A Clinician-mediated, Longitudinal Tracking System for the Follow-up of Clinical Results

Transcription

1 A Clncan-medated, Longtudnal Trackng System for the Follow-up of Clncal Results by Danel Todd Rosenthal B.S., Bology (1997) Duke Unversty M.D. (2001) Unversty of South Florda College of Medcne Submtted to the Health Scences and Technology Dvson and the Dvson of Bomedcal Informatcs n Partal Fulfllment of the Requrements for the Degree of Master of Scence n Bomedcal Informatcs at the Massachusetts Insttute of Technology May 16, u e a z0 7j 2005 Danel Rosenthal. All rghts reserved. The author hereby grants M.I.T. permsson to reproduce and dstrbute publcly paper and electronc copes of ths thess and to grant others the rght to do so. Sgnature of Author Health Scences and Technology Mav 16, 2005 Certfed by... / Henry Chueh M.D., M.S. Assstant Professor of Medcne, Harvard Medcal School Thess Supervsor Accepted by Martha L. Gray Ph.D. Edward Hood Tapln Professor of Medcal and Electrcal ngneerng Co-drector, Harvard-MIT Dvson of Health Scences and Technology ARCHIVES

2 A Clncan-medated, Longtudnal Trackng System for the Follow-up of Clncal Results by Danel Todd Rosenthal Submtted to the Health Scences and Technology Dvson and the Dvson of Bomedcal Informatcs May 16, 2005 In Partal Fulfllment of the Requrements for the Degree of Master of Scence n Bomedcal Informatcs at the Massachusetts Insttute of Technology ABSTRACT Falure to follow-up on abnormal tests s a common clncal concern comprsng the qualty of care. Although many clncans track ther patent follow-up by schedulng follow-up vsts or by leavng physcal remnders, most feel that automated, computerzed systems to track abnormal test results would be useful. Whle exstng clncal decson support systems and computerzed clncal remnders focus on provdng assstance wth choosng the approprate follow-up management, they fal by not trackng that follow-up effectvely. We beleve that clncans do not want suggestons how to manage ther patents, but nstead want help trackng follow-up results once they have decded the management plan. We beleve that a well-desgned system can successfully track ths follow-up and only requre a small amount of nformaton and tme from the clncan. We have desgned and mplemented a complete trackng system ncludng 1) an authorng tool to defne trackng gudelnes, 2) a query tool to search electronc medcal records and dentfy patents wthout follow-up, and 3) a clncal tool to send remnders to clncans and allow them to easly choose the follow-up management. Our trackng system has made mprovements on prevous remnder systems by 1) usng our unque rsk-management gudelne model that more closely mrrors, yet does not attempt to replcate, the clncal decson process, 2) our use of massve populaton-based queres for trackng all patents smultaneously, and 3) our longtudnal approach that documents all steps n the patent follow-up cycle. Wth these developments, we are able to track 450 mllon peces of clncal data for 1.8 mllon patents daly. Keyword follow-up trackng; remnder system; preventve medcne; computerzed medcal record system; practce gudelnes; clncal decson support system Thess Supervsor: Henry Chueh M.D., M.S. Ttle: Assstant Professor of Medcne, Harvard Medcal School 2

3 Table of Contents 1 Introducton Background Preventve M edcne & Approprate Follow-up Case Study Background M ethods Results Im plcatons Identfyng Patents wthout Follow-up Representng Logc Gudelnes... I Com puterzed Clncal Rem nders Barrers to Gudelnes and Resultng Rem nders Goals for Trackng System Rsk-M anagem ent Stratfcaton Tm ng of Interventon Scope and Scale Desgn G udelne M odel Clncal Elem ent Dctonary Patent D ata Identfyng Patent Sets Rem nder groupng Applcaton Archtecture Im plem entaton Tracker Authorng Tool Tracker Query Tool Gather Patent Data Construct SQ L From Tracker Logc Planned Versus Completed Follow-up Scores Tracker Clncal Tools Clncal Servces Inbox Applcaton Dscusson References

4 1 Introducton The falure to successfully apply evdenced-based gudelnes to the preventon and management of dsease has been descrbed as the "qualty chasm" by the Insttute of Medcne (IOM).[1] Important factors contrbutng to ths "knowledge-performance" gap n delverng qualty care nclude tme lmtatons durng the clncal encounter,[2] dffculty managng an ncreasng burden of clncal data,[3] and lack of approprate follow-up of abnormal results.[4] Clncal decson support systems (CDSS) have shown some success mprovng adherence to gudelnes. [5] In addton, computerzed clncal remnder (CCR) systems have shown promse; however they are hstorcally "real-tme" tools, makng recommendatons to the provder wth the patent present at the pont-of-care. Alternatvely, other applcatons have shown success mprovng gudelne adherence by delverng recommendatons to the provder "asynchronously" between patent vsts. [6] Whle these advances am to mprove the qualty of care, they also contrbute to the ncreasng volume of data presented to clncans. In the outpatent settng, the average clncan cares for several hundred patents who often have only one or two offce vsts each year. Wth the ncreasng amount of nformaton to manage, falure to follow-up on abnormal tests s a common clncal concern comprsng the qualty of care. Furthermore, ths falure to follow-up s a sgnfcant source of malpractce ltgaton n outpatent medcne. Currently, provders remember to check follow-up by ether a) schedulng a follow-up vst, b) wrtng a physcal note, c) leavng the patent's chart on ther desk, or d) manually settng a remnder usng an electronc resource such as a calendar program. Provders feel that automated systems to track abnormal test results would be useful. [7] Our goal was to produce a follow-up trackng system. Whle exstng CDSS and CCR systems focus on provdng assstance wth choosng the approprate follow-up management, they fal by not trackng that follow-up. We beleve that clncans do not want suggestons how to manage ther patents at the pontof-care, but nstead want help trackng follow-up management once the patent has left the offce. We desgned our system to reflect ths phlosophcal dfference by changng the focus from pont-of-care suggestons to follow-up trackng. Our trackng gudelnes do not attempt to replcate the decson process, but nstead focus on representng all plausble follow-up choces to allow for easy selecton for dsease follow-up. We are able to catch patents who may fall through the proverbal cracks of follow-up by usng a populaton searchng approach rather than trackng patents ndvdually. We search all electronc health records and admnstratve clams databases to dentfy groups of patents who need follow-up, send remnders to the clncans, and then track the follow-up. Our trackng system provdes the solutons to the process of follow-up care (Fgure 1) ncludng 1) a unque gudelne model created specfcally for ease of follow-up trackng, 2) a populaton-based search engne that dentfes sets of patent wthout follow-up, and 3) a notfcaton tool that clncans use to vew new results and choose follow-up management. Our system then tracks these follow-up plans, contnually searches the patent records, and notfes clncans f follow-up has been mssed. We descrbe the trackng system we created. 4

5 Icted) Vmedcal experts (l, A...fnc eflt' dentfy patents at rsk qucnt) I, : tracker gudelne cent -cords (followv-up nccdcd) 1 \Y t IIIJ l LC follow-up notfy clncans O clncans plan follow-up Fgure 1. Process of Follow-up Care. To effectvely track patents and ther follow-up, a system must 1) defne what patents requre follow-up, 2) search medcal records and dentfy those patents, 3) notfy ther clncans, then 4) track ther follow-up management. Ths entre process needs to be repeated frequently and relably to catch all patents who are mssng follow-up. 5

6 2 Background 2.1 Preventve Medcne & Approprate Follow-up Preventon s ncreasngly the goal of health care delvery. Preventon strateges dffer accordng to when the preventve acton s taken durng the dsease process, ncludng prmary, secondary, and tertary preventon. Prmary preventon occurs before a dsease begns to develop. Examples of prmary preventon nclude mmunzatons and health counselng. Secondary preventon occurs after a dsease has begun to develop but pror to symptoms. Most cancer screenng programs fall nto the category of secondary preventon because they am to detect exstng cancers whle the patents are stll asymptomatc and unaware of the cancer. In cancer cases ths s partcularly mportant as early detecton often yelds better prognoses. Lastly, tertary preventon occurs after a dsease has developed. Whle prmary preventon may prevent dsease and secondary preventon may mnmze the mpact of an early developng dsease, tertary preventon ams to reduce the negatve effects of an exstng dsease. Chronc dsease management s an example of tertary preventon, such as preventon of end-organ damage from poorly-managed dabetes or mortalty from worsenng heart dsease. Preventon programs that are most successful have a sgnfcant publc health mpact, a long nterval of dsease progresson, an establshed screenng method to dentfy the target populaton, and an effectve nterventon. However, these preventve efforts are frequently undermned by lack of tmely follow-up. Ths s especally problematc when there s a lack of rapd follow-up n cancer screenng. Reducng the burden of morbdty and mortalty attrbuted to cancer have been a natonal health care prorty n the Unted States for several decades, prmarly through preventon. [8, 9] Great strdes have been made n mprovng ntal screenng rates, but screenng alone cannot prevent cancer. For screenng to be effectve, tmely follow-up of abnormal fndngs s essental. Delays n follow-up and subsequent treatment can decrease survval. Studes demonstrate that many ndvduals wth abnormal screenng tests do not receve ths tmely follow-up. In a study of women wth abnormal screenng mammograms, nadequate follow-up occurred n 18% of these women. [10] Other studes have suggested that non-whte women may experence delays n addressng abnormal mammogram results. [11] In another case seres, only 54% of women recommended to have mammographc survellance after a percutaneous bopsy compled. [12] For cervcal cancer screenng, studes have shown that between 7 and 49% of women wth abnormal Pap smear tests fal to receve approprate follow-up.[13, 14] In a study of women dagnosed wth nvasve cervcal cancer, 11% had faled to follow-up on a prevous abnormal Pap smear. [15] In the Mnnesota Colon Cancer Control Study, 5% of ndvduals wth a postve fecal occult blood test (FOBT) dd not complete follow-up wth a physcan. [16] In the Dansh FOBT tral, 16% wth an abnormal test dd not have a complete colonoscopy performed. [17] 6

7 2.2 Case Study Background Clearly, effectve cancer preventon requres screenng and approprate follow-up. For a dsease specfc needs assessment, we looked at follow-up rates for cervcal cancer screenng. In 2004, an estmated 10,520 new cases of nvasve cervcal cancer (ICC) wll be dagnosed n the Unted States, and 3,900 women wll de from the dsease, accountng for 1. 4 % of cancer deaths among women. [18] The average latency perod from the earlest detectble pre-malgnant stage to nvasve cervcal cancer s between 10 and 20 years. [19] Papancalou cytology (Pap smear) s the most effectve screenng test for pre-malgnant cervcal changes. Abnormal cells detected on Pap smear provde an opportunty for further dagnoss and treatment. Currently, nearly 9 3 % of women n the Unted States report havng at least one Pap smear n ther lfetme.[20] Whle 2 % of abnormal pap smears progress to ICC at 24 months,[19] n a 2004 case seres 4% of women dagnosed wth ICC had a hstory of an abnormal pap smear wthout approprate follow-up n the followng 6 months. [21] Thus, the contnung goal of ICC preventon s tmely follow-up of abnormal results. Eghteen month follow-up of abnormal results range from 22% to 64%. [22] The goal of ths case study s to characterze patterns of follow-up for dfferent Pap smear results over a 4 year perod wthn our own academc health center Methods We analyzed 32,890 Pap smear reports from a cohort of 13,401 women cared for n the Massachusetts General Hosptal (MGH) Internal Medcne Assocates (IMA) prmary care practce between January 1, 2001 and December 31, All avalable pathology reports durng ths perod were retreved from the Clncal Data Repostory (CDR) and lnked to demographc and admnstratve clams data from the Research Patent Data Repostory (RPDR). We extracted and categorzed data from Pap smear reports accordng to the Bethesda Classfcaton of Cervcal Cytology. Abnormal result categores ncluded atypcal squamous cells of undetermned sgnfcance (ASCUS), atypcal squamous cells unable to exclude a hgh grade leson (ASC-H), atypcal glandular cells (AGC), low grade squamous ntraepthelal leson (LSIL), hgh grade squamous ntraepthelal leson (HSIL), squamous cell carcnoma, and adenocarcnoma. We lmted our data analyss to the 29,863 ( 9 1%) reports wth cytology qualty documented as "satsfactory." We defned the follow-up nterval to be the tme between the abnormal Pap smear result and a subsequent gynecology offce vst or procedure, e.g. repeat Pap smear, human papllomavrus DNA assay, colposcopy, curettage, or bopsy. We chose to examne our data wth survval analyss methods whch allow censorng of patents lost to follow-up, thus removng them from the study populaton at the tme contact was lost. Ths methodology dentfes the patents lost to followup and ncludes the tme up untl the loss of contact n our follow-up tmng estmates, provdng a more accurate vew of the actual follow-up trends. For example, f a patent dd not receve follow-up for an abnormal Pap smear yet was seen n another MGH clnc 8 months later for an unrelated problem, those 8 months would be ncluded n our analyss, but the patent would be removed from the analyss after that pont, regardless of the total tme that had passed snce the Pap smear was performed. Patents who ded before follow-up were censored at ther date of death. Patents who dd not receve follow-up were censored at the last date that an admnstratve clam was recorded anywhere wthn the MGH health care system. We used SAS for the survval analyss. To assess the stablty of our data, we created several models by 1) redefnng follow-up to nclude only gynecology procedures, 2) restrctng the cases to nclude only new or progressve dsease, and 3) redefnng the censorng end date to nclude only offce vsts. Snce follow-up rates and general trends dd not sgnfcantly change wth any of these alternate models, we chose the most nclusve model for our fnal analyss. 7

8 G Results Of the 32,890 clncal reports, 99.9% were cross-referenced n the admnstratve clams data, thus valdatng the use of clams data n ths cohort. Of the 29,863 ( %) satsfactory specmens, we were able to classfy 29,846 (99.9%) yeldng 27,753 (92.9%) normal and 2,039 (6.8%) abnormal results (Fgure 2) j 90 r _ j _ - I $' I 00 l 70 I T. Q GO I0 F X C a cases censored. mean, 50% f, Im, 3m, 6m. ly, C lassc alon n (%) n (%) month month % % % % ^ --.IIIXIIII----- ASCUS (627) (13) ASC-H 9 2 (04) (22) LSIL (11 (280) (11) HSIL 102 (50) 5 (5) carcnoma )... (0) AGC_ 77 6 (38) (8) adenocarclnoma 6 0 _ (03) (0) T r _--- - r- - Tlme to Follow-tlp, molhs r E 10 I I Fgure 2. Tme to Follow-up of 2,039 Abnormal Pap Smear Resul Is. * ASCUS: atypcal squamous cells of undetermned sgnfcance; ASC-H: atypcal squamous cells unable to exclude a hgh grade leson; LSIL: low grade squamous ntraepthelal leson; HSIL: hgh grade squamous ntraepthelal leson; AGC: atypcal glandular cells. Censorng accounted for 239 (11.7%) cases wthout follow-up and 2 deaths. Of these 239 cases, 55% dd not have follow-up at 3 months, 3 5 % at 6 months, 19% at one year, and 14% at 18 months. Of the remanng 1,798 (88.2%) cases wth follow-up, ASC-H, carcnoma, and adenocarcnoma receved the tmelest follow-up wth 100% by 3 months. ASCUS results receved the slowest follow-up wth 5 0% at 3.9 months, and 87% at 1 year. AGC, LSIL and HSIL receved ntermedate follow-up wth 50% between 1.8 and 3.5 months, and 92-98% at 1 year, resultng n 122 (16.4%) cases wthout follow-up at 6 months, and 42 (5.6%) cases at 1 year Implcatons We dentfed sub-optmal follow-up of abnormal Pap smear results n a cohort of prmary care patents cared for wthn our academc health system. Our defnton of follow-up was chosen to be the most nclusve of all current practce patterns and ncludes any subsequent evaluaton. However, ths 8

9 prelmnary study does not comment on the approprateness of such follow-up for a gven clncal hstory and Pap smear result. Therefore actual approprate follow-up rates would lkely be lower than our estmates, namely for the hgher rsk lesons of LSIL and HSIL. These emprc data provded justfcaton for desgnng and mplementng our trackng system for result follow-up. 2.3 Identfyng Patents wthout Follow-up The frst task n such a trackng system s to dentfy at-rsk patents wthout follow-up. Ths system should montor these patents along the clncal care pathway (Fgure 3). rsk management -. rsk assessment dagnoss plan order - complete -- delver results - Fgure 3. Clncal Care Pathway Durng a clncal vst, the provder assesses the patent's rsk of havng a partcular dsease and decdes a prelmnary dagnoss. The clncan and patent then decde a management plan. From ths pont, there are many sequental tasks that need to be completed untl these management results return back to the clncan. The process then reterates wth the new nformaton, however wth a tme delay untl ths next vst. To contnue wth the cervcal cancer screenng example, durng an ntal vst (ta) wth a woman, they decde that she s at average, albet low, rsk for developng cervcal cancer and they plan to perform cervcal cancer screenng accordng to natonal gudelnes. The clncan performs the pelvc exam durng the same vst and orders a Pap smear to pathology for evaluaton. The remander of the screenng process then occurs wthout the patent present, asynchronously. At a later date, the pathologst examnes the Pap smear and dctates a report whch s then sent back to the prmary care provder. Ths process usually takes 1-2 weeks. The clncal care pathway begns agan when the clncan receves the report (tl). If t s abnormal, the woman's rsk of developng cervcal cancer ncreases. At ths moment, the clncan processes the abnormal results wthn the context of the patent's clncal hstory and usually has a follow-up plan n mnd for further dagnostc testng accordng to natonal gudelnes, thus s a hgh-yeld opportunty to begn trackng ths follow-up process. Yet ths plan s seldom recorded untl the next patent vst. To complete the follow-up, the patent needs to be contacted, schedule a follow-up vst, decde a follow-up plan wth the clncan and then execute ths management of further testng. Due to the complex nature of asynchronous communcatons and schedulng, ths follow-up cycle (t 1 ) s prone to system errors and delays. We beleve clncans beneft more from assstance at follow-up (t 1 ) than durng the ntal vst at We am to facltate patent trackng along ths care pathway by dentfyng those at rsk for dsease who have not had follow-up. Ths process requres us to dentfy several sets of patents based on documented rsk factors and recorded management actons (Fgure 4). 1. R - patents at rsk 2. Mc - patents wth completed management 9

10 As evdent n the case study, t s plausble to dentfy these sets from exstng data sources. The reason not all patents n Mc came from R s because sometmes management s completed wthout explctly documentng rsk factors. In addtlon to completed management, t s mportant to also record management plans. Theoretcally, f systems could dentfy all rsks and replcate the decson process from dagnoss to plan, all management could be predcted and successfully tracked. However, ths task s complex and not practcal to desgn relably. Recordng the provder plans provdes a more relable predcton of what management results should be performed. Furthermore patents wth documented follow-up plans snouui DC rackcu ullercniuy Lanl paucnts Wl11 no follow-up plan at all. By recordng documented follow-up plans, we can dentfy Mp. Not all patents n Mc are n Mp, because most patents receve management wthout recevng documented plans. Rs NIanagcmnent (lallnc T -. lanagement :omplctcd) Fgure Fgure Patent Patent Populatons Populatons The large sets are rsks (R), management completed (Mc) and management planned (Mp). Patents wth 3. MP - patents wth documented, planned completed follow-up are Fc; wth planned followmanagement up are Fp; and no follow-up plan are Fnp. I I From the sets R, Mp, and Mc, we can dentfy three follow-up sets. Patents at rsk who have had completed follow-up, R n Mc, are F. For these patents, t s mportant to record when the follow-up was completed and not send a meanngless remnder to the clncan. Next, patents at rsk wth no completed management, yet who have a document follow-up plan, (R - Mc) n Mp, are Fp. It s mportant to track these patents to ensure ther management plan s completed. The remanng at-rsk patents, wthout completed or planned management, R - Mc - Mc, are Fnp. It s mportant to notfy ther clncans of the lack of follow-up. 4. Fc - patents wth documented, completed follow-up 5. Fp - patent wth documented, planned follow-up 6. F,p - patents wth no documented plan for follow-up When a management plan s chosen, those patents move from Fnp to Fp. As tme passes, f the planned management s performed, those patents move from Fp to F,. However f the management s not performed, those patents move from Fp back to Fn,. It s these patents who move back nto Fnp that become the subjects for delnquent management nterventon. As patents mss more follow-up attempts, more tme elapses, dseases progress, and ther prognoses worsen. Therefore, these delnquent management patents are the hgh-yeld nterventon patents, because the clncan has already decded on management, yet there has been a breakdown n the management pathway. To dentfy longtudnal errors n ths pathway, t s mportant to mantan a hstory of how patents move between the three follow-up sets. 2.4 Representng Logc Before we can begn to dentfy patents wthout follow-up, we need a formalsm for descrbng the characterstcs of these patent populatons. Boolean logc provdes such a formalsm, usng "and," "or," "not" and "f-then" logc represented symbolcally by ^, v, -, and)-, respectvely. A smple dsease 10

11 process wth only one management plan could be represented n Englsh as "for all patents, f they match specfc set of rsk factors, then they should have a specfc set of follow-up management." R- M The resultng set of patents wthout follow-up s those wth the rsk factors and who have not had management. R^ 'M The rsks can be defned as multple rsk groups joned by "or." Each rsk group contans one or more clncal elements. For example, a rsk group of men > 30 years-old or women > 40 years-old could be represented as: (gender="male" ^ age>"30") v (gender="female" ^ age>"40") Logcal clauses may be nested wthn one another, however most clncal logc s no more than a few layers deep. (gender="male" ^ (dagnoss="dabetes" v dagnoss="heart dsease")) Management choces contan smlarly structured logc. (procedure="sgmodoscopy" v procedure="colonoscopy") The ultmate structure of a tracker conssts of rsks and managements. R(rskgroup, v... rskgroups) - M(managementgroup v... managementgroup) Formalzng follow-up trackng as groups of patent sets defned by Boolean logc both mrrors the clncal decson process and s more easly converted nto computerzed gudelnes and database searchng programs. 2.5 Gudelnes Clncal care gudelnes am to document and dssemnate best-practce patterns to clncans as we learn more about successful health care delvery. Gudelnes are usually consensus statements wrtten by governmental organzatons, medcal socetes, such as the Amercan College of Cardology, and hosptals. These groups gather data from prevous medcal studes n a process known as evdence based medcne (EBM), defned as "the conscentous, explct, and judcous use of current best evdence n makng decsons about the care of ndvdual patents."[23] In the past decade, EBM has helped shape bestpractce standards n delverng clncal care. [24] Dssemnaton of these gudelnes n an envronment of rapdly changng clncal evdence has been a challenge for the medcal communty. As a result, n 1998 the Agency for Healthcare Research and Qualty (AHRQ), a branch of the US Department of Health and Human Servces, n conjuncton wth the Amercan Medcal Assocaton (AMA), created the Natonal Gudelne Clearnghouse (NGC), a webste of all publshed gudelnes. Although the clearnghouse currently contans 1,552 gudelnes, the NGC does not choose whch gudelnes are representatve for a gven dsease. Instead, t dsplays all pertnent gudelnes and allows the clncans to choose whch gudelnes to follow. Implementng these 11

12 gudelnes n automated clncal systems can be problematc because they are often wrtten n free-text whch requres nterpretaton to local dsease trends and are not structured for easy computer nterpretaton. To solve ths problem, a longstandng goal of medcal artfcal ntellgence has been to develop computer nterpretable gudelnes (CIG). Most computatonal models for representng and nterpretng gudelnes, ncludng Arden, Asbru, EON, GEM, GLIF, GUIDE, Prodgy, Protege, and Proforma, use "f-then" logc powered from EMR data. [25] Most models try to represent the full clncal process, all decson steps, supportng patent data, and approprate management optons. The natural extenson of these gudelnes s to assst wth qualty healthcare delvery. One mechansm to encourage complance wth gudelnes s electronc remnders. 2.6 Computerzed Clncal Remnders Preventve computerzed clncal remnder (CCR) systems, workng n conjuncton wth gudelnes, have been shown to be effectve.[5] Remnders are often delvered by emal, alphanumerc pager, prnted document, automated phone call, or durng computer-based chartng or order entry. In prmary preventon, outpatent physcan remnders ncreased use of mmunzaton servces by an odds rato of 3.80 (95% CI ) n a revew of 81 studes.[26] In secondary preventon, evaluaton of a remnder systems mplemented at MGH n 1992 showed long-term changes n complance wth clncal gudelnes over 5 years, ncludng cervcal cancer screenng (64% to 7 5 %) and breast cancer screenng (63% to 70%).[27] In Pttsburgh, a Veteran Affars Hosptal remnder system demonstrated an ncrease n prostate cancer screenng (17% to 86%) between 1998 and 2001 after mplementng ther system.[28] In tertary preventon, health practce organzatons have begun to use clncal remnders to mplement a chronc care model at the prmary care level, demonstratng sgnfcant qualty mprovement. [29] Despte the success of these systems for basc remnders, lttle has been done usng remnders to track the follow-up process due to sgnfcant process barrers. 2.7 Barrers to Gudelnes and Resultng Remnders Although CIG and CCR systems have the potental to mprove the qualty of care, ther actual effectveness s dmnshed by both technologcal and clncan barrers. Wth so many versatle CIG models and formats, the man technologcal barrer to ther adopton s not representng the gudelne programmatcally; rather t s logstc challenges n gatherng supportve evdence to power the gudelne logc. Clncal data s represented dfferently wthn each electronc health record (EHR). When a gudelne s appled to local data, each term must be mapped accordngly. Even f ths challenge could be easly overcome, the clncal data s often not recorded n the detal that the gudelnes requre. Dagnoses and some physcal fndngs may be recorded n the EHR and contaned n a problem lst, however more specfc detals are frequently recorded n free-text and not codfed. Therefore these gudelnes often can not functon wthout requrng the clncan to nput further patent data, whch nterrupts an already busy offce vst. Even f we assume that gudelnes can be mapped to local patent resources and all avalable patent data exsts to power the gudelne, the larger queston remans regardng the usefulness of gudelnes to experenced clncans. Whle clncans prefer to have remnders delvered durng the clncal encounter, they tend not to nteract wth remnders that request further nformaton, as t nterrupts ther clncal workflow. [30] Furthermore, CIG systems often generate remnders that are not specfc to the patent due to lack of full electronc data. For example, clncans may receve remnders that no longer apply to 12

13 a patent, because the management had already been performed. In addton, clncans are often aware of best-practce gudelnes and feel CIG systems generate remnders that do not provde them wth addtonal nformaton, but nstead overwhelm them wth requests that provde mnmal addtonal value to ther clncal care. CIG systems fal to delver ther ntended outcome due to these techncal ssues and lack of clncan buy-n. Prevous efforts to develop these systems have focused on what s possble, rather than on what s needed. Smply because t can be done, does not mean that t should be done. Many gudelne and remnder systems attempt to replcate the clncal decson process leavng clncans wth recommendatons that they usually already know. We beleve the low-hangng frut of remnder systems s not n makng management suggestons; nstead clncans need assstance trackng the follow-up management results. 2.8 Goals for Trackng System Wth these barrers n mnd, we set out to develop a unque, populaton-based trackng system. Successful gudelne and remnder systems need to operate as an adjunct to the clncal process as opposed to a substtute. We set out to develop a tracker system that mrrors the bare essence of the clncal decson process, asssts clncans at the optmal tme for nterventon durng the clncal care pathway, and can track unlmted patents and dsease processes. Our system asssts the busy clncan by trackng ther ntended follow-up, rather than smply suggestng a follow-up plan that the clncan already knows Rsk-Management Stratfcaton Whle most gudelnes attempt to assst provders wth clncal decsons, we beleve that clncans would beneft more from trackng of follow-up once those decsons have been made. Only a small amount of nformaton s requred to begn the trackng process. The process of rsk-management stratfcaton requres the aggregaton and processng of many rsk factnrs. nd then the selecton of one or more of manv rsk factors management optons. However, no matter how complcated the rsk assessment, the clncal nformaton s always dstlled to a sngle rsk-management stratum (Fgure 5). The follow-up for a gven stratum s often 1. I I 1. - I I~ I lrmted to a handtul ot subsequent tests or procedures, r _- _. ~, r~~~~ - _- rr- ~ ho rsk-management stratum t.8. aulluriilal LII:bL -lrly / ccal -lay, LULPULLCU tomography, or surgcal consult; abnormal Pap smear -) repeat Pap smear, colposcopy, curettage, bopsy, or gynecology consultaton; hgh LDL repeat LDL to assess management effectveness. These lmted optons are thus easer to codfy n gudelnes and are usually management process-orented and often avalable n admnstratve clams data. Therefore, the most effcent entry pont s Fgure 5. Rsk-Management Stratum at the rsk-management stratum, for t s the most broad A complex collecton of rsk factors s smplfed and least granular concept n the clncal decson to a sngle rsk-management stratum, e.g. hgh process, and therefore the easest to represent. If we rsk, through the clncal decson process. Ths stratum usually an easly defnable collecton of allow a clncan to jump rght to a rsk-management stratum usually an easly defnable collecton of management follow-up optons. stratum, we can quckly dsplay follow-up choces. By focusng on the rsk-management strata, we avod attempts to replcate the complex rsk assessment process. Instead, we provde some basc rsk factors but let the clncan perform the majorty of the rskmanagement process by presentng few rsk-management strata. From there we do not suggest whch management s approprate. Instead we smply present all management optons related to that stratum 13

14 and leave the management decsons to the clncan. For these reasons, we bult our tracker model around ths clncal concept of rsk-management stratfcaton Tmng of Interventon We beleve the optmal moment for gudelne nterventon s not durng rsk assessment, rather durng results revew. To properly nterpret the results, the clncan s requred to recall the patent hstory and clncal detals and s thus prmed to plan for the follow-up. If follow-up s needed, t s decded at ths pont, often when the patent s not present. Therefore, ths asynchronous process s more prone to errors of lost communcatons and s an opportunty for more successful nterventon. We beleve that provdng gudelnes and remnders at results revew elmnates the low-yeld task of duplcatng the clncal decson process and focuses on the hgh-yeld results of process-orented clncal data trackng. Thus our prmary goal of patent trackng s to asynchronously facltate the follow-up of dsease processes Scope and Scale For the greatest clncal mpact, we am to develop a system that can track any dsease process, rangng n context from annual screenng for dabetes to the specfc follow-up of an abnormal Pap smear. From a publc health perspectve, we am to be able to track allpatents for all dsease processes usng all avalable clncal data. Our system s specfcally desgned on ths massve scale. Most remnder systems were desgned to work synchronously durng a clncal encounter, therefore were often bult to collect and process data on a sngle patent at a tme. Ths data process s often performed durng the vst by accessng multple data sources and runnng logcal rules one by one for specfc dsease processes. Alternatvely, ths ndvdual patent query s performed when a new clncal result s receved electroncally. Ths desgn works well for small a small number of patents (100's 's) and a small number of smple gudelnes. However to track patents not durng a vst would requre searchng all data resources for all patents every day or by settng hundreds of these trggers for any result related to a dsease process. Exstng remnder system models smply can not support the volume of clncal data and processng power requred to track a large volume of patents asynchronously between vsts. We propose a trackng system desgned not for the ndvdual patent, but nstead for the entre patent populaton. Rather than gatherng clncal data as needed, we choose to mantan all avalable clncal data, and process all dsease trackers for all patents usng all clncal data n large batch processes offlne, and smply record the results of these processes. The advantages of ths approach allow us to provde: 1. support for a large scale 2. qualty assurance on centrally located data 3. nstantaneous access to pre-processed results 4. more clncal data from other offlne systems such as admnstratve clams 14

15 3 Desgn Our goal s to dentfy patents who have not had follow-up, notfy ther clncans, and then track the subsequent follow-up. To accomplsh ths task, our trackng system searches patent data usng defned rules for each dsease process. Our tracker gudelnes provde the logc for these searchng rules. These gudelnes automatcally search the clncal and admnstratve data to dentfy and track the patents wthout follow-up. When patents fall off track, the system generates remnders to the clncans, who use the system for both chronc dsease management and follow-up trackng. We descrbe desgnng the gudelne model, dentfyng patents wthout follow-up, generatng remnders, and lastly the technologes we used n the applcaton archtecture. 3.1 Gudelne Model Our gudelne model (Fgure 6) contans multple top-level trackers for each dsease process we want to track ("Cervcal Cancer"). Wthn each tracker are one or more rsk-management strata that represent the culmnaton of the rsk assessment process ("routne screenng"). Each rsk-management stratum contans a sngle rsk set and an accompanyng sngle management set; our goal s to ensure that the patents n the rsk set have had follow-up and therefore are also n the management set. The rsk set and management set contan one or more groups, each composed of one or more clncal elements, e.g. "male > 40 years old," "dabetes and LDL > 100mg/dl", and optonal or-groups wth one or more nested clncal elements. <trackers> <managementset> <trackers> <managementgroupl> <rskmanagementstratuml> <clncalelement/> <rskset> and <rskgroupl> <orgroupn> <clncalelementl/ > <clncalelement./ > and </orgroupn> <clncalelementn/ > </managementgroupl> and or <orgroupl> <managementgroupn> < clncalelement 2 />... or </managementgroupn> <clncalelementn/ > </managementset> </orgroupl> </rskmanagementstratuml> and or <orgroupn> <rskmanagementstratumn> </orgroup.> </rskgroupl> or <rskgroup,> </rskmanagementstratumn> </trackerl> or <tracker,> </rskgroup,,> </rskset> </tracker,,> </trackers> Fgure 6. Tracker Gudelne Model n XML Each tracker contans rsk-management strata, rsk/management groups, or-groups and clncal elements The smallest unts n our model are the clncal elements. These elements descrbe patent clncal data ncludng demographcs, dagnoses, procedures, clncal encounters, clncal provders, medcatons and laboratory tests. A patent's health hstory s a collecton of these clncal elements. Demographc data 15

16 are contnuous (age) and categorcal (race, gender). Dagnoses, procedures, medcatons, clncal encounters and clncal provders are categorcal. Laboratory tests may be ether categorcal or contnuous dependng on the test result type. All clncal elements have a unque full name and an abbrevated name. Constrants for each clncal element may nclude dates ("n the past 1 year") and value lmts and ther logcal operators ("> 100mg/dl"). For example, to dentfy the set of all patents who had a low densty lpoproten (LDL) level greater than 100mg/dl n the last year, the clncal element would look as follows, represented n XML: <clncalelement name="ldl" fullname="lab\chemstry\ldl" operator="greaterthan" lowervalue="100" unts="mg/dl" datelowerlmtnum="1" datelowerlmtunt="year"/> These clncal elements are used as both rsk factors and management choces. The results of today's management choces become the potental rsk factors for tomorrow's rsk assessment. For example, f a person has an LDL > 1 00mg/dl (today's rsk factor) then an LDL should be repeated n the next year (tomorrow's management choce). The results of ths repeated LDL test wll then serve as the rsk factor for ts subsequent follow-up, and so on. 3.2 Clncal Element Dctonary The clncal elements are the language we use to understand the patent health records. To successfully search these records, ths language must contan a standardzed vocabulary of terms, or a lexcon. Numerous medcal lexcons have been created to defne health records. In an effort too combne all avalable medcal lexcons, the Natonal Lbrary of Medcne created the Unfed Medcal Language System (UMLS). As a result, ths dctonary of dctonares, called a metathesaurus, contans nformaton about over 5 mllon bomedcal terms from more than 100 lexcons used for patent records. Importantly, the UMLS contans vocabulares and codng systems desgnated as US standards for the exchange of admnstratve and clncal data. For our clncal element dctonary, we chose a lexcon bult from UMLS but wth addtonal terms defnng local data that are not contaned n the UMLS. Our patent data are contaned n the Research Patent Data Regstry (RPDR).[31] Ths data warehouse contans patent data from multple hosptal systems wthn the Partners Healthcare System n Boston, Massachusetts. The RPDR metathesaurus contans 63,000 terms wth correspondng UMLS codes, ncludng demographcs (age, gender, race), dagnoses (Internal Classfcaton of Dseases, Nnth Revson), laboratory results (Logcal Observaton Identfers Names and Codes (LOINC) and local termnology), medcatons (Natonal Drug Code), procedures (Current Procedural Termnology), and clncal encounters and provders (local termnology). The structure of the RPDR metathesaurus s herarchcal (Fgure 7). Broadly defned terms are grouped nto categores that contan more specfc categores; these categores, n turn, contan even more specfc categores, and so on. At the bottom of ths herarchcal structure are the ndvdual clncal elements. Each of those groups contans even more specfc categores. 16

17 The advantages of usng the RPDR metathesaurus are that 1) t s derved from the UMLS whch allows us to ntegrate our system wth other UMLS-based systems, and 2) ts herarchcal structure allows for smplfed clncal element terms. To gve an example of how usng a herarchcal metathesaurus can help smplfy gudelne termnology, defnng the laboratory test "cholesterol" can be smplfed to a sngle term that represents all related cholesterol lab names. The RPDR metathesaurus maps all local lab names and codes from dfferent systems, n dfferent hosptals, from dfferent ponts n tme, all to UMLS terms. The LOINC code for "cholesterol" contaned n the UMLS s However, there are 23 dfferent local defntons for ths sngle concept wthn the Partners Healthcare System, because many systems are older legacy systems and do not take advantage of standard concept codng. To fx ths problem, the RPDR metathesaurus represents all of these terms n a sngle "Labs\Chemstry\Cholesterol" group. If a '~ r, l:ng.trhr h-at rc a E,nPn'.l0lr hlr-. j lr,, I]rlto c ~ If hc!itl-t'r C,,ttor l a..,r-l t) RhP rnal to Se Jl Medlttorls ~ Arterlalst~ ul( dsease ji F 'tl'dure, j erehrl:luasclar d-sease tj F';'defs j 'C tl.l RheuatJ: hearl dsease I, eae of 3lles j Ll'.tse of pulnlfrlra, lrcjlatlrln J H,'pere'sr.e dspse E 5.r ental hp -terson [stlilg es-er,l 31 'l~[erlre'15lp ' 4lllallte en l:] l~lerte l orl 'p I Irllr, v "flp.pr! lal hvpe!pnl,r l j H'rltPl-lr ' h.'art ard le rl dlcpasr H',perILensl h dt dl,,e'-e J H-c' eltes,e renal lsea, P 'J jel: ondar;. h',pertr l o Ischetur hearlt dlease the frms he of r d 'earl-ljs anl' tlmlphatlf -ef dsease '1 -'rcl-l'nu!ts Ir the er atall peu od Fgure 7. RPDR Metathesaurus Herarchy Each category contans ether subcategores or ndvdual elements wth ncreasng specfcty. In ths example, the crculatory dagnoses category contans the subcategores of rheumatc fever, vascular dsease, cerebrovascular dsease, hypertensve dsease, etc. gudelne requres the lab term "cholesterol," the author may nclude all 23 terms n the gudelne wthn an or-group, or smply nclude the one RPDR term "Labs\Chemstry\Cholesterol," thus smplfyng the data representaton. If the gudelne wanted to refer to any chemstry lab, t would smply be represented as "Lab\Chemstry" rather than all of ts 3,174 ndvdual chemstry terms. 3.3 Patent Data Once we defne our gudelnes wth clncal elements, they gude the search through patent data. Our system uses patent data from multple data warehouses. These clncal repostores contan dagnoses, medcatons, laboratory test results, procedures, vst nformaton and clncal provders. Informaton mssng n one repostory s complmented by data from another. Prmarly we use the RPDR data whch are stored n a Mcrosoft SQL 2000 database runnng on a Wndows 2000 server. Ths data warehouse contans over 450 mllon clncal elements for 1.8 mllon patents seen at ether Massachusetts General Hosptal (MGH) or Brgham & Women's Hosptal (BWH) from as far back as Addtonal nformaton s extracted from the Partners Clncal Data Repostory (CDR). 3.4 Identfyng Patent Sets After we search the patent data usng our gudelnes, we generate summary scores for all gudelnes for all patents. An example queston the tracker ams to answer would be, "Have all women wth a Pap smear result had tmely acknowledgement of the report and follow-up, f abnormal?" The tracker would be "cervcal cancer" and the rsk-management stratum would be "routne screenng." The rsk set would contans all women wth a Pap smear report, and the management set would be ether a repeat Pap smear after some tme nterval or another gynecologc procedure or offce vst. 17

18 MRN tracker maxmum maxmum maxmum maxmum rsk set rsk score management set management score annual (gender="male" 0.5 (procedure= 0.33 check-up ^ age>30) (1/2 match) "chest x-ray" (1/3 completed) A lab="chem7" ^ lab="cbc") annual (gender="female" 1.0 (procedure= 1.0 check-up ^ age>40) (all match) "chest x-ray" (all completed) A lab="chem7" ^ lab="cbc") heart (lab="ldl> 100") 1.0 (lab="ldl" 0.0 dsease (all match) ^ medcne="statn") (none completed) Table 1. Summary Patent Scores for All Rsk-Management Strata For example, for the rsk set of ( (gender="male" ^ age>30) v (gender="female" ^ age>40) ), a 25 year-old man would receve 1 out of 2 ponts (0.5) for matchng half of the rsk group (gender="male" ^ age>30), but would receve a score of 0 for not matchng any of the rsk group (gender="female" ^ age>40). Therefore, hs rsk set score would be hs maxmum score for those rsk groups, 0.5. Alternatvely a 65 year-old woman would receve a rsk set score of 1.0, for matchng both (gender="female") and (age>40). Management scores are also recorded. To dentfy these patents, we calculate scores, between 0 and 1, representng how close the patents match the clncal scenaro (Table 1). For each tracker rsk-management strata, all patents receve both a rsk set score and management set score. These scores are the maxmum rsk group score and maxmum management group score, respectvely. Each group score s the fracton of clncal elements that match for that patent. If two groups te for the maxmum rsk or management set score, both are recorded. After scores for all patents are calculated for all trackers and ther rsk-management strata, they are recorded n a database. 3.5 Remnder groupng Wth these scores, we track patents as they move through the clncal care process and send remnders to clncans when patents fall off track. To avod sendng a hgh volume of remnders to clncans and thus overwhelmng them, we bult ntellgent groupng nto the gudelne model. These remnder groupngs allow us to send results n batches rather than separately. Each management clncal element can have a separate alert type of ether none, routne, or prorty. These alerts are defned by the mnmum tme to wat before reportng grouped results to the clncan and the maxmum tme to wat before reportng mssng results (Table 2). result alertng type none prorty routne mn wat tme before reportng results 0 (delver each mmedately as they arrve) tmn, (delver together but abnormal mmedately) max wat tme before reportng mssng results. tmax-p tmax-r Table 2. Groupng Types and Tmng For example, f a clncan chooses to follow-up a patent by orderng management consstng of (labl ^ lab ^ 2 procedurel ^ procedure2), a clncan may choose routne delvery for the labs (tm r = 3 days, tmaxf = 7 days), andprory delvery for the procedures (tma- p = 1 month). If lab, results arrve to the electronc health record n 1 day, t wll not be sent to the clncan untl watng a total of 3 days (tn r) for lab 2 results to arrve. After 3 days, labl and lab 2 results, f avalable, wll be sent together. If for some reason a lab result s not receved, a delnquent alert wll be sent to the clncan after the tma,,, of 7 days s reached. For the prorty procedures, snce the mnmum wat tme s 0, as soon as the result s receved, t wll be sent to the clncan. The tmax p s set hgher, to 1 month, because procedures usually take longer to schedule, perform, nterpret and report. Alternatvely, 18

19 f a clncan chooses to check results manually and does not want to be alerted by the system, an alert type of none s chosen. To defne these remnder groupngs, the followng attrbutes are added to the management clncal elements: alerttype {"none", "routne", "prorty"}, alertmnnum {number}, alertmnunt {"day", "week", "month", "year"}, alertmaxnum {number}, alertmaxunt {"day", "week", "month", "year"}. The above example s represented n XML: <managementgroup> <clncalelement name="labl" alerttype="routne" alertmnnum="3" alertmnunt="day" alertmaxnum="7" alertmaxunt="day"/> <clncalelement name="lab 2 " alerttype ="routne" alertmnnum="3" alertmnunt="day" alertmaxnum="7" alertmaxunt="day"/> <clncalelement name="procedure" alerttype= "prorty" alertmnnum="o" alertmnunt="day" alertmaxnum "1" alertmaxunt="month"/> <clncalelement name="procedure 2 " alerttype="prorty" alertmnnum="o" alertmnunt="day" alertmaxnum="1" alertmaxunt="month"/> </managementgroup> 3.6 Applcaton Archtecture 9 Ij I z Fgure 8. Three Ter Applcaton Archtecture As nformaton travels through the applcaton nfrastructure, t s processed usng dfferent programmng languages, converted to other data formats, and transmtted to the next ter. Creatng gudelnes, searchng and scorng patent data, and generatng remnders are all controlled by computer programs wthn our applcaton archtecture. We use a number of technologes and computng languages across three ters: database, busness logc, and presentaton (Fgure 8). 19

20 The database ter conssts of both the data storage and the technologes to access these data. We use the structured query language (SQL) to retreve data from the Mcrosoft SQL Server relatonal databases. For data sources that do not allow drect SQL queres, such as the CDR, we use other servces to collect those data and store a copy n a local database that we can then query wth SQL. The busness logc ter contans the functonal programs that communcate between the data and the user nterface. Our applcatons run on Internet servers usng Mcrosoft Actve Server Pages (ASP) wrtten n JavaScrpt. These applcatons are dvded nto data servces, programs that query and gather nformaton from the database ter, and presentaton servces, programs that format these data for the user nterface. Data s passed between the database and presentaton ters through the busness logc ter n the extensble Markup Language (XML) standard. These XML data are transformed to the format requred by the destnaton ter usng extensble Stylesheet Language Transformatons (XSLT). The presentaton ter s the dsplay that allows the user to nteract wth the data. One presentaton technology we use s a web browser, dsplayng data wth the Dynamc Hypertext Markup Language (DHTML). Addtonal presentaton logc s programmed wthn the browser usng JavaScrpt, provdng a drect connecton to the busness logc. Ths technology enables us to update the webpage dynamcally wthout the need to reload the page. Another presentaton technology we use s Macromeda Flash, a multmeda authorng envronment that s presented n the Flash Player, a vrtual machne applcaton that runs wthn a web browser. Flash applcatons are wrtten n ActveScrpt 2.0, an object-orented language. 20

21 4 Implementaton Our trackng system addresses each challenge of the process of follow-up care (Fgure 9). The trackng process begns wth medcal experts defnng the gudelnes. These gudelnes are then used to automatcally search patent medcal records and dentfy patents wthout follow-up. Messages are sent to clncans who can then vew these patents and choose the approprate follow-up management. If the management occurs on tme, those results become part of the patent's medcal record, and the next tme the patent record s searched, that patent wll now have documented follow-up. However, f the management has not occurred, automated remnders are sent to the clncan. Ths process of searchng for patents wthout follow-up, trackng subsequent follow-up and generatng remnders occurs automatcally every day as new results are receved and new managements are chosen. medcal experts defne gudelnes usng the Tracker Authorng To search all patent clncal clcmcnts usng the Tracker Query Tool I - ;,.,,, - 1"'.." dentfy patent sets and calculate c,, , tlj,,' ~L ~ a,. 11 I, ~ f...~ k.w... patents complcte follow-up tracker scrvc Ces send remnder mcessagcs to clncans :... I , ;,R1.... ks =Ira~~~~~ tralcker gudlcltes l [1. _ clncans clloose ollow-up IT1llmanagement Usng tnl Tracker Clncal Tool Fgure 9. Process of Follow-up Care n the Tracker System The tracker system encompasses all aspects of the follow-up care process. Medcal experts create tracker gudelnes usng the Tracker Authorng Tool. The Tracker Query Tool automatcally searches all patent medcal records daly, dentfes patents wthout follow-up and records ther status for all trackers n the watchlst. Other servces access the watchlst and generate messages to clncans, such as a new result requres follow-up, or a patent requres annual cancer screenng. Thrd party applcatons present these messages to clncans. Clncans then choose a management opton usng the Tracker Clncal Tool. Once management s planned, the entre process begns agan, and all patents are tracked untl all ssues requrng follow-up have been resolved. 21

22 Most of our program nterfaces are desgned for a rch, web-based envronment. Utlzng the Internet allows clncans to access our tools usng a standard Internet browser and removes the need to dstrbute and mantan clent-sde software. However wth the advantages of a web-based envronment come the lmtatons of a less nteractve, sluggsh experence usng a web-browser. Therefore, to create a smooth, rch experence we employ several web-based technques ncludng clent-sde H'ITP, herarchcal navgaton and drag-n-drop. These technologes make the user nteracton more comparable to standard wndow-based applcatons. Clent-sde HTTP technology utlzes JavaScrpt objects to drectly connect to server-sde data and subsequently modfy the browser nterface wth Dynamc HTML. Therefore, the user does not have to submt the webpage to update the data; the result s a smooth nteractve experence wth mnmal observed delay and preservaton of the nterface and data state. Ths clent-sde technology s especally useful for loadng large amounts of data n small batches as needed, provdng a quck ntal load and allowng for herarchcal navgaton. Our nterface mrrors the underlyng data structure. Tracker gudelnes and the RPDR metathesaurus are both structured herarchcally. Ths organzaton s ntutve to users and mmcs how data s organzed n dsease-related groups wth ncreasng clncal detal n subgroups. At the top level, the user sees only the most basc groups, but as needed, may expand each group to see nested subgroups and other dseaserelated tems. For example, although the RPDR metathesaurus contans 63,000 terms, ntally only the top 7 categores are dsplayed: Demographcs, Dagnoses, Encounters, Labs, Medcatons, Procedures, and Provders. Expandng the Dagnoses group reveals the man categores of dagnoses. Ths herarchcal structure prevents the user from nformaton overload and results n a clean graphcal user nterface. cal experts fne the r gudelnes -ff 4.1 Tracker Authorng Tool To begn the trackng process, clncal experts defne the trackng gudelnes. The Tracker Authorng Tool s a web-based applcaton that enables clncal experts to buld tracker gudelnes from clncal elements (Fgure 10). The user nterface dsplays the tracker navgator on the far left (a), the rsk-management composer n the man porton of the screen (b), and system messages and tps n the bottom left (c). The tracker navgator herarchy (a) s retreved usng the Tracker Defnton Servce (TDS), one of many data servces that queres the Tracker gudelne database n SQL, then transforms the resultng tracker XMI, gudelnes dynamcally nto HTML for dsplay. The tracker navgator mrrors the structure of the underlyng gudelne model, such that authors can vew trackers, rsk-management strata, and clncal elements herarchcally. Usng the tracker navgator, the author can create, vew and edt trackers and rsk-management strata. 22

23 b amn.lr-rrnnlllllomaf I E jd E dt w IJ * new tracker Breast Cancer * Cervcal Cancer:e * Colorectal Canc _ ;,1.,,,,.. * Dabetes ler na o Hypercholestert lter froyp * new rsktnn O low rsk O r* group 0 r1:lator group, l 1f '~',,, 1 X~ d ''', 'Iqa- r, * rut.' group * rlrd'~tol ',,-y mng group I medum rsk ~ ~ ~ ~ $ D ;1l ' -I ': ' ~ 'I, Off! as'-~ ' ~., -,. '~ -,'!' ),', * Hvyertenson Iejr ohath y '. d u ' gron e.. j ;-1 f E, 1191' _ '4 j,,s,.6,-1, 4] F. 1a,,t li _ I j ' 1!F S, s,,,! %V~ -- -j. - n rrana =- -[S =- Fgure 10. Tracker Authorng Tool The tracker navgator (a) dsplays the current tracker herarchy wth embedded acton lnks to author new trackers. The rsk-management composer (b) graphcally represents the rsk-management stratum whch ncludes the rsk set (d) and the management set (e), each wth several groups (f, g) consstng of clncal elements (h). The composer center space contans the RPDR metathesaurus () and searchng tool (j). Confrmaton messages and tps are shown n the system message wndow (c)..-._. I. Once the author selects a rsk-management stratum, the remander of the authorng occurs n the rskmanagement composer (b). Snce the rsk set (d) and management set (e) n the tracker model have the same data structure, graphcally they appear smlarly, sde by sde. The author selects ether the rsk or management set by clckng the approprate tab at the top of the composer; the correspondng set s dsplayed n the center of the screen (d), and the other set s dsplayed to the rght (e). The author can vew both sets of groups smultaneously, for they are ntmately related n terms of clncal logc,.e. R M. The author creates a new rsk group by clckng "new" n the composer tool bar (j). The author then fnds the relevant clncal elements ether by manually navgatng the metathesaurus herarchy () or by searchng for terms explctly usng the search tool (j). The RPDR metathesaurus dsplay data are managed by RPDR Metathesaurus Servce. To add the clncal element to the group, the author drags the element from the metathesaurus and drops t n the approprate group. The author can select ndvdual tems ( con) or an entre RPDR category (1 con) as a clncal element. The author then defnes the attrbutes of each clncal element. If the author s edtng a rsk group, rskorented attrbutes are dsplayed (Fgure 11). All RPDR data types have an ncluson date nterval, whch narrows the dates that the gudelne searches for patents wth a clncal element. Laboratory rsk factors have addtonal value attrbutes ("> 100") and normal/abnormal ranges. A mouse-over these areas n ths wndow explans the attrbute. In addton, the author may ndcate that ths clncal element should be the lack of evdence, by clckng the NOT logcally box. 23