new guidelines for diabetes management. Additionally the interface would also be able to add information sources such as new labs and lab values that can factor in to the knowledge rules. Additionally, as physicians use the system, the default override codes and default monitoring requirements in the system will also be customizable to meet localization needs, as well as changing guidelines and regulatory requirements. 3.2 USER INTERFACES To the Clinician, AP RX CDSS would be invisible unless an intervention or alert is required by the system. The AP RX CDSS will be fully integrated in to the CPOE. The prescribing clinician will place their prescribing order via CPOE, with no additional form or information being required to start the CDSS workflow. Upon the order being placed, it kicks off an automatic assessment of the prescription in question for anti-psychotics. The output will be via information placed on the screen (e.g. Pop-Up Window) for users to access, and the steps needed to resolve the issue. The System will summarize the key information that leads to the recommended decision. HOW IT WORKS The system would work by the use of a service on client system, designed to pick up information from the CPOE, e.g. Screen Reader/HL7 Interface Engine. Additionally, on the server side a Database/HL7 service that reads the order can also be used to retrieve order information for additional redundancy. Once the patient and order information is resolved, the CDSS system will go out to the HIS/HIE to gather the necessary information needed to make a clinical assessment. If information is sufficient, a recommendation or alert is made. Conversely, if information is insufficient, a recommendation is made to follow up on missing information. SECTION 4. EVALUATION 4.1 FDA RECOMMENDED GUIDELINES FOR SOFTWA RE VERIFICATION AND VALIDATION Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 14

15 When considering software verification and validation for a medical product, one must consider the FDA recommended guidelines for software verification and validation, last revised in The FDA has established these guidelines in order to outline its expectations. The FDA has decided to classify medical devices in 3 categories, I, II, and III, with I having the least restrictive requirements and class III having the most stringent requirements. The AP RX CDSS system would be classified as a class II device. The FDA describes Class II as devices are those for which general controls alone are insufficient to assure safety and effectiveness, and additional existing methods are available to provide such assurances. Therefore, Class II devices are also subject to special controls in addition to the general controls of Class I devices. xiii Class II devices are required to submit pre-market notification following the submission of a 510(k) file. As the FDA only provides guidelines xiv, it is up to the user to interpret the guidelines for their needs. Some key points that must be applied in our evaluation that the FDA only expects the least burdensome approach to verification and validation. All requirements and specifications must be validated. Verification and Validation must be planned and documented, and lastly the verification and validation has an independence of review. Independence of review does not imply third party verification and validation; it is an expectation of segregation of duties within the evaluation process. The FDA also provides their definitions of software verification and validation FDA Definitions: Software verification xv Provides objective evidence that the design outputs of a particular phase of the software development life cycle meet all of the specified requirements for that phase. Software validation xvi "Confirmation by examination and provision of objective evidence that software specifications conform to user needs and intended uses, and that the particular requirements implemented through software can be consistently fulfilled." The AP RX CDSS, would be a Class 2 device which would require Pre-Market Notification, following the submission of a 510(k) file xvii. 4.2 ITERATIVE APPROACH Due to the complexity and importance of the AP RX CDSS, we will take an iterative approach, with a minimum of two iterative rounds of verification, validation and clinical efficacy. After each round, the results are reviewed and the next iterative round will be adjusted based on the results of the first round. This is a key step in order to have an opportunity to reflect on the results as well as to have an opportunity to cover any evaluation gaps that may be identified along the way. This iterative approach can be used in a continuous cycle of improvement. 4.3 VERIFICATION System verification will be completed by using a variety of black box software testing techniques. Unit testing, functional testing, integration testing, system testing, performance and stress testing Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 15

16 will be used. Additionally, the model will be tested with test case scenarios to ensure the right treatment alerts fire for the prescribers. The system will also be tested with known real de-identified patient data with differing baseline characteristics, laboratory data, differential diagnosis, and medications, with the resulting response analyzed and verified. Prescriber response to the same de-identified patient data will also be tested to ensure that the appropriate alerts and order sets are being brought up and saved by the system. Requirements and specifications for the system will also be verified to ensure all system requirements are met, as recommended by the FDA. The testing will also be completed by an independent team that was not involved in the design, or requirements gathering process. 4.4 VALIDATION System Validation will be completed using a variety of methodologies, the first being User Acceptance Testing (UAT). UAT will be conducted by a sample group of clinical experts that specialize in behavioral and mental health who regularly prescribe anti-psychotic medications. Similarly, clinical trials and feedback will be conducted in a similar fashion and in accordance to FDA guidelines. Additionally, the reporting function of the AP RX CDSS system will be used to assess the appropriateness of the override reasons. Reports will be generated to evaluate the clinician response to the alerts. For each override, where the Other category was chosen, our panel of medical experts will evaluate the responses to determine if the override reasons need to be adjusted, and if the system will need to be updated. 4.5 CLINICAL EFFICACY The Clinical Efficacy will be measured by reviewing system use. The ordering patterns of physicians before and after the implementation of the system will be reviewed and evaluated to measure the impact of the AP RX CDSS in clinical use. Additionally, a retrospective review of patients prescribed a SGA will be conducted to determine if baseline monitoring parameters were being ordered, if medium/high risk medications were being ordered correctly, and if proper followup monitoring was conducted. A post-implementation review of prescriber patterns will also be conducted to determine if the system had any effect on the ordering of medications and labs. We will also conduct a review on new diagnosis of diabetes, hypertension, and dyslipidemia after SGAs are prescribed and the clinician response to the new diagnosis. 4.6 COMPETITOR MODELS Literature search revealed an article by Bronzino, et. al. that discussed several clinical decision support systems which provide for monitoring of drug treatments in psychiatric patients. xviii Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 16

17 System Description Comparison HEADMED Pharmacology system for depressed patients Interactive interface found to be too tedious for clinical users BLUEBOX Evaluation for suicide risk and proposed treatment planning Accurate and consistent; at research level PSYXPERT Mental disorder diagnostic aid Not used for prescribing medication Online Diagnostic Monitor Interactive model designed used by clinician during the course of patient interview Results found to conflict with attending clinical treatment conclusions OVERSEER Monitoring of drug treatments for bipolar disorder, schizophrenia, and major depression Prototype designed with a user-interface which closely models the form of clinical and social interaction the clinical environment Issues alerts when standard clinical practices are not followed or when labs results are abnormal Contains alerts for users as well as supervisors for hospital monitoring None of the above systems were found to have the features of the AP RX CDSS as far as incorporation of laboratory, medication, vital signs, as well as diagnoses, while maintaining the autonomy of the prescriber as well as the patient. SECTION 5.DISCUSSION Shortcomings of system/model, how can it be improved A stumbling point that we ran into at the beginning of this project was in attempting to provide an algorithm for a clinical decision support system for the prescribing of antipsychotic medications. The difficulty in algorithms in this area was discussed with a research scientist at Columbia University. Dr. Sharat Parameswaran of New York s Columbia University shared the following information with one of our group members: Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 17

18 The issue which makes defining risk and establishing a specific algorithm or decision tree complicated is the diversity and complexity of the research data regarding the issue of weight gain and metabolic side effects. Studies evaluating weight gain with antipsychotics usually report mean changes in weight. However, an unofficial dichotomous variable used to assess weight gain risk is whether individuals gain 7% of their baseline weight. Even if this dichotomous variable is used to assess risk, the actual results have been variable across studies, due to the variance in research study design. The additional factor complicating the issue is that most studies on this subject are time-limited and only assess weight gain in the acute setting. Assessing metabolic risk is similarly complicated by study variability, variability in the definition of metabolic side effects, and variability in results. In addition, there are no studies that examine the full range of antipsychotic medications and their relative risk of weight or metabolic side effects - even large studies like CATIE xix or CUTLaSS xx, xxi are limited by their study design and exclusion (intentionally or not) of certain medications. For this reason, we moved away from our attempts to base the AP RX CDSS on a decision tree algorithm, and to use clinical guidelines set by: 1. The Consensus Development Conference on Antipsychotic Drugs and Obesity and Diabetes 2. The Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Readiness for implementation/real-world application pilot/beta implementation In regards to readiness for implementation, the system concept and design would require a review by and collaboration with subject matter experts - practicing medical and psychiatric clinicians. Consultation for this project was obtained from several physicians regarding concept; this was a high-level review. Prior to real-world application, there is a need to beta test the system using real patient data. This could be performed at a psychiatric research institute that would provide an appropriate clinical setting and research resources to support the requisite testing environment. Key assumptions were that we needed to design a system that would support or assist clinicians that would result in overall improvement to not only mental health, but physical health as well. One of the main assumptions that we made is that the laboratory data for the patient would be integrated into the electronic medical record no matter where it was obtained (across the continuum of care of inpatient and outpatient settings) The CDSS was limited to just one aspect of the complex treatment of psychiatric disorders. We focused just on the effects of weight gain and risk for diabetes in relation to the second generation antipsychotic medications; other side effects of the medications and treatment considerations were not included in the model design. We also did not include the first generation antipsychotic medications that have known metabolic side effects. The model is limited to the treatment of adults - out of scope were the impact of antipsychotics on children which has different treatment and monitoring recommendations. The timing of the monitoring parameters were based on the timeline from the consensus guideline and did not adjust for increased monitoring that may be necessary for the patient s other clinical conditions. We also included just the medications mentioned in the consensus guideline; Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 18

19 however, newer medications and research that have come out since the guidelines were published and were not included in this model. What can be the future extensions of your model and system? The CDSS was designed for psychiatrists. Further extensions would be for primary care providers. We would further expand the CDSS in relation to the limitations mentioned above: include all aspects of the medications, metabolic syndrome, first generation antipsychotics, children, increased monitoring based on co-morbid disease states, newer medications and research. Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 19

20 APPENDIX A: MODEL Patient is admitted to hospital or presents to outpatient clinic Pharmacy Antipsychotics Hypoglycemics Lipid lowering agents Antihypertensives Low Risk Medication 3 Laboratory data Complete 4 Nurse performs initial assessment 1 Physician examines the patient and prescribes a second generation antipsychotic medication via CPOE or e-prescribing Order interfaces with EMR, laboratory, and pharmacy systems Does patient have allergy or contraindication to medication? Yes No Low Risk Medication 3 Laboratory data INCOMPLETE 4 Laboratory Fasting blood glucose Fasting lipid panel EMR Diagnoses, height, weight, waist circumference, blood pressure, allergies Alert prescriber Medication ordered? No Yes Prescriber overrides alert 2 and provides reason Medium/High Medication 3 Medium/High Risk Medication 3 Laboratory data Complete 4 Laboratory data INCOMPLETE 4 Order cancelled Figure A.1: The Model Part 1 Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 20

21 Low Risk Medication 3 Laboratory data Complete 4 Low Risk Medication 3 Laboratory data INCOMPLETE 4 Suggest ordering incomplete labs Psychotropic lab order set 6 Labs ordered Inpatient Outpatient Orders 7 laboratory prescription 7 Physician alerted with results or if no lab results (for outpatient within 1 week to follow up with patient) Suggest monitoring protocol 5 Medium/High Risk Medication 3 Laboratory data Complete 4 Labs not ordered Medium/High Risk Medication 3 Laboratory data INCOMPLETE 4 Does patient have metabolic syndrome 8 OR has significant lab changes 9? Clinician is required to order missing baseline labs; bring up orderset No Yes Alert prescriber and suggest low risk med alternative Order cancelled and low risk med prescribed Medium/High risk medication prescribed Clinician must choose override reason 10 Clinican orders missing labs Clinician waits for lab results No Yes Medication order cancelled Patient must have monitoring protocol ordered and referral to general practitioner Figure A.2: The Model Part 2 APPENDIX B: MODEL ASSUMPTIONS The nurse will perform the initial assessment before the physician examines the patient, and the patient will cooperate with the nurse so that she will be able to obtain all necessary baseline information required of the system. Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 21

22 Physicians will order a second generation antipsychotic when there is an appropriate indication. All of the patient s previous laboratory data will be in the system no matter where it was obtained (inpatient or outpatient) so that it can be cross-referenced. If patient was previously on SGA upon presentation, assume no treatment breaks. The hospital system can mandate the ordering of labs according to system policy for certain medications and/or indications. APPENDIX C: MODEL REFERENCES 1. Initial assessments: a. Performed by nurse or other clinician b. Patient and family history of obesity, diabetes, dyslipidemia, hypertension, cardiovascular disease c. Measurements: height, weight, waist circumference (at the level of the umbilicus), blood pressure d. Medication history and allergies 2. Override reasons a. Allergy incorrect b. Disagree with recommendations c. MD aware, will monitor d. Not a true allergy e. MD aware, will monitor f. Patient previously tolerated therapy g. Other*: (if chosen, text box will appear to document rationale) 3. Medication Categories (determined by risk for weight gain and diabetes) a. Low Risk: Aripiprazole and Ziprasidone b. Medium Risk: Risperidone and Quetiapine c. High Risk: Clozapine and Olanzapine 4. Laboratory data considered complete if a. Patient has not been previously on a SGA i. Fasting blood glucose within 1 year ii. Fasting lipid panel within 5 years b. Patient has previously been on a SGA i. CDSS to determine when last labs obtained ii. Labs considered complete if within two weeks of due date according to monitoring protocol timeline based on initial prescribing date. c. Monitoring protocol is minimal timeline does not consider increased monitoring due to patient s other disease states. 5. Monitoring protocol a. Inpatients i. Blood pressure daily ii. Measured weight weekly iii. If patient still hospitalized in timeframes of monitoring protocol: fasting plasma glucose, fasting lipid profile, NPO 12 hours prior to labs iv. If patient is discharged from hospital prior to next laboratory draw date, orders will automatically be converted to outpatient prescriptions upon discharge with instructions provided to the patient. Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 22

23 b. Outpatients and orders upon patient discharge from hospital i. Patient to weigh themselves every 4 weeks x 3 months then quarterly and notify physician if weight increases by 5% from baseline (target weight to be calculated for the patient). ii. Return to clinic at 12 weeks from initiation of SGA for follow-up labs (fasting plasma glucose and lipid profile). iii. Annually: personal/family history, weight, waist circumference, blood pressure, fasting plasma glucose iv. Every 5 years: fasting lipid profile 6. Psychotropic lab order set a. Clinician to order missing labs b. NPO after 1800 the night prior to labs (12 hours prior to lab draw) c. Labs at 0600: fasting plasma glucose, fasting lipid profile d. Resume previous diet after labs obtained 7. For simplicity, inpatient orders versus outpatient prescriptions depicted separately here, but should be assumed to apply to orders throughout the diagram. Refer to reference 6 for orders. 8. Metabolic syndrome as defined by ATP III definition (3 of the 5 criteria) a. Waist circumference: Males >102 cm (>40 in) or Females >88 cm (>35 in) b. BP 130/85 (or treated with antihypertensive medications) c. HDL cholesterol: Males <40 mg/dl or Females <50 mg/dl (or treated with cholesterol medications) d. TG 150mg/dL (or treated with cholesterol medications) e. FBG 110mg/dL (or treated with insulin and/or hypoglycemic medications) 9. Monitoring protocol alert values a. Weight increase 5% of baseline b. BMI increase by 1 unit c. Increases in blood pressure or lab values as defined above for metabolic syndrome 10. Medium/high risk medication order override reasons a. Adverse event or allergy to alternative treatments b. Contraindications to alternative treatments c. Patient previously on treatment (admitted on medication) d. Patient preference e. Previous treatment failure with alternative medications f. Patient being monitored or treated for metabolic syndrome g. Other*: (if chosen, text box will appear to document rationale) APPENDIX D: PROPOSED ANTIPSYCHOTIC ELECTRONIC MEDICAL RECORD SUMMARY PAGE In addition to the second generation antipsychotic ordering CDSS, we propose adding an additional tab to the electronic medical record that summarizes that pertinent information related to the risk of metabolic syndrome and antipsychotics medications. The summary page will populate automatically with information already documented in the EMR. By summarizing the information onto one page, the clinician will be able to quickly reference important information and make better informed decisions related to patient care. Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 23

24 Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 24

25 REFERENCES i Physical health care in persons with severe mental illness: a public health and ethical priority, Mario Maj, 2/2/2009, Date of Access: 7/18/2010. ii Cardiotabs for a Health Heart: Glossary, No Author, Accessed: 7/31/2010. iii Metabolic syndrome in people with schizophrenia: a review, Dehert et.al., 02/02/2009, Date of Access: 7/18/2010. iv Physical health care in persons with severe mental illness: a public health and ethical priority, Mario Maj, 2/2/2009, Date of Access: 7/18/2010. v Physical health care in persons with severe mental illness: a public health and ethical priority, Mario Maj, 2/2/2009, Date of Access: 7/18/2010 vi Metabolic syndrome in people with schizophrenia: a review, Dehert et.al., 02/02/2009, Date of Access: 7/18/2010. vii Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality, Bates, et. al., 5/27/03, Journal of the American Medical Informatics Association, Volume 10, Number 6 November/December viii American Diabetes Association, American Psychiatric Association, American Association of Clinical Endocrinologists, North American Association for the Study of Obesity. Consensus Development Conference on antipsychotic drugs and obesity and diabetes. Diabetes Care 2004; 27: ix Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001; 285, x U.S. Food and Drug Administration. Warning about hyperglycemia and atypical antipsychotic drugs. FDA Safety News, 2004 [article online]. Available from Accessed August 3, xi Goff DC, Cather C, Eden Evins A, et. al., Medical Morbidity and Mortality in Schizophrenia: Guidelines for Psychiatrists. J Clin Psychiatry 66:2, Feb 2005, xii Morrato EH, Newcomer JW, et. al, Metabolic Screening After the American Diabetes Association s Consensus Statement on Antipsychotic Drugs and Diabetes. Diabetes Care 2009; 32(6), xiii U.S. Department Of Health and Human Services: FDA, Initials. (2002). General and Special Controls. Medical devices. Retrieved (2010, August 14) from default.htm xiv U.S. Department Of Health and Human Services: FDA, Initials. (2002). General Principles of Software. Retrieved (2010, August 13) from xv U.S. Department Of Health and Human Services: FDA, Initials. (2002). General Principles of Software. Retrieved (2010, August 13) from Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 25

26 xvi U.S. Department Of Health and Human Services: FDA, Initials. (2002). General Principles of Software. Retrieved (2010, August 13) from xvii U.S. Department Of Health and Human Services: FDA, Initials. (2002). Device Classification. Retrieved (2010, August 13) from xviii Bronzino JD, Morelli RA, et.al, OVERSEEER: A Prototype Expert System for Monitoring Drug Treatment in the Psychiatric Clinic, IEEE Transactions on Biomedical Engineering, Vol. 36, No. 5, May xix Lieberman JA, Stroup TS, McEvoy JP, et al. Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. N Engl J Med 2005; 353: xx Jones PB, Barnes TR, Davies L, et al. Randomized controlled trial of the effect on quality of life of second- vs first-generation antipsychotic drugs in schizophrenia: Cost Utility of the Latest Antipsychotic Drugs in Schizophrenia Study (CUtLASS 1). Arch Gen Psychiatry 2006; 63: xxi Lewis SW, Barnes TRE, Davies L, et al. Randomized controlled trial of effect of prescription of clozapine versus other second-generation antipsychotic drugs in resistant schizophrenia. Schizophr Bull 2006; 32: Copyright 2010 Katherine Chun, Michael Lim, Mari Pire St. Pierre 26